U.S. patent application number 12/437385 was filed with the patent office on 2009-11-12 for medical failure pattern search engine.
This patent application is currently assigned to Lawrence A. Lynn. Invention is credited to Eric N. Lynn, Lawrence A. Lynn.
Application Number | 20090281838 12/437385 |
Document ID | / |
Family ID | 40852498 |
Filed Date | 2009-11-12 |
United States Patent
Application |
20090281838 |
Kind Code |
A1 |
Lynn; Lawrence A. ; et
al. |
November 12, 2009 |
MEDICAL FAILURE PATTERN SEARCH ENGINE
Abstract
A patient safety search engine and alarm processor is programmed
to repetitively search the electronic medical records of all
patients in a hospital system to automatically provide early
detection of patients with evolving pathophysiologic cascades, and
in particular the cascades of evolving death, such as cascades of
septic shock. The search engine also searches for a wide range of
evolving pathophysiologic failures which are commonly fatal if
detected too late. An alarm processor is provided which is
programmed to provide an alarm upon the detection of a cascade or
failure. The processor is further be programmed to provide an image
of the cascade, and to determine, the severity of the cascade, and
the time of onset of the cascade in relation to the timing and type
of procedures and treatment and the increased cost associated with
the cascade. Discretionary real-time system-wide searches for a
wide range of clinical failure patterns or images within the
hospital system may be performed using the disclosed patient safety
search engine.
Inventors: |
Lynn; Lawrence A.;
(Columbus, OH) ; Lynn; Eric N.; (Villa Ridge,
MO) |
Correspondence
Address: |
NELLCOR PURITAN BENNETT LLC;ATTN: IP LEGAL
6135 Gunbarrel Avenue
Boulder
CO
80301
US
|
Assignee: |
Lynn; Lawrence A.
Columbus
OH
|
Family ID: |
40852498 |
Appl. No.: |
12/437385 |
Filed: |
May 7, 2009 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61126906 |
May 7, 2008 |
|
|
|
61200162 |
Nov 25, 2008 |
|
|
|
Current U.S.
Class: |
705/3 |
Current CPC
Class: |
G16H 50/70 20180101;
G16H 50/20 20180101; G16H 15/00 20180101; G16H 50/80 20180101; G16H
10/60 20180101 |
Class at
Publication: |
705/3 |
International
Class: |
G06Q 50/00 20060101
G06Q050/00 |
Claims
1. A hospital monitoring system comprising: a database comprising
an electronic medical record repository of data wherein the
electronic medical record repository of data contains the data of a
plurality of patients in at least one hospital; and a processor
programmed with instructions for: searching the database for at
least one complex cascade pattern within the data of the plurality
of patients and to identify one or more patients generating the at
least one complex cascade pattern.
2. The hospital monitoring system of claim 1, comprising an alarm
processor programmed with instructions to provide an automatic
alarm upon the detection of the complex cascade pattern.
3. The hospital monitoring system of claim 1, wherein the complex
cascade pattern comprises a cascade of at least one of sepsis,
severe sepsis, septic shock, microcirculatory failure, and
shock.
4. The hospital monitoring system of claim 1, wherein the complex
cascade pattern comprises at least a plurality of linked
perturbations and/or trends of physiologic and laboratory data
creating a progressively enlarging aggregation of progressively
greater numbers of perturbed physiologic and laboratory data.
5. The hospital monitoring system of claim 1, further programmed to
determine at least one characteristic of the complex cascade
pattern, the characteristic comprising at least one of a severity
of the complex cascade pattern, a duration of the complex cascade
pattern, a time of onset of the complex cascade pattern, a maturity
of the complex cascade pattern, a timing relationship of the
complex cascade pattern to other events or another complex cascade
pattern, a cost associated with the complex cascade pattern, a
global pattern of the complex cascade pattern, a time of
termination of the complex cascade pattern, components of the
complex cascade pattern, a state of evolution of the complex
cascade pattern, a length of stay subsequent to, or in association
with the complex cascade pattern, or treatments associated with the
complex cascade pattern.
6. The hospital monitoring system of claim 5, wherein the at least
one characteristic of the complex cascade pattern is defined by at
least one of a number of perturbations and/or trends which comprise
the complex cascade pattern, a severity of the perturbations and/or
trends, a number of systems affected by the complex cascade
pattern, a presence, number and/or severity of failure of
compensation in response to perturbations associated with the
complex cascade pattern.
7. The hospital monitoring system of claim 1, wherein the processor
comprises instructions for determining a rate of growth of the
complex cascade pattern.
8. The hospital monitoring system of claim 1, wherein the processor
comprises instructions for determining the rate of growth of the
complex cascade pattern by at least one of the increase in number
and/or severity of new perturbations being added per unit time, the
increase number of systems affected, and the increase number of
perturbations present in different systems.
9. The hospital monitoring system of claim 1, wherein the processor
comprises instructions for detecting events or components that are
temporally and/or spatially associated with the complex cascade
pattern but that are not part of the complex cascade pattern.
10. The hospital monitoring system of claim 1, wherein the
processor comprises instructions for converting the electronic
medical records into a format favorable for searching for the
complex cascade pattern.
11. The hospital monitoring system of claim 10, wherein the format
comprises sequential and timed variations comprised of at least
positive variations and negative variations of the data.
12. The hospital monitoring system of claim 10, wherein the
electronic medical record repository of data comprises data from a
plurality hospitals, and wherein the processor is programmed
comprises instructions for identifying the patients that are
generating complex cascade patterns and the hospital in which they
are located.
13. A patient data processing system comprising a processor
programmed to: convert the electronic medical records of at least
one hospital into sequential and timed trends comprised of at least
positive trends and negative trends of both the physiologic
parameters and the laboratory data, detect relational trends
comprised of a combination of positive and/or negative trends,
detect complex cascade patterns comprised of a plurality of
combinations of relational trends, automatically output a display
of the image of the detected complex cascade, automatically output
a warning indicating the detection of the complex cascade and the
identification of the patient generating the complex cascade, track
the growth or decline of the complex cascade and output an
indication indicative of growth or decline.
14. The patient data processing system of claim 13, wherein the
complex cascade pattern is indicative of physiologic failure.
15. The patient data processing system of claim 13, wherein the
physiologic failure is at least one of sepsis, severe sepsis,
septic shock, and microcirculatory failure, a shock cascade, and a
septic shock cascade.
16. The patient data processing system of claim 13, wherein the
processor comprises instructions for to determining and outputting
an indication of the type of the cascade detected.
17. The patient data processing system of claim 13, wherein the
processor comprises instructions for to determining and outputting
at least an indication of the timing and type of the trends along
the cascade.
18. The patient data processing system of claim 13, wherein the
processor comprises instructions for to determining and outputting
at least an indication of the length of the cascade.
19. The patient data processing system of claim 13, wherein the
processor comprises instructions for to detecting the onset of
therapy determining and outputting at least an indication of timing
of therapy in relation to the cascade.
20. A patient data processing system for processing electronic
medical records of a hospital, comprising a processor programmed
to: generate a time-series of data of at least a portion of a
plurality of patients in the hospital, including at least data
relating to the physiologic state and/or care of each patient;
convert the datasets, including at least the monitored datasets and
laboratory datasets into parallel and overlapping time series;
identify occurrences comprising inflammatory occurrences, metabolic
occurrences, volumetric occurrences, hemodynamic occurrences,
therapy occurrences, hematologic occurrences, or respiratory
occurrences; identify the timing of the occurrences; identify at
least one relational pattern of occurrences along a plurality of
time series that is indicative of failure cascade of at least one
of a sepsis cascade, a pulmonary embolism cascade, a metabolic
cascade, or a microcirculatory failure cascade; and output an alarm
when the failure cascade is detected.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional
Application No. 61/126,906, filed May 8, 2008 and to U.S.
Provisional Application No. 61/200,162, filed Nov. 25, 2008, the
disclosures of which are hereby incorporated by reference in their
entirety for all purposes. This application is related to co-filed
US patent application titled "Patient Safety Processor" the
disclosure of which is hereby incorporated by reference in its
entirety for all purposes.
BACKGROUND
[0002] The present disclosure relates systems and methods for
detecting and monitoring patient conditions in clinical medicine
settings.
[0003] Patients die unexpectedly on hospital wards under the
careful watch of even knowledgeable and diligent healthcare workers
at alarming rates. It has been argued that hospitals have a culture
of failure tolerance. However, a more critical analysis reveals
that this "tolerance" is actually resignation and that the high
number of clinical failures comprises the unavoidable result of the
ill-conceived attempt to manage the profound complexity of
overlapping human pathophysiology without adequate technology.
Unfortunately hundreds of common but subtle perturbations which
combine to produce complex pathophysiologic failure cascades which
progress to death can potentially occur with every patient in the
hospital.
[0004] While the physiologic complexity of just one patient is
often overwhelming, a single nurse may have twelve complex patients
and a single hospitalist physician may have 30. In the present
state of hospitals, most of the physiologic complexity resides in
the electronic medical records (EMR) even as the patient progresses
toward death. Unless an expert physician or nurse puts all the
pieces together timely to see the evolving failure, the patient is
often doomed even though healthcare workers are nearby.
[0005] Patient care in a hospital setting involves a complex
management process because human pathophysiology is highly complex
and healthcare workers address multiple patient issues
simultaneously. Decisions about patient priority and care made by
the healthcare workers are subjective to some degree and may vary
depending on the level of expertise and experience of each person
involved in patient care.
[0006] Because of the complexity involved in patient care,
particularly in a hospital setting, healthcare workers have
attempted to provide a level of uniformity to the process through
protocol-based care. Such care may involve "if X-threshold-breach
then Y-action" branching decision tree protocols. However, such
protocols when considered in relation to the true level of
pathophysiologic complexity often comprise a profound over
simplification so that the healthcare worker can easily proceed
down the wrong branch of a decision tree.
[0007] In addition to protocol-based care, healthcare workers often
monitor various physiological parameters of a patient in order to
obtain more information upon which they may base clinical care
decisions. Many of these parameters may include blood oxygen
levels, pulse rate, routine blood tests and vital sign tests, which
may be recorded in a centralized electronic medical record.
However, this testing may not be effective in the early detection
of certain clinical conditions or in providing the healthcare
worker with a clear picture of the patient's condition and care.
Even subtle and minor levels of perturbation may lead to profound
instability in certain clinical situations. For example, minor
changes in the serum sodium in the setting of a stroke may lead to
confusion and then obtundation, which may increase the risk of
aspiration, pneumonia, and venous thrombosis. Indeed, the level of
serum sodium decrement to produce such abnormalities may be as
little as 8 mEq, a decrement which would otherwise not be likely to
produce an adverse reaction in the absence of an acute stroke.
Since an 8 mEq decline in serum sodium would normally be tolerated
in the absence of a stroke, it may be easily overlooked as a cause
of profound instability by a healthcare worker who may not be
knowledgeable or diligent enough to recognize the entire relational
complexity. It is very common that subtle or simple events or
occurrences actually comprise linked components of a much larger,
dangerous, but undetected expanding pathophysiologic failure
process. Since simple pertubations are readily overlooked by the
physician (or if they are identified, the pivotal linkage to other
processes is commonly unrecognized), this allows the
pathophysiologic failure process to progress, untreated toward
death.
[0008] In another example of the challenges involved in the timely
detection of evolving complex patient conditions, septic shock is
often the end result of progression from the uncomplicated state of
infection to progressive states of the inflammatory response
syndrome, sepsis, severe sepsis, and finally septic shock. These
distinctions of states are arbitrary and poorly defined at the
bedside. The vast majority of patients have infection with fever
without further progression and many even progresses to the
inflammatory response syndrome without further progression to
septic shock. Because routine blood testing and even continuous
vital measurements may not always detect the pre-shock state,
specialized blood tests and biomarker profiles specifically
developed to detect the pre-septic shock state have been developed.
However, specific blood test and profiles suffer from a lack of
specificity, in part because the variable response of patients to
physiologic perturbation. Whether or not a given patient progresses
to shock depends on much more than the biomarkers present and their
concentrations. Progression to shock may depend on a complex
relationship of patient-specific physiologic responses to
immunologic and inflammatory perturbation as well as the
physiologic state of the patient at the onset and during the
perturbation and the timeliness and adequacy of intervention (e.g.
antibiotics and/or fluid). Since most of these factors are not
captured by blood test measurements or biomarker profiles, even
serial testing directed specifically toward the detection of the
pre-shock state may not provide sufficient information to provide
for reliable timely detection of the evolving state of severe
sepsis or septic shock.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] Advantages of the present disclosure may become apparent
upon reading the following detailed description and upon reference
to the drawings in which:
[0010] FIG. 1 is an exemplary component diagram of a patient
demonstrating the overlapping patient complexities that may be used
to construct relational binaries, image components and MPPC for
searching and detection;
[0011] FIG. 2 is a diagram depicting the levels of analysis in
accordance with an exemplary embodiment;
[0012] FIG. 3A is a data flow diagram in accordance with an
exemplary embodiment;
[0013] FIG. 3B is a diagram of an exemplary system in accordance
with an exemplary embodiment;
[0014] FIG. 3C is a data and action flow diagram in accordance with
an exemplary embodiment;
[0015] FIG. 4 is an exemplary UML Static Diagram of the primary
classes within one embodiment of a relational binary processor;
[0016] FIG. 5 is an exemplary UML Static Diagram of a subset of the
relational binary processor specifically expanding the definition
of the event type;
[0017] FIG. 6 is an exemplary UML Static Diagram of the primary
classes within the patient safety processor;
[0018] FIG. 7 is an exemplary UML Static Diagram of the primary
classes within the binary definition set;
[0019] FIG. 8 is an exemplary UML Static Diagram of the primary
classes within the Failure image component Definition Set;
[0020] FIG. 9 is an exemplary user interface model of the
convergence editor that depicts a sleep apnea binary diagram;
[0021] FIG. 10 is an exemplary user interface model of the
aggregate failure image component editor that depicts a failure
image component diagram associated with narcotic-induced
ventilation instability;
[0022] FIG. 11 is an exemplary user interface model of the
convergence editor that depicts a heparin therapy binary
diagram;
[0023] FIG. 12 is an exemplary user interface model of the
convergence editor that depicts an insulin therapy binary
diagram;
[0024] FIG. 13 is an exemplary user interface model of the
convergence editor that depicts a narcotic therapy binary
diagram;
[0025] FIG. 14 is an exemplary user interface model of the
aggregate failure image component editor that depicts a failure
image component diagram associated with heparin-induced
hemorrhage;
[0026] FIG. 15A is a failure image frame that includes a plurality
of timelines organized into groupings showing an expanding cascade
of evolving death due to septic shock.
[0027] FIG. 15B is a failure image frame that includes a plurality
of timelines organized into groupings showing a failure image of an
expanding cascade of septic shock with portions of the image being
separated into sequential states.
[0028] FIG. 15C is a failure image frame that includes a plurality
of timelines organized into groupings showing early timepoints in
an expanding cascade of severe septic shock;
[0029] FIG. 15D is a failure image frame which shows an image of a
failure cascade severe septic shock with inflammatory, hemodynamic,
and respiratory augmentation, and with early immune failure;
[0030] FIG. 15E is a failure image frame which shows an image of a
failure cascade of severe septic shock with inflammatory,
hemodynamic, and respiratory augmentation, with immune failure, and
with evidence of decline in respiratory gas exchange and fall in
platelet count;
[0031] FIG. 15F is an exemplary failure image frame showing an
image of an advanced cascade of severe septic shock with
progression to metabolic failure, renal failure, hemodynamic
failure and respiratory failure;
[0032] FIG. 16 is an exemplary congestive heart failure (CHF)
failure image that includes a plurality of timelines organized into
groupings;
[0033] FIG. 17 is an exemplary sleep apnea failure image that
includes a plurality of timelines organized into groupings;
[0034] FIG. 18 is an exemplary thrombocytopenic purpura failure
image that includes a plurality of timelines organized into
groupings;
[0035] FIG. 19 shows an overview image of perturbation onset and
progression from the time lapsed MPPC of FIG. 15A, wherein the
perturbations in each grouping are incorporated into an aggregate
index along a single smoothed time series for each group;
[0036] FIG. 20 is a general failure image that includes a plurality
of timelines from the complexity diagram of FIG. 1 showing a
failure image of excessive secretion of serum inappropriate
antidiuretic hormone (SIADH) induced fall in hyponatremia;
[0037] FIG. 21 is a split screen diagram of a drag and drop
interface for constructing combined physiologic and treatment
images for the patient safety processor showing the construction of
an MPPC indicative of narcotic-associated recovery failure in the
presence of sleep apnea;
[0038] FIG. 22 is an exemplary image frame of a failure image
editor for constructing a septic shock MPPC for recognition by the
Patient Safety Processor; and
[0039] FIG. 23 is a diagram of an exemplary patient safety
processor network.
DETAILED DESCRIPTION
[0040] One or more specific embodiments of the present disclosure
will be described below. In an effort to provide a concise
description of these embodiments, not all features of an actual
implementation are described in the specification. It should be
appreciated that in the development of any such actual
implementation, as in any engineering or design project, numerous
implementation-specific decisions must be made to achieve the
developers' specific goals, such as compliance with system-related
and business-related constraints, which may vary from one
implementation to another. Moreover, it should be appreciated that
such a development effort might be complex and time consuming, but
would nevertheless be a routine undertaking of design, fabrication,
and manufacture for those of ordinary skill having the benefit of
this disclosure.
[0041] The present disclosure provides systems and methods for
diagnosis, monitoring, and treatment of certain clinical
conditions.
[0042] One embodiment comprises a processor system including an
electronic medical records database of a hospital or hospital
system containing at least laboratory and physiologic data of at
least one patient, a search engine programmed to automatically and
repetitively search data within or derived from the database to
detect complex patterns or images of evolving pathophysiologic
cascades, and to further define the cascade, quantify the cascade,
and to determine the relationships and cost of the cascade.
According to one aspect of the invention a pathophysiologic cascade
as detected by one embodiment comprises an expanding
pathophysiologic process. Such expansion commonly occurs within the
initially affected system as for example in the immune system (as
an inflammation cascade) and then expands into other systems such
as the respiratory and cardiovascular system often through
chemical, neurological, and/or anatomical mechanisms of
augmentation, up regulation, down regulation, compensation,
compensation failure, and combined systems failure. The most
important cascades detected by an embodiment of the present
invention are cascades of evolving death (CED).
[0043] One embodiment comprises a search engine which
automatically, intermittently and/or continuously searches for and
detects pathophysiologic cascades and particularly cascades of
evolving death (CED), and an alarm processor programmed to identify
the patient which is generating the CED and to provide an alarm
upon the detection of such a cascade at a site adjacent the
location of the patient, to a care giver managing the patient, to a
ward in which the patient resides, to a quality control center or
patient safety management center, to the patient him or herself as
by a pager or phone which may be configured to display an image of
the cascade, the type of the cascade, and/or at least one
characteristic of the cascade. The pager may generate a series of
lights which are indicative of the severity of the failure and/or
cascade detected. The wearing of the pager by the patient prevents
the healthcare worker from discounting or ignoring the findings
since the patient him or herself (or the patients family if the
patient is not competent) is also notified by the processor.
[0044] According to one aspect of the present invention, cascades
of evolving death (CED) are detected by the search engine as
expanding aggregations of perturbations and variations of signals
and/or tests derived from a biologic organism which spreads across
signal and tests derived from different systems within the organism
and commonly ends in death. Commonly, as the CED evolves, the
number of perturbations, the number of different types of positive
or negative trends or variations, and/or the number of different
types of threshold breaches, and the number of perturbed systems,
progressively rise.
[0045] One pathophysiologic process which commonly generates a
widely expanded CED is severe sepsis. Severe sepsis commonly
induces microcirculatory failure which eventually expands the CED
across all systems dependent on microcirculation. In the lungs,
evolving microcirculatory failure causes a progressive decline in
the efficiency of gas exchange, minute ventilation rises to
compensate or as a direct result of the factors (such as toxins)
which are associated with the cause of the process. With many
systems the response is biphasic, with an initial augmentation
perturbation (with comprises a favorable or preparatory response of
the system. As the cascade progresses, subsequent to augmentation,
failure related perturbations develop expansively. In the sepsis
CED example, metabolic system perturbation may initially be an
augmentation perturbation comprising a simple preparatory fall in
hydrogen ion, but later as the cascade evolves widespread failure
related perturbations dominate.
[0046] Cascade of evolving death commonly contain smaller
relational patterns which may progress virtually throughout the
cascade and by themselves may portend death. For example the CED of
severe sepsis contains the pattern of pathophysiologic divergence
of ventilation and arterial oxygen saturation which is described in
U.S. patent Ser. No. 10/150842 (the disclosure of which is
incorporated by reference as if completely disclosed herein).
Despite the fact that such smaller relational patterns may progress
virtually throughout the CED (and by themselves may portend death)
these small patterns generally represent only a very small portion
of the signal "bandwidth" of the death cascade, especially as the
cascade matures and becomes widely expanded. The smaller relational
patterns therefore are useful for detection of the likely presence
of a CED but do not provide the specificity to determine the cause
of the CED and may not be, especially early in their manifestation,
specific for the presence of a CED.
[0047] Typical CED have at least one initiating apex or vertex
which comprises the onset of the cascade. The apex or vertex is
generally within a single physiologic system. The CED expands out
from the apex or vertex across the initially affected system and/or
into and across other systems. It is common for CED to expand
within the initial system first. Like a progressively enlarging
cone of perturbation projecting initially within and then beyond
the initial system, the CED may expand to involve virtually all
systems by the time the point of death is reached.
[0048] Cascades of evolving death, in 3D space can be represented
by a cone with apex comprising the onset of the cascade, the length
of the cone being defined by cascade duration, the angle of the
come being defined by cascade expansion rate, and the
cross-sectional area at a given point being defined by the
magnitude of the cascade expansion at that point in time.
[0049] When, according to one embodiment of the present invention,
the data sets are organized into a 2D format (such as a time series
matrix which is compartmentalized such that each system defines a
separate compartment of time series) the CED commonly produces a
triangle with the angle at the vertex defining, in part, the speed
of expansion of the CED. The CED, without intervention, will
commonly end in death. In the 2D representation a death vertex is
identified on the X axis and the triangle can be competed by
passing a line vertically through all perturtubations or variations
which remain at the time of death. This forms the base of the
triangle of the CED.
[0050] If the patient achieves spontaneous or assisted recovery the
cascade begins to contract. The point at which contraction begins
forms the vertex and base of the cascade expansion triangle of the
CED and the vertex and base of contraction triangle of the CED
which eventually contracts to a stable state vertex point at the
end of the contraction triangle. Even after a patient has
recovered, the state of the physiologic components of the time
series matrix of the stable state after the CED may be different
from that which preceded the CED and this difference and its
location in relation to systems and time series types is often a
quantifiable indication of the extent and type of residual injury
sustained by the patient due to the event which induced the CED,
the CED itself, and/or due to treatment.
[0051] While the term cone and triangle is used herein for
graphical representation of the expanding cascade, a preformatted
matrix will be affected in a wide range of expanding patterns.
Expansion may not be uniform or linear but rather the shape of the
expanding cascade varies depending on a wide range of factors as
will be discussed.
[0052] The processor may further be programmed to determine at
least one of the type of cascade, the severity of the cascade, the
duration of the cascade, the time of onset of the cascade, the
maturity (as for example the stage) of the cascade. The severity
may be determined by the number of perturbations or trends which
comprise the cascade, the severity of the pertubations or trends
(as for example by the slope and/or magnitude of the perturbations
or trends which comprise the cascade in relation to the baseline
values and/or statistical normal values by another severity
measure), the number of systems affected by the cascade, the
presence, number and /or severity of failure of compensation in
response to perturbations associated with the cascade, the growth
of the cascade, as for example by the number of new perturbations
being added per unit time or the number of systems affected. The
processor may also detect the events or components associated with
or which are a part of the cascade. The alarm processor may be
programmed to provide an indication of each of the forgoing. Alarm
display may be provided for presenting any of the forgoing in
textual, auditory, graphical, or other formats. The search may be
reinitiated each time new data is added, each time a particular
type of data is added, or at a preselected or adaptive frequency.
For example the searching frequency may increase when early
components of a possible cascade have been identified or when an
actual cascade has been identified.
[0053] One embodiment retains the images and relationships detected
during the previous search(s) so that the subsequent new search
cycle of the data set is less processor intense involving, for
example, only the comparison of the new data (with or without prior
formatting) to the previous processed data sets to determine if a
new cascade is developing, an existing cascade is becoming more
severe or improving, or if another event in relation to a cascade
(such as a treatment event) has occurred.
[0054] One embodiment comprises a patient data processing system
for converting the global electronic medial record (EMR) of a
patient or patients into a real-time patient monitor comprising a
pathophysiologic cascade search engine configured to repetitively
and/or continuously search the EMR for evolving complex
pathophysiologic cascades and an alarm processor for outputting a
warning upon detection of a cascade. The pathophysiologic failure
cascade search engine may be programmed to continuously search the
EMR for evolving complex images of physiologic failure such, for
example a sepsis cascade, the alarm processor may be programmed to
provide an alarm upon detection of the pathophysiologic failure
cascade, and the image processor programmed to output an image of
the evolving failure cascade. The data processing system may also
be programmed to quantify the cascade, track the progression of the
cascade, identify, highlight and/or alarm associated events in
relation to the cascade, determine the cost associated with the
cascade, determine the timing of treatment in relation to the
cascade, and determine the response of the cascade in relation to
treatment.
[0055] In another embodiment the processor is programmed to convert
the electronic medical records into a particular format favorable
for searching for pathophysiologic cascades. In one example such a
format comprises sequential and timed trends comprised of at least
positive trends and negative trends of both the physiologic
parameters and the laboratory data, detect relational trends
comprised of a combination of positive and/or negative trends,
detect complex cascade patterns comprised of a plurality of
combinations of relational trends, automatically output a display
of the image of the detected complex cascade, automatically output
a warning indicating the detection of the complex cascade, track
the growth or decline of the complex cascade and output an
indication indicative of growth or decline the cascade pattern may
be indicative of a single or multiple physiologic failures such as
at least one of sepsis, severe sepsis, septic shock, and
microcirculatory failure, a shock cascade, and a septic shock
cascade to name a few. The processor may be programmed to determine
and output an indication of the type of the cascade detected, to
determine and output at least an indication of the timing and type
of the trends along the cascade, to determine and output at least
an indication of the length of the cascade, to detect the onset of
therapy and to determine and output at least an indication of
timing of therapy in relation to the cascade. The patient data
processing system may comprise a computer be programmed to search
the EMR to detect sequential and timed trends comprised of at least
positive and negative trends of both the physiologic parameters and
the laboratory data, determine relational timing of the detected
positive and negative trends, detect complex cascade patterns
comprised of a plurality of combinations of positive and negative
trends evolving in sequential timed relation to each other, and
output an indication of the detected complex relational cascade
pattern.
[0056] In another embodiment, the processing system may comprising
a computer programmed to convert the electronic medical records
into sequential and timed trends comprised of at least positive
trends and negative trends of both the physiologic parameters and
the laboratory data, detect relational trends comprised of a
combination of positive and/or negative trends, detect complex
cascade patterns comprised of a plurality of combinations of
relational trends, output an alarm indicating the detection of the
complex cascade pattern.
[0057] Another embodiment comprises a patient data processing
system for processing electronic medical records of at least
physiologic parameters and laboratory data of at least one patient
comprising a computer programmed to identify positive and negative
trends comprising at least a combination of inflammatory trends,
metabolic trends, hemodynamic trends, hematologic trends, and
respiratory trends, identify the relational timing of the positive
and negative which relationally or collectively are indicative of
the septic shock or pre-septic shock failure cascade, and identify
and output an indication of the septic shock or pre-septic shock
failure cascade. The may be further programmed to identify the
onset of treatment, and identify the timing of treatment in
relation to at least one component of the cascade and to analyze
the relational pattern to identify the earliest trend comprising a
component of the cascade, identify the onset of treatment, and
identify the timing of treatment in relation to said earliest
trend. In another embodiment for processing electronic medical
records of at least physiologic parameters and laboratory data
comprising a computer programmed to generate a large set of
time-series of data of a patient including at least data relating
to the physiologic state and/or care of a patient, convert the
datasets, including at least the monitored datasets and laboratory
datasets into parallel and overlapping time series, identify
occurrences comprising at least, inflammatory occurrences,
metabolic occurrences, volumetric occurrences, hemodynamic
occurrences, therapy occurrences, hematologic occurrences,
respiratory occurrences, identify the timing of the occurrences,
identify at least one relational pattern of occurrences along a
plurality of time series which is indicative of failure cascade of
at least one of a sepsis cascade, a pulmonary embolism cascade, a
metabolic cascade, and a microcirculatory failure cascade, output
at least one of an indication of the cascade, the timing and type
of the occurrences along the cascade, and length of the cascade.
Another embodiment comprises a method for converting the global
electronic medial record into a patient monitors the method the
electronic medical record system having a display comprising steps
of converting the electronic medical records into sequential and
timed trends comprised of at least positive trends and negative
trends of both the physiologic parameters and the laboratory data,
detecting relational trends comprised of a combination of positive
and/or negative trends, detecting complex cascade patterns
comprised of a plurality of combinations of relational trends,
outputting a display of the image of the detected complex cascade,
outputting a warning indicating the detection of the complex
cascade pattern, and tracking the growth or decline of the complex
cascade and output an indication indicative of growth or
decline.
[0058] In another embodiment the patient data processing system for
processing electronic medical records of at least one patient
comprising a computer programmed to convert at least the
physiologic and laboratory data of the electronic medical records
into a predetermined format for imaging, imaging the formatted
electronic medical record, detect an image indicative of at least
one of patient physiology and patient care, output an indication of
the presence of physiologic failure. The computer may be further
programmed to analyze the images to detect relational patterns of
the detected physiologic failure to determine the severity of the
physiologic failure and/or to detect relational patterns indicative
of the patient response to the detected physiologic failure to
determine the severity of the physiologic failure and/or to detect
relational patterns indicative of patient care in response to the
detected physiologic failure to determine at least one of the
timeliness and efficacy of the care. The physiologic failure can
for example be at least one of sepsis, severe sepsis, septic shock,
a sepsis cascade, microcirculatory failure, a shock cascade, a
septic shock cascade to name a few.
[0059] The predefined format may comprise a time series matrix, an
objectified time series matrix, or anther format. The predetermined
format may include at least one region comprised of at least one
collection of time series of specific physiologic components. For
example at least one of inflammation indicators, respiratory
indicators, cardiovascular indicators, and metabolic indicators to
name a few. The predefined format can comprise a plurality of
regions comprised of a plurality of collections of time series of
different specific physiologic components. The images may be
comprised of aggregations of variations of physiologic data and
laboratory data, the variations having positive or negative slopes,
and/or aggregations of relational variations of positive and/or
negative trends of physiologic data combined with positive and/or
negative trends of laboratory data.
[0060] The patient data processing system may include an image
archive system for archiving images of physiologic failure and for
sharing these with other processors to grow the general archive and
knowledge of the different images and variations of the images of
failure. The patient data processing system may convert the time
series matrix into a predetermined format of the time series matrix
and image the formatted time series matrix to detect an image
indicative of physiologic failure.
[0061] One embodiment comprises a patient data processing system
having an object recognition system, for processing medical records
of at least one patient comprising a computer programmed to convert
the medical records of at least the physiologic and laboratory data
of at least one patient into a time series matrix defining vertical
and horizontal axes, and, using the object recognition system,
search the time series matrix continuously or intermittently for a
cascading plurality of relational patterns indicative of evolving
physiologic failure along both the vertical and horizontal axes of
the matrix.
[0062] Another embodiment comprises a patient data processing
system for analyzing electronic medical records for real-time
detection of physiologic failure comprising steps of continuously
or intermittently search the medical records to detect events along
the time series matrix, detect relational events along the time
series matrix comprised of the detected events, detect relational
cascade patterns comprised of a plurality of combinations of
relational events, take action based on the detection of the at
least one pattern wherein, for example, the pattern is indicative
of physiologic failure.
[0063] In one embodiment the search engine is programmed to detect
cascades comprised of at least a plurality of linked perturbations
and trends of physiologic and laboratory data associated with
relational compensation creating a progressively enlarging
aggregation of progressively greater numbers of perturbed
physiologic and laboratory data. The processor may be further
programmed to determine at least one characteristic of the cascade,
the characteristic comprising at least one of, the severity of the
cascade, the duration of the cascade, the time of onset of the
cascade, and the maturity of the cascade, the timing relationship
of the cascade to other events or other cascades, the cost
associated with the cascade, the global pattern of the cascade, the
time of termination of the cascade, the components of the cascade,
the state of evolution of the cascade, the length of stay
subsequent to or in association with the cascade, the treatments
associated with the cascade. The characteristic of the cascade may
be defined by, for example, the number of perturbations and/or
trends which comprise the cascade, the severity of the
perturbations and/or trends, the number of systems affected by the
cascade, the presence, number and/or severity of failure of
compensation in response to perturbations associated with the
cascade, and the growth of the cascade to name a few. The processor
may be programmed to determine the rate of growth of the evolving
cascade as by for example one or more of the increase in number
and/or severity of new perturbations being added per unit time, and
the increase number of systems affected, and/or the increase number
of perturbations present in different systems, to name a few.
[0064] The processor may be programmed to detect the events or
components which are temporally and/or spatially associated with
the cascade but which are not part of the cascade for example
treatment, surgical procedures, transport, injections, blood
transfusion, sedation, IV assess, catheterization, manipulation, to
name a few.
[0065] A processor-based system may characterize and quantify
patient physiological conditions by analyzing data relating the
patient into time series data and then generating an image or
moving image of the abnormal components of the time-series that may
be further processed into operator-interpretable data. According to
one embodiment, this may be accomplished by generating a large set
of time-series of data relating to the physiologic system,
converting the datasets (including monitored datasets, laboratory
datasets, and historic datasets) into parallel time series of each
data component, separating the unperturbed time-series components
from the perturbed time-series components, aggregating the abnormal
components into a real-time motion pictures of the abnormal
components, and recognizing and interpreting the motion pictures
and the events relational events and image components of the motion
picture.
[0066] In one embodiment, data from the electronic medical records
and patient monitors are used to generate graphical displays, which
may include moving pictures of the patient condition. In an
embodiment, such moving pictures, or animated displays, may be
referred to as "motion pictures of physiologic condition" (MPPC).
Provided herein is a processing system and method for generating
real-time MPPC of clinical data. The data and/or images may also be
analyzed to detect perturbations, aggregate and cascading
perturbations, perturbation relationships, physiologic responses to
perturbations, treatments associated with the perturbations,
physiologic responses to the treatments, physiologic failures,
testing failures, treatment failures, and communication failures to
generate the MPPC. In addition, the MPPC may also include a
graphical representation of any treatment applied in association
with the clinical condition.
[0067] Once the image or moving image (i.e. an image that includes
more data over time as the patient monitoring progresses) MPPC of
the patient condition has been generated, this image may be further
processed to create an operator-interpretable indicator to assist
in patient diagnosis and/or treatment. For example, the image may
be directly compared to a database of similar images taken from
patients with clinically confirmed diagnoses. The database image or
composite of multiple images with the greatest similarity to the
generated image may indicate the correct diagnosis for the patient.
For example, if the generated moving image, particularly as the
image progresses over time, has the greatest similarity to a
database image indicating "myocardial infarction," a processor may
generate a text or other indicator to a healthcare provider
indicating such a diagnosis. The processor may also indicate that
additional tests should be ordered to confirm the diagnosis. The
processor may also indicate and/or provide orders for specific
treatments in light of the diagnosis. In an embodiment, a moving
image may be indicative of two or more clinical conditions. The
processor may indicate tests that may rule out one or more of such
conditions. In addition, over time, one condition may be determined
by the processor to be more likely while additional time-series
data may also rule out another condition.
[0068] These database images may be formed from retrospective
clinical data. In an embodiment, the images may be analyzed for
similarity by any suitable technique, including image registration.
In an embodiment, the individual time-series objects that make up
the image may be processed as a group for similarity to other
groups of time-series objects associated with a particular
diagnosis or clinical condition. The MPPC may, for example, include
abnormal and/or perturbed components and in particular "Motion
Pictures of Physiologic Failure" (MPPF) of the physiologic system
and of exogenous forces relating to that system.
[0069] Also provided herein is a processor and processing method
for the automatic generation and/or analysis of the images of
physiologic and/or clinical condition and the characterization and
aggregation of the image components of complex dynamic systems,
such as physiologic systems and medical care systems. The
processing system may generate real-time MPPC of healthcare signals
and processing those images to timely detect perturbations,
aggregate and cascading perturbations, perturbation relationships,
physiologic responses to perturbations, treatments associated with
the perturbations, physiologic responses to the treatments,
physiologic failures, testing failures, treatment failures, and
communication failures to generate and then recognize motion
pictures of physiologic failures and of the treatment applied in
association with the failures. According to one embodiment, a
processor first renders parallel time-series from each of a
plurality of sensors and testing sources, which are applied to
broadly monitor the dynamic system for failure. In an example, a
processor programmed with instructions for time series
objectification of patient data detects patterns along the parallel
times-series, converts these patterns into time series of discrete
objects, then organizes these objects into discrete relational
objects (such as binary objects, or relational binaries, derived of
relational object pairs). The processor then organizes the
relational binaries to render a unifying programmatic image of the
physiologic system and the care provided. The processor then
automatically recognizes objects in the image components and may be
able to perform analysis on the images.
[0070] One embodiment may a patient safety processor having a
single processor or a combination of processors programmed to
generate time series objects, a relational binaries, moving images,
patient safety images, and/or patient safety visualizations. The
patient safety processor outputs images of the patient's
physiologic system and medical care. In an embodiment, the
processor includes processing functions for time series
objectification, relational binary processing, and an imaging
processing. In an embodiment, the imaging processing includes a
single matrix construction processor.
[0071] According to an embodiment, perturbations detected by the
processor are converted to image components that may be used to
generate a moving image. In an embodiment, an MPPC may be
representative of a "motion picture of physiological failure"
(MPPF) when a failure image becomes progressively more complete and
recognizable by the processor as each additional failure image
component is added. One embodiment may involve building a dynamic
real-time image of disease, injury, and/or drug reactions, the care
provided, and the expense associated with that care. The image is
initially associated with initial image components including one or
more minor perturbations, which may for example be caused by
circulation of one or more toxic and/or immunogenic material of
endogenous or exogenous origin. At first these perturbations, such
as toxins, inflammatory and/or thrombogenic mediators, may induce
and/or cause only minor changes in cell permeability, ion flux, and
trigger various minor physiologic perturbations and responses each
of which may produce an image component. The measurements of
various mediators, ions, biologic profiles, as well as standard
blood tests, and the outputs of vital sign monitors may begin to
vary as a function of these early physiologic perturbations and
responses, and it is these variations that enlarge the group of
image components from which the larger image (i.e. the MPPF) is
derived. Early in the process, each of these alterations in
permeability, cell injury, mediator production, and physiologic
perturbations, when considered in isolation, are often minor.
However, collectively they may represent the early manifestations
of a nascent and evolving moving image of a serious clinical
condition.
[0072] According to one embodiment, each perturbation is
programmatically organized to form an image component of the MPPC.
Many of these detected images components may be isolated because
they are related to a benign process, and the image may
self-extinguish or may not develop into an image associated with a
clinical condition involving intervention or an MPPF. Yet, as noted
above, others may represent the first image components of an early
moving image. Provided herein are systems and methods for the
detection of the early image components of an evolving moving image
to provide timely detection of physiologic failures before these
failure progresses to shock (including, for example, hypovolemic,
obstructive, septic, toxic, cardiogenic, hypoxic, and/or
hypercarbic shock.) In one embodiment, it is advantageous to detect
the early image components of the moving image before shock
develops to improve the prognosis for the patient and to apply
goal-directed therapy while clinical intervention is still
beneficial.
[0073] According to one embodiment, a patient safety processor
generates a MPPC, which may be used for processor-based
protocolization of care. This motion picture may be comprehensive
of multiple data sources, including not only the events comprising
a single or few parameters, such as heart rate, but also other
parameters that may include, for example: the slope and pattern of
the heart rate, the slope and patterns of the systolic pressure
variation, the slope and patterns respiration rate, the slope and
patterns SPO.sub.2, the slope and patterns ventilation-oximetry
index, the slope and patterns drug and fluid infusion rate, the
slope and patterns blood pressure, the slope and patterns of the
Neutrophil count, and the slope and patterns of inflammatory and/or
thrombotic markers, and various other blood, urine and/or exhaled
gas test to name a few. The signals from all of these sources may
be converted to time-series and/or step functions and may, for
example, be physiologic signals, therapy signals, laboratory
signals, or historical signals, which may be objectified, as by an
objectification processor, to produce the discrete programmatic
objects (events). According to one embodiment, the processor
detects a first discrete event that includes a pattern or value of
at least one medical signal, and a second discrete event that
includes a second pattern or value of at least one medical signal,
the processor then aggregates at least the first event and the
second event to produce a first relational object, the processor
further detects a third event that includes a pattern or value of
at least one medical signal, and a fourth event that includes a
second pattern or value of at least one medical signal, the
processor then aggregates at least the third event and the fourth
event to produce a second relational object. The first relational
object and the second relational object are then aggregated to
produce a first image component. Additional image components are
built accordingly and the image components are then aggregated
according to the time of occurrence to derive the moving image and
care.
[0074] In an example, the pulse related components of the typical
motion picture of sepsis failure cascade would include occurrences
such as early rise in heart rate, rise in pulse amplitude, and rise
in slope of the pulse upstroke (as measured at the finger tip) in
combination and typically proceeded by a brisk rise in inflammatory
markers. In contrast the typical motion picture of occult
hemorrhagic failure cascade (as for example due to heparin related
retroperitoneal hemorrhage) would include occurrences of an early
rise in heart rate, a fall in pulse amplitude, and a fall in slope
of the pulse upstroke (as measured at the finger tip) and a rise in
the respiratory related pulse pressure variation and a fall in
hemoglobin. According to one aspect of the present invention all of
these occurrences along the image of an occult hemorrhagic failure
cascade can all be derived from a multi wavelength pulse
oximeter.
[0075] According to an embodiment, a relational binary processor is
provided that divides detected variations into discrete alpha
events and beta events, which are combined by the relational binary
processor to construct the relational events which are termed
relational binaries. These relational binaries are aggregated
according to timing to construct image components. These image
components are then further aggregated according to timing to
construct and progressively build MPPC (from which visual images or
electronic representations may be derived as desired). These MPPC
are often moving images of catastrophic cascading failures, thereby
allowing more reliable detection to allow timely rescue of the
patient.
[0076] The signals may be chemical or physiologic measurements, as
provided by patient monitors, recorded in the electronic medical
record, and/or may be biomarkers specifically ordered, either
automatically by the processor or manually by the clinician to
indicate the potential presence of the sepsis (as those, for
example, disclosed in U.S. patent application Ser. Nos. 10/704899,
11/647,689). The presence and/or concentration of such markers may
be presented in the context of the MPPC with the timed positioning
relative to the others parameters, which then allows the relevance
of the biomarker to be much more readily identified. According to
an embodiment, the temporal and relational pattern of inflammatory
markers and temporal and relational patterns of contemporaneously
measured or associated physiologic parameters are aggregated to
produce a progressively enlarging MPPC of an evolving patient
condition.
[0077] Therefore, to achieve the detection of various pre-shock
states as well as earlier detection of failures, one embodiment
detects early variations and aggregates them to provide an MPPC to
dynamically present expanding failure cascades of pre-shock and
shock states. This allows separation of expanding images from the
smaller and less expansive image components having benign
characteristics, and further allows separation of the images of
minor isolated failures from failures that progress to generate an
expanding MPPC heralding the potential for transition to one of the
shock states. Each group of images as well as the complete MPPC and
care may be analyzed for the purpose of assessing patient care in a
hospital, a ward, or under the care of a given healthcare
worker.
[0078] The occurrence of a large number of image components
indicating non cascading failures which self extinguish may be
indicative of an unstable patient population or poor health care
delivery. In the alternative a large number of cascading failures
are indicative of major risk of a high rate of death or injury in
that environment or under that healthcare workers care. The MPPC
and the image components may be used to determine if that is due to
the patient population or the quality of the care.
[0079] One embodiment detects failure cascades along with the
determination of the specific fundamental perturbations, or
treatments, or lack of treatments that occur early in a failure
cascade. Specific fundamental failures are detected before they
progresses to complex failures and particularly before they
progresses to the pre-shock or shock state. Furthermore, the
processor builds an image derived of the relational perturbations
and treatments as the cascade expands. According to one embodiment,
each time series is processed to separate expected events from
unexpected events. The unexpected and/or abnormal events are then
aggregated further to repetitively generate relational events,
image components and finally the MPPC which comprises a motion
picture of the cascade (if present) as well as the treatment
applied in association with the cascade. This MPPC is further
processed to allow the detection of the probable cause or causes of
the occurrence of the moving failure images well as the image
components of the MPPC as it evolves thereby allowing detection of
the nature and cause of the failure cascade.
[0080] As noted above according to one embodiment, an analysis is
provided wherein the fundamental components of the analytic process
comprise a basic relational variable that includes a plurality of
events. In a preferred embodiment, the basic relational variable is
that includes two events (a relational pair) and this is called a
relational binary. In one embodiment, the relational binaries are
initially selected by the users as from a menu (or by a drag and
drop interface) of relational binaries and/or of events from which
the user builds the desired object binaries the binaries are then
used as by drag and drop to build the image components and MPPC for
detection. This may be performed by, for example, by national or
regional expert groups, or by specific departments in a hospital,
or by an individual physician to provide custom management. This
may also be automatically performed by the processor (as, for
example, through the investigation of a large number of historical
data sets that have been comprehensively analyzed and categorized
according to outcomes. The objectified time series matrix and/or
the MPPC may be may be outputted in various interactive,
hierarchical, and relational formats for review and automatic or
manual adjustment.
[0081] The MPPC may detect a wide range of failures. For example:
"physiologic failures, treatment occurrence failures indicating the
absence of expected treatment in relation to a given perturbation,
testing occurrence failures indicating the absence of expected
testing in relation to a given perturbation, treatment response
failures indicating the absence of the expected correction of
perturbation or the occurrence of a new potentially complicating
perturbation in relation to a given treatment and/or dose.
[0082] The processor combines the complex data of the electronic
medical record into a single motion picture of perturbations,
treatments, physiologic responses, diagnostic testing, recoveries,
diagnoses, missing data, patient locations, and/or other datasets.
Dynamic images are generated of relational variations of a set of
time series associated with a complex system to generate a real
time motion picture of a failure of the system and/or of forces
applied to the system. According to one embodiment of the present
invention, the patient safety processor automatically outputs a
unified timeline, for example, derived of detected images of a
given type. According to another embodiment of the invention the
processor, upon detecting a failure cascade, may present and
highlight the evolving MPPC in real time on an outputted display of
an image diagram for the physician to review. The portion of the
motion picture, which has already been completed, may be reviewed
backward and forward to review in a single summary snap shot
view.
[0083] In one embodiment, an electronic medical record may be
converted to an MPPC. A patient data processing system comprising a
processor programmed with instructions for converting an electronic
medical record into trend data, such as sequential, timed data, for
example trends of physiologic parameters and laboratory data over
time. When the data is converted into trend data, the processor may
detect relationships between the trend data. For example, such
relationships may include positive and/or negative trends. The
relationships may be relational trends (i.e., when one parameter
goes up, another parameter goes down). Complex cascade patterns of
physiological conditions may be formed from a plurality of
combinations of relational trends. The complex casade may form an
MPPC, for example of sepsis, severe sepsis, septic shock, and
microcirculatory failure, a shock cascade, and a septic shock
cascade.
[0084] For example, a processor may process the electronic medical
record and search to detect sequential and timed trends including
positive and negative trends of physiologic parameters and
laboratory data. Then, the processor may determine relational
timing of the detected positive and negative trends to detect a
complex cascade patterns that include a plurality of combinations
of positive and negative trends evolving in sequential timed
relation to each other. The processor may output an indication of
the detected complex relational cascade pattern. For example, the
indication may be physiologic failure, such as sepsis, severe
sepsis, septic shock, and microcirculatory failure, a shock
cascade, and a septic shock cascade. The processor may also provide
detailed information about the individual trends, such as the
length of each trend or the timing of the entire cascade. If
therapy information is included in the electronic medical record,
the image may include an indication to mark the onset of therapy
and to determine and output at least an indication of timing of
therapy in relation to the cascade. If the electronic medical
record is from a patient that is still in care, the processor may
include an alarm functionality to indication early points in a
failure cascade.
[0085] In another embodiment, a patient data processing system may
identify specific positive and negative trends, such as a
combination of inflammatory trends, metabolic trends, hemodynamic
trends, hematologic trends, and respiratory trends. After the
identification of the trends, the processor may identify the
relational timing of positive and negative trends, which
relationally or collectively are indicative of the septic shock or
pre-septic shock failure cascade to identify and output an
indication of the septic shock or pre-septic shock failure
cascade.
[0086] During a failure cascade, the earliest point in the cascade
may include an earliest trend (e.g., a respiratory, immunologic,
hemodynamic, or other patient trend) that marks the beginning of
the cascade. Therapy intervention at this point may have the
highest chance of success. In one embodiment, a processor may
analyze the relational pattern to identify the earliest trend of a
component of the cascade, identify the onset of treatment, and
identify the timing of treatment in relation to said earliest
trend. Such analysis may benefit caregivers in determining which
therapies have the highest success rate for a particular
physiological condition cascade. Alternatively, such information
may also help a physician determine which types of cascades are
likely to be self-limiting.
[0087] Many physiologic failures such as, for example septic shock,
pulmonary embolism, congestive heart failure, respiratory arrest
due to narcotics in the presence of sleep apnea, thrombotic
thrombocytopenia purpura (TTP), hemorrhage due to anticoagulation,
respiratory failure due to bronchospasm, and adult respiratory
distress syndrome, but not limited to these clinical conditions,
begin with one or two non-specific perturbation(s). Physiologic
failure is commonly a relational expansion, often beginning with a
fundamental physiologic perturbation at a single focal point in
time. In fact, this initial perturbation is often completely masked
once the cascade has progressed past a certain point. In such
cases, testing or monitoring for the single perturbation may not be
useful for making a diagnosis. In many cascading clinical
conditions, the first perturbation(s) of the cascade may often only
be detected in retrospect after the cascade has further progressed
when the first perturbation(s) is no longer present. This provides
a basis for optimizing the detection of the first point(s) by
real-time imaging of the cascade as it develops and then examining
the image to determine the first perturbation(s).
[0088] While a pattern of a single time series provides a larger
image of a dynamic process than a single value or range, such a
pattern is still only a tiny image fragment of the process. The
determination of thresholds and even the detection of various
patterns of perturbations comprise incomplete analysis, which will
inevitably allow an unacceptable rate of progression to
catastrophic failure. Even in situations wherein a measurement or
test may seem definitive as a stand-alone test, action or
conclusions based on a single value (or an average of a plurality
of values) will have a reasonable probability of being incorrect.
Consider, for example, a single measured spot SPO.sub.2 value of
94. This value is largely meaningless without knowing if the
SPO.sub.2 is rising, falling, or cycling. Yet this infinitesimal
image fragment of a patient's complex physiologic system is used
everyday in hospitals to determine care. Furthermore, even if the
pattern of the SPO.sub.2 is known (for example the SPO.sub.2 has
been stable at about 94 for at least 12 hours) this is an
incomplete image, which is largely useless and, in fact, a
potentially misleading piece of information. Without knowing the
relational pattern of the minute ventilation during the related
time interval of the measured SPO.sub.2 pattern, the healthcare
worker may be lulled into a false sense of security even as the
patient is dying of septic shock or heart failure. Furthermore, an
alarm or interpretive output which is based on a programmatic image
of both the patterns of both the SPO.sub.2 and the related minute
ventilation without additional relational elements of the image,
such as, for example, the associated pattern of the white blood
cell count, temperature, pulse, blood pressure, microbiologic
values, and medications will be incomplete leaving too much
synthesis for the healthcare worker. In another example, consider
the detection of a pattern of a sustained rise in pulse or
respiration rate. Each such pattern represents a tiny fragment of
the present physiologic state and each pattern may be benign or
alternatively may be an early image component of a much larger
dynamic process of failure often associated with an evolving
failure cascade. The difference between a benign or pathologic rise
in pulse or respiration rate cannot be determined with this tiny
image alone and often cannot even be known at the time of the onset
of the rise. Therefore a tree diagram protocol with a branch based
on a rising pulse or rising respiration rate adds a great degree of
programmatic complexity with a high risk that the protocol will
precede down the wrong pathway.
[0089] As noted above when the detection and the determination of
the mode of potentially fatal but profoundly complex physiologic
failures is left to a population of heterogeneous healthcare
workers, an unacceptable rate of death may be anticipated.
[0090] As well, an incomplete analysis of the physiologic system
will often cause the healthcare worker to generate a large amount
of investigation, testing, analysis and evaluation that is not
necessary and therefore increases the cost of overall care.
Further, these false paths of treatment and evaluation may distract
the care worker from the determining the actual operative failure
modes, which will ultimately induce adverse outcomes.
[0091] Prior to shock, a patient's physiologic system is perturbed
by both disease and treatment. A given treatment provided to
correct a perturbation might reduce the perturbation, have no
effect on the perturbation, exacerbate the perturbation, cause
another perturbation and/or make another perturbation worse or
better. To determine which effect a treatment is having and to
assure that this determination of treatment effect is complete, it
is necessary to collect and, just as importantly, as provided by
one embodiment, organize and analyze large amounts of relational
data in a timely manner.
[0092] Another problem is that, within present hospital systems the
healthcare worker is forced to do a great deal of archeology
(digging, isolating, identifying, etc.) before synthesis may be
effectively completed. For this reason, the synthesis of
information by the healthcare worker is often not executed in a
manner, which allows immediate searching, filtering, re-analysis,
etc. This friction combined with the typical workload of healthcare
workers limits the number and range of high-level scenarios, which
may be investigated. Also the healthcare worker may, because of
lack of available organized data and time, execute decisions
without a complete set of synthesized information and worse, may
not realize that this is the case.
[0093] For these reasons, even with conventional electronic medical
record embedded protocols, patients remain subject to a range of
failures across a broad range of failure modes based on the
complexity of their individual condition and the complexity of the
environment facing the care giver. In fact, because failures often
overlap, one protocol may reduce the risk of one failure while
increasing the risk of another. For example, oxygen given to treat
hypoxemia under one protocol may delay the detection of pulmonary
embolism by stabilizing the SPO.sub.2 and hiding the early signs of
impending shock from the healthcare worker.
[0094] Although the number of potential modes of failure is very
high in any hospital environment, the occurrence of certain modes
of failure is reasonably likely under a given set of circumstances
in the hospital. A failure mode diagram illustrating common modes
of failure given a combination of a group of diseases is shown in
FIG. 1. The number of potential failures may be very large (in the
hundreds) for a given patient in a hospital setting and the nurse
or physician is often expected to monitor many such patients on the
floor while timely detecting the failures such that the nurse is
expected to timely detect even a single failure from as many as a
thousand failures which may occur among the patients under his or
her care. For this reason, processor based failure imaging and
detection is desirable.
[0095] FIG. 1 illustrates a complexity diagram 200 of an exemplary
patient on a medical hospital ward. The diagram 200 demonstrates
the level of complexity that may be modeled into moving images as
provided herein to determine the nature of and origin of
perturbations within this level of complexity. The diagram 200 is
one type of failure mode diagram which may be constructed by an
expert panel and then used according to one embodiment to
facilitate the construction of the various components the moving
images provided herein, including the events, relational binaries,
and image components. The failure image component diagram 200
includes a number of overlapping diseases present for this single
patient including diabetes 202, congestive heart failure 204,
arterial fibrillation 206, stroke 208, sleep apnea 210 and sepsis
212. The diseases may induce physiologic failures, such as a
divergent rise in ventilation 216, a rapid ventricular rate 218,
pulmonary edema 214, and fall in oxygen saturation (hypoxemia) 222.
Furthermore the treatments are potentially associated with
medication failures such as a high threshold breach of the partial
thromboplastin time (PTT) or a low threshold breach of the glucose
(hypoglycemia) 234. Additionally, the administration of a treatment
(for example, insulin 224, a diuretic 226, an ACE inhibitor 228, a
beta blocker 230 and/or heparin 232) to a patient may lead to
additional physiologic failures (for example, a fall in platelet
count (thrombocytopenia) 236, the occurrence of heart block 238, a
fall in serum potassium (hypokalemia) 240, a fall in serum sodium
(hyponatremia) 242, a fall in blood pressure (hypotension) 244. In
one embodiment, a single patient may have early high blood glucose
(hyperglycemia) 215 followed by later low blood glucose
(hypoglycemia) 234. As shown, the interrelationship of progression
of multiple diseases, the patient symptoms, and multiple treatments
may lead to treatment delay 248 or confusion 220.
[0096] FIG. 2 depicts an overview of the flow of analysis for
modeling complex patient physiological condition in one embodiment.
A wide range sources may provide inputs to the modeling. For
example, patient monitors 256, patient records 272, historical
patient data 260, lab results 264 and therapy data 268 may provide
the raw data input into the analysis stream. These inputs are
converted to a set of parallel time series 276. Patterns and
threshold violations along this plurality of parallel time series
identified, coalesced, synthesized and organized into discrete
objects forming object streams 280 within each channel. These
discrete objects are analyzed to identify known relational patterns
into instances of relational binaries 284. In one embodiment,
expert systems then further refine the analysis by organizing and
synthesizing these relational binaries into a set of failure images
288, which as an aggregate whole make up a unified programmatic
image of the complex and dynamic state of a patient and/or a
patient population.
[0097] FIG. 2 depicts the flow of analysis 240 from raw data to the
aggregate of images, while FIG. 3A and FIG. 3B includes some of the
data stores, data flow, processors and output mechanisms within the
exemplary embodiment. FIG. 3A depicts another data flow of one
embodiment. The data management system 300 includes a monitor 302,
a processor 304 that may include, for example, time series
objectification processor 336, relational binary processor 348, and
failure imaging processor 360. Alternatively, processors 336, 348,
and 360 or instructions for performing the processing steps of time
series objectification, relational binary processing, and/or
failure image processing may be located on one or more additional
processing components in communication with processor 304 that are
part of the system 300. The processor 304 is adapted to provide
output of the analysis to a device 306, which provides an interface
for a healthcare worker. The data flow involves inputs from a wide
range of sources (304, 308, 310, 312, and 314). As shown, the
inputs may be sent to a processor 304 that may direct further
action for the patient, including testing orders 316, indicators to
the healthcare provider that may be displayed on a console or
device 306, and therapy orders 315. Accordingly, the healthcare
worker may use the device 306 to control and oversee the entire
hospitalization process. In one exemplary embodiment, the processor
304 may be used to drive the device 306. The processor 304 may be
adapted to constantly process all of the real-time data of all of
the patients regardless of the status of the viewing console and to
automatically send testing orders 316 and/or therapy orders 315
based on the analysis of the images derived from the processor 304,
as will be discussed.
[0098] The data management system 300 may include one or more
processor-based components, such as general purpose or
application-specific computers. In addition to the processor-based
components, the data management system 300 may include various
memory and/or storage components including magnetic and optical
mass storage devices and/or internal memory, such as RAM chips. The
memory and/or storage components may be used for storing programs
and routines for performing the techniques described herein that
are executed by the processor 304 or by associated components of
the data management system 300. Alternatively, the programs and
routines may be stored on a computer accessible storage medium
and/or memory remote from the data management system 300 but
accessible by network and/or communication interfaces present on
the computer.
[0099] The data management system 300 may also comprise various
input/output (I/O) interfaces, as well as various network or
communication interfaces. The various I/O interfaces may allow
communication with user interface devices, such as a display,
keyboard, mouse, and printer that may be used for viewing and
inputting configuration information and/or for operating the system
300. The various network and communication interfaces may allow
connection to both local and wide area intranets and storage
networks as well as the Internet. The various I/O and communication
interfaces may utilize wires, lines, or suitable wireless
interfaces, as appropriate or desired.
[0100] In an exemplary embodiment, the device 306 is turned on as
for continuous viewing (with a notification) by the processor 304
when images are indicative of a significant potential failure
and/or cascade process or at a point wherein the patient's risk
class exceeds a threshold value. The risk class may, for example,
be derived as a function of a calculated instability index or a
detected instability index pattern and/or detected failures. The
instability index may be, for example, a confidence metric
correlated with a matched image. For example, when an MPPC has a
high likelihood of being associated with a serious condition, the
instability index may be high. The instability index may be a
numeric index, a color or graphic indicator, and/or an audio or
text message.
[0101] In accordance with an exemplary embodiment, the device 306
includes an interactive single screen displaying items, such as one
or more working diagnoses, differential diagnosis, parameters
derived from patients including laboratory parameters, monitored
parameters, and subjective parameters (e.g., sedation scale,
confusion scale, or pain scale) or the like. In an embodiment, the
term "parameter" herein may refer to an absolute or relative data
point or set, a pattern, or a deviation, a range of such data
points or sets, a pattern of such data, a relationship along a
single set of data and/or or between a plurality of sets of data,
and/or patterns of data. The data may be an objective data type or
subjective data type and may be directly and/or indirectly derived
or historical in origin. In addition various outputs from the
failure imaging processor 360 (FIG. 3B) may be displayed. According
to on embodiment, the processor 304 may provide data for display
present on the device 306 or through a report (either electronic or
paper) or within an electronic representation that may provide an
interface to external systems.
[0102] The data management system 300 further includes a medical
records database 308 including laboratory data 310, historical data
(e.g., diagnosis) 312 and therapy data (e.g., medications) 314. The
medical records database 308 is coupled to the processor 304 and to
the monitor 302 so that those systems may have access the data
stored in the medical records database 308. The processor 304 may
include a component or direct link to the centralized patient
medical record, which contains real time data and receives data
input from all hospital sources. Thus, a database containing
substantially all of the components relating to the patient
available to the hospital may be directly accessible to the
processor 304 in real time to allow the embedded relational
processor render relational binaries, and construct and detect
failure image components which include these data from varied
sources.
[0103] In accordance with an exemplary embodiment, the processor
304 is adapted to comprehensively engage the medical records
database 308. As discussed further below, the processor 304 may be
programmed to provide for formal, automatic simultaneous
engagement, of physiologic failure image components, medication
failure image components, testing failure image components,
aggregate failure image components as derived from the relational
processor and to render them in a timeline for viewing.
[0104] The processor 304 may be adapted to provide an immediate
review of all failure image components and to take action based on
the detection of specific failure image components. The processor
304 may be capable of responding faster and more reliably than the
healthcare worker because it may be adapted to constantly monitor
the evolving failure image components form the earliest onset of
the first divergent binary. The processor 304 may therefore detect
failure image component cascades, which originate from single
divergent binaries, which might easily be undetected by the
healthcare worker until it is too late. The processor 304 may also
be programmed to alarm on divergent or null binaries upon which no
action has been taken or upon which the action has not corrected
the evolving divergent binary or failure image component. For
example, in a scenario in which the processor 304 has been updated
by the nurse that a blood culture has been obtained, the presence
of a null binary may be generated indicating testing failure image
component if after a pre-selected time the result is not available
to the processor 304 whereas the presence of a divergent binary
indicative of a physiologic failure image component may be detected
if the culture is positive. If testing failure image component is
detected the processor 304 notifies the lab of the apparent delay.
The notification is an alpha event and a receipt response to that
notification is a true beta event. Therefore the failure of the lab
to indicate receipt may cause the occurrence of a divergent binary,
which may trigger the notification of the nurse in the same manner
until a convergent binary concludes the sequence. If on the other
hand, a physiologic failure image component is detected (the
culture is positive), the processor 304 notifies the nurse again in
the same binary generating fashion.
[0105] While a positive blood culture is the beta event of the
culture testing binary, it is the alpha event for another group of
testing binaries such that the initial divergent testing binary may
cause the processor to assure acquisition of a complete blood
count, a comprehensive metabolic profile, increased frequency of
blood pressure and pulse measurements, ventilation indexing
oximetry and other testing as programmed into the processor 304 in
response to the specific divergent binary detected (in this case a
positive blood culture). These new testing binaries may generate
unexpected beta events (such as a low blood pressure, a high pulse,
or high ventilation to oximetry index) and these beta events may
thereby define a new set of divergent physiologic binaries. This
new set of divergent binaries (in aggregation) may be sufficient to
meet the pre-selected criteria of an aggregate failure image
component suggestive of early septic shock, which diagnostic
consideration now comprises an alpha event to a plurality of new
binaries which have been programmed into the processor to assure
timely and proper monitoring, timely proper patient location, and
timely proper diagnostic testing, and timely and proper
intervention in the event of the detection of this type of
aggregate failure image component. In addition, the beta events of
the divergent physiologic binaries which comprised the aggregate
failure image component now become alpha events for new physiologic
binaries wherein the beta event of each of the new binaries
comprises the return of each these values back to a normal range
within a pre-selected time period (thereby assuring, that the
aggregate failure image component is corrected timely, if
possible). In additional, the positive blood culture is also the
alpha event for a treatment binary such that the processor 304 may
be expecting to see the correct antibiotic in response to positive
blood culture administered within a pre-selected time interval. If
this does not occur a divergent binary indicating treatment failure
may be identified and assured nurse notification may proceed by the
binary building method previously discussed.
[0106] According to one embodiment, in response to the detection of
any significant divergent physiologic binary, the device 306 may be
programmed to prevent the failure of notification by building a set
notification binaries, which must end with convergence. The device
306 may also be programmed to prevent failure to timely treat by
building a set of treatment binaries, which must end with
convergence. Further, the device 306 may be programmed to prevent
failure test by building a set of testing binaries, which must end
with convergence. The device 306 may also be programmed to detect
associated physiologic failure image components by identifying
divergent physiologic binaries in associated with the initially
discovered divergent binaries.
[0107] According to one embodiment, the PSP includes an associated,
connected and/or embedded eventing system. In this eventing
subsystem, users may designate actions to be initiated or data to
be recorded when a specific occurrence is identified. This eventing
system may interface with other internal or external systems
including notification systems, workflow systems, asynchronous
communication systems, reporting systems, decision support systems,
dashboards, data warehousing and/or data mining systems to name a
few.
[0108] According to one embodiment the relational processor is
self-modulating and provides an automatically expanding analysis,
which is rapidly responsive to the occurrence of even a minor
failure image component. The analytic activity of the processing
system is capable of multidimensional growth and diminishment in
direct response to the magnitude and number of failure image
components detected. In this regard, the processor 304 upon the
occurrence of a physiologic failure image component may generate a
cascade of notification, testing, treatment, and physiologic
binaries even if that failure image component comprises only a
single physiologic divergent binary. The beta event of the
physiologic binary may comprise the alpha event of each of a new
generation of notification, testing, treatment, and physiologic
binaries. Each of these new binaries also have a beta event, each
which may induce the formation of other binaries wherein the beta
event comprises the alpha of another binary of the same or another
type. A spontaneously growing cascade of binaries thereby evolves
toward assuring timely notification, timely testing, and timely
restoration of physiologic stability.
[0109] A rapidly expanding, cascade of these types of divergent
binaries indicates evolving patient instability of the patient or
poor performance of the healthcare system. An analysis (as by
objectified pattern recognition or statistical analysis) of the
timed patterns of the types and sequence of the divergent binaries
may allow the determination of poor health or poor responsiveness
of the healthcare worker is causing the cascade to be propagated.
As health is restored, and provided the healthcare workers are
timely responsive, the binary cascade may automatically diminish
and the various failure image components may no longer be detected.
The outputs of the relational binary object processor therefore
provides a self modulating processing system which may be readily
used and further analyzed to track the health of a single patient,
or the patients on a given floor, or the patients hospital wide.
The outputs of the object binary processor also provides a self
modulating processing system indicative of the quality of
healthcare delivery provided to a given patient, on a given floor,
or hospital wide.
[0110] The processor 304 may be applied to other complex dynamic
data sets other than medical data wherein a self-modulating
relational analysis and control would be useful. The processor 304
has utility for the data mining, for example in association with
the processing of archived datasets to identify the failure image
component process from the initial spark (the first divergent
binary) to extensive system failure. The processing of archived
datasets provides the opportunity to review the automatic
modulation of the binary cascades which are derived of various
failures and to facilitate the construction of dynamic failure
image component diagrams for complex processes in the hospital, as
well as in industrial processing such as the food, chemical, or
pharmaceutical processing. The processor may be programmed such
that the user may select each alpha event and allow the processor
to detect, offer, and/or derive events and relational binaries,
which have specified temporal, frequency, or spatial relationships
with the selected event object. Alternatively the processor 304 may
be programmed to construct its own set of convergent object
binaries with a learning dataset by processing the outputs of
healthy individuals and then the processor may be used to detect
divergent binaries when applied to patients by identifying the lack
of the expected beta events (which were defined by the learning
dataset). Sensitivity for cascading (the initiation of further
processing based on the detection of a divergence or a failure
image component) may be adjusted by modifying the sensitivity for
trueness of the beta event or by modifying the criteria such as
slope, or magnitude of the objects during the objectification
process. This provides a high degree of flexibility in defining
sensitivity to the designation of a binary as divergent and this
therefore allows a high degree of control over the sensitivity to
cascade initiation, propagation, and extinguishment. Cascades may
be modular or divergent or failure image component specific. A
modular group of cascades may be selectable from a menu and then
each one in the group modified as desired.
[0111] As shown in FIG. 3B, the processor 304 may include
instructions for any number of processing functions. As shown the
processor 304 may include an event editor 331 (creates event
definitions 332), a convergence editor 343 (creates binary
definitions sets 344), and a failure image component 355 (creates
failure components 356). The event definitions 332, binary
definitions 344, and failure components 356, may be used an inputs
for the time series objectification processor 336, the relational
binary processor 348, and the failure imaging processor 360. The
time series Objectification Processor 336 is programmed, with the
rules and parameters provided by the event definition set 332, to
convert parallel time series (324, 328) of the electronic medical
record 320. The relational binary processor 348 then, with the
rules and parameters provided by the binary definition set,
processes the object streams 340 to generate streams and cascades
of relational binaries 352. Further then, the failure imaging
processor 360, with the rules and parameters provided by the
Failure image component definition set 356, synthesizes the
relational binaries, and in some cases isolated objects from the
object stream, into one or more images 364. The output of each of
these three processors (336, 348 and 360) as well as the original
time series upon which they were applied are stored in an MPPC
database 368. In an example, the processor 304 may be programmed so
that detection of one or more events, binaries, image components or
detection of a specific MPPC, may cause the processor to take
action such as provide an outbound notification of the detection,
orders for testing or treatment, or direct control signals to a
treatment and/or testing device to change, cease or initiate
testing and/or treatment.
[0112] According to one embodiment, the relational binary processor
348 and the time series objectification processor 336 may adapt to
the output of each other to modify the analysis. For example, the
detection of an event, a reciprocation, an incomplete reciprocation
or other objects or patterns by the time series objectification
processor 336 may cause an adjustment to the cascade responsive to
the detection of a divergence. Alternatively or in combination the
criteria for designation of a wave segment as an event object
within the time series objectification processor 336 (for example
the slope criteria for identifying a fall event object of serum
sodium) may also be adjusted based on the presence of a specific
alpha event. In an example, when an alpha event comprising a
diagnosis of cerebral vascular infarction (CVA) is detected, this
may cause the time series objectification processor 336 to reduce
the absolute slope (less negative slope) for designating a fall
event object of serum sodium, which, is preferably one of the betas
in such patients. By automatically reducing the absolute slope for
the designation of the beta event the alpha diagnosis of cerebral
vascular infarction is adjusting the sensitivity of the diagnostic
process allowing automatic and dynamic adjustment upon the
occurrence and detection of different physiologic vulnerabilities.
In this example, the increase in sensitivity for detection of a
fall event object in serum sodium (which, combined with the alpha
that includes a CVA diagnosis) would comprise a divergent binary),
which may trigger a diagnostic cascade for close monitoring of the
serum sodium and/or the evaluation of additional laboratory studies
and/or the reduction of free water delivery. This is desirable due
to the unique vulnerability faced by patients with CVA as a
function of the potential for inappropriate increase in
anti-diuretic hormone due to the CVA.
[0113] Since the relational binary definitions within the binary
definition set 344 may be individually defined and refined by
processing large populations of historical data, correlations may
be verified, rather than being simply proposed and maintained as a
function of consensus or expert opinion. In one embodiment,
cascades originated by criteria for divergence provided by an
expert, which untimely lead to extinguishment without intervention
may be automatically adapted to either change the sensitivity for
the detection of the divergent beta or to change the cascade
resulting for the divergent binary. In another example, cascades
originated by criteria provided by an expert which continue self
propagate and expand despite timely action and without progression
of the physiologic divergence may be automatically adapted to
either change the sensitivity for the detection of the divergent
beta or to change the cascade resulting for the divergent binary.
The sensitively and specificity may be further enhanced because the
system may be applied to archived training data sets wherein the
outcomes are known so the magnitude and direction of the cascades
may be compared to the desired magnitude and direction of the
cascades and adjusted accordingly. With applied archived datasets
the application of auto-adaptive adjustment in event criteria,
divergence criteria, or cascade generation may be applied until the
cascades proceed without premature auto extinguishment and
excessive propagation. Furthermore the system may be applied to
hypotheticals on the missing data to allow determination as to how
they might affect incomplete (null) binaries.
[0114] According to one embodiment the processors, including the
time series objectification processor 336, the relational binary
processor 348 and failure imaging processor 360, may output the
results of their analysis into the MPPC Database 368. The MPPC
Database 368 contains the time series 328 on which the analysis was
performed as well as the results of analysis including the event
streams 340, the relational pairs 352, the aggregate failures 364
as well as aggregations, relationships and alternative images of
these elements. In one embodiment, the metadata rule-sets (both
primary and alternative and/or temporarily overridden or altered
elements) are persisted as XML (Event Definition Set 332, Binary
Definition Set 344, Failure image component Definition Set 356) in
the Patient Safety Image Database 368.
Time Series Objectification Processor
[0115] A time series objectification processor 336 may contain
instructions as provided in U.S. patent application Ser. Nos.
11/280,559, and 11/351,449 the specifications of which are
incorporated by reference herein in their entirety for all
purposes. Accordingly, such processors may function by constructing
a time series of each parameter derived during the process of the
hospitalization and then objectify each time series. These time
series may, for example, include objective measured values, drug
dosing, infusion rates, and subjective clinical scores to name a
few. At least some of the time series may be provided as a step
function. For example, time series of the weights, serum sodium
values, SPO.sub.2, respiratory rate, heart rate, drug infusion
dose, sedation score, pain score, stupor score, working diagnoses,
an instability score, a severity of illness score, to name a few,
may all be included. From these time series, the time series
objectification processor may render an aggregate "object cylinder"
or time series matrix for example, which may include parallel
streams of objects derived from all of these time series.
[0116] In an embodiment, a time series objectification processor
converts a set of time series into a stream of sequential and
overlapping discreet elements or objects such that substantially
the entire time series of data is converted to a time series of
objects in a relational hierarchy of ascending complexity. The
objects into which the time series is converted may be predefined
by the user and/or adaptively defined. The discrete objects which
are created represent and characterize an occurrence providing a
time location and a set of properties derived from the aggregated
data within the boundary defined. This process when applied to a
plurality of parallel time series generates an Objectified
Time-series Matrix (OTM). Objects may be very simple such as a
brief rise or fall along a single time series, or highly complex
such as a sepsis cascade object comprised of and inheriting
hundreds of simpler objects of relational physiologic variation,
treatment, and response to treatment to name a few across a large
OTM. These objects along the OTM are differentiated by location and
the properties derived and therefore individual objects can be
qualified and the objects of the OTM can be searched against. The
conversion of the time series matrix to form an OTM provides for
identification, qualification and search ability of relationships
between substantially all patterns and relationships which is
embodied in the data of the EMR. Objectification is therefore one
means of converting an electronic medical records into a particular
format for imaging or searching for images. The objectification
processor may for example be programmed as a continuous search
engine to continuously search for predetermined complex objects
which at this level of complexity comprise images (such as the
image of an evolving sepsis cascades) along both the vertical and
horizontal dimension across multiple parallel time series of the
OTM). When the electronic medical records (EMR) of a patient is
converted into an OTM, the continuous search engine may linked to
an alarm processor to thereby provide an automatic alert upon
detection of specified images (such as the image of a sepsis
cascade, the image of failed or missed treatment, or the image of a
drug reaction to name a few). This converts the EMR into a real
time image generator with real-time detection of both complex and
simple failures. The wide range of simple and complex relational
patterns or images which are provided in an inheritance hierarchy
of ascending complexity and are continuously searchable are derived
of for example physiologic process, pathophysiologic failure, and
the care of the patient, to name a few, are all exposed for
continuous or intermittent searching or imaging along the OTM
[0117] As discussed, one embodiment includes a patient safety
processing system, which includes a time series objectification
processor 336 and a relational binary processor 348. The relational
binary processor 348 may be embedded into, or communicate with the
time series objectification processor 336. The time series
objectification processor 336 is programmed to convert parallel
time series of the electronic medical records from a central source
or a wide range of sources as well as from other processors (e.g.,
the Patient safety processor), into parallel object streams. The
relational binary processor 348 then processes the object streams
to generate streams and cascades of relational binaries. According
to one embodiment, the processor 304 automatically outputs a
unified timeline, for example, derived of detected failure image
components of a given type. According to another embodiment the
processor, upon detecting a failure cascade, may present and
highlight the evolving MPPC in real time on an outputted display of
a failure image component diagram for the physician to review.
According to another embodiment, the processor 304 persists failure
image components and all other results of the analysis into the
MPPC Image Database 368, which may be the source for visualization,
reporting, and interfaces into other systems. The portion of the
motion picture which has already been completed may be reviewed
backward and forward to review in a single summary snap shot
view.
[0118] As discussed, according to one embodiment, the relational
binary processor 348 generates relational binaries. Such relational
binaries include an alpha event object and a beta event object. An
early step in this process includes the defining the relational
binaries by the user or by the processor. To define a relational
binary, first, the alpha event is defined (as by the user or
adaptively). The alpha event is defined both in terms of its
channel and the object along the channel. In one embodiment, the
objects along each channel are defined by characteristics (such as
the slope, amplitude, or other features defining the object as
discussed in the aforementioned patent applications).
Alternatively, threshold violations may be identified as an alpha
event. A beta event is defined, again in terms of its channel and
its characteristics and may be either a pattern or a threshold
event. Both alpha and beta events may also be defined in terms of
the relationship of its characteristics to the characteristics of
other events, such as those, which preceded the specific event. In
one embodiment the user may define the relational objects, (as by
using a drag and drop designer), by selecting the channel (which
defines the time series type), and by selecting the event objects
(for example a fall event or a rise event) which meet specified
range of criteria, and by identifying the timed relationship (such
as the time interval) of the beta event in relation to at least a
portion of the preceding alpha event, and/or by identifying the
spatial relationships and/or frequency relationships of one event
to the other event. In one embodiment, alpha objects and beta
objects are defined by the criteria provided to the time series
objectification processor 336 alone for the detection of event
objects such that the Relational Binary Processor may be concerned
only with the detecting the presence and timing of detected event
objects not with modifying or affecting the criteria for event
detection if desired. (The detection of event objects by a time
series objectification processor 336 may be as disclosed in U.S.
patent application Ser. No. 11/280,559 and U.S. Pat. No. 7,081,095,
the specifications which are incorporated by reference in their
entirety herein for all purposes.) This is not to limit the
functionality of the relational binary processor 348 (since
processing systems, which incorporate the programming of the
relational binary processor 348 to specify criteria, are included
in an embodiment,) to detect objects as a function of basic time
series patterns by the objectification processes where these basic
patterns are converted to discrete objects. The relational binary
processor 348 then aggregates the relational binaries according to
their time of occurrence and/or to specific criteria for
aggregation set by the user or processor to derive image components
and the image components are aggregated according to their time of
occurrence and/or to specific criteria for aggregation set by the
user or processor to derive the MPPC and care derived of events and
patterns across hundreds of parallel time series. In a sense, the
relational binaries and events become the discrete "pixels" from
which MPPC of a patient's physiologic system are constructed by the
processor 304.
[0119] According to one embodiment, the processor 304 or is also
programmed to organize the events and relational binaries into
larger aggregate factorable objects, which may also be constructed
as a unified object timeline rather than a motion picture. Each
aggregate factorable object includes a specific aggregation of
events and relational binaries objects. In some aggregate
factorable objects, the individual relational binary and event
objects occur in a specific sequence or range of sequences (which
may be overlapping) and the objects have a specific temporal
relationship (or range of temporal relationships) with respect to
each other. One specific type of object timeline may be specified
as simply a grouped set. In another example, relational binaries
are ordered in specified sequence in which the event and relational
binaries objects were detected thereby defining the object
timeline.
[0120] According to one embodiment objects of specified types may
also be combined derived to render a "unified patient timeline"
which is a simple summary of the patient's physiologic system and
care. The MPPC and care provides the information at more
comprehensive level. Both may be configured to provide further
simplified summarization or image detail revealing drill down. The
unified patient timeline may for example, represents an instance of
at least one factorable aggregate object derived from a plurality
of parallel time series into a single time-series or time line,
often of relational binary objects of a specific type or plurality
of types. In one instance the unified patient timeline and/or the
MPPC and care is constructed to be a life long time line and/or
motion picture, which preferably is recorded whenever signals are
available, such as during a hospitalization or when connected to a
home monitor or when blood testing is made. The beginning of the
motion picture or time line is defined by the time of the earliest
date of data (which may be derived from archived patient data) the
unified patient timeline does not end until a patient dies.
Segments of the timeline (or motion picture)may be separated for
examination by location of the patient such as a hospitalization
segment, or by actions taken to treat the patient, such as a
peri-operative segment, or by events relating to altered patients
states such as the segment immediately preceding death or while
sleeping. According to one embodiment, an object nomenclature is
provided which designates the timed and sequence relationships of
the binary objects and events of a plurality the parallel patient
related time series, thereby converting a large plurality of
datasets into this single time series of factorable objects, which
is readily outputted interpretable through application of a
succinct nomenclature.
[0121] In one embodiment the physician may mark a test result or
other data point as mistaken or anomalous. In this case the
processor splits the analysis into two--the working analysis (which
removes or alters the test result or other data point) and a
background analysis (which maintains the original data). The
processor may run scenarios in which the original test result stays
in effect to determine if conditions occur that might have been
expected from the "so-called" anomalous test. The background will
not affect the working analysis but notification may be generated
if a correlation of events is found in a sufficiently suggestive
pattern to warrant a consideration that the original test results
may not have been mistaken and, in fact, would account for
conditions that do not fit the current working state (e.g., the
state with the test results removed). Background analyses may be
deleted according to time (e.g., after a certain amount of time in
which no correlation to following events is found) or at the
request of the user or system operator (e.g., to reduce resource
utilization).
[0122] In another example the processor may be programmed to
generate more frequent testing binaries to confirm or exclude an
apparently evolving image. In this way the processor is trying to
look as far forward as possible with additional testing to confirm
the motion picture of a particular failure as early as possible so
that the delay associated with waiting for the detection of a
failure cascade as by various traditional threshold breaches is
eliminated.
[0123] In an example, as part of assuring that the future image is
complete, the testing binaries are designated such that the
addition of certain drugs (the alpha event) into the image, may
cause automatic orders for testing to monitor for complications
related to the drug (the beta event) if selected, events, binaries,
image components, or MPPC are present. In an example, if the
physician orders heparin, a testing binary is generated and added
to the image which includes automatic order for a platelet count
every 48 hours. According to one embodiment, the time series
objectification processor 336 is objectifying the time series of
platelet counts to detect a least one fall event (as for example
defined by a negative slope and/or a magnitude of fall and/or a
threshold fall), if a declining slope is detected a divergent
binary is generated and a marker indicating a fall is added to the
image along the platelet count time series, the processor may
generate more frequent platelet testing binaries, to confirm the
presence of these divergent binaries in the image. If multiple
divergent binaries are detected then the processor may generate
different types of testing binaries wherein the alpha event is the
fall in platelet count. This may trigger a cascade of testing
binaries such as, for example, wherein the alpha event is a binary
that includes a heparin treatment and a fall in platelet count t
and the beta event is, for example, a platelet factor 4 assay or/or
another assay.
[0124] In this way, using the failure imaging processor 360, the
delay associated with waiting for an absolute or relative threshold
drop in the platelet count is reduced. In addition the cascade may
include additional testing binaries (as for hepatic function tests,
to determine the safety of Argatoban, a medication which may be
ordered if the image components are consistent with heparin induced
thrombocytopenia. Here the advantage of having these binaries and
image components as part of a MPPC is evident, because the
processor will be examining the images of the motion picture for
other causes of the fall in platelet count which may include
cascades indicating TTP as will be discussed and/or occult
hemorrhage.
[0125] One embodiment programmatically images the parallel
physiologic time-series to render a relational pyramid of data with
the top of the pyramid representing data at the highest level of
analysis and abstraction while data moves down through layers of
analysis, the bottom layer being the raw data streams. The
healthcare worker may investigate the pyramid in the following ways
to name a few: [0126] 1) Drilldown--the care worker may navigate
into the details of the data and the rationale of the analysis
(i.e., both the conditions that exist and the rules by which the
analysis has arrived at its conclusion) [0127] 2)
Aspects--viewports into the system which emphasize certain
elements/conditions and de-emphasize (and/or filter out) other
elements/conditions)
[0128] These two examples above may be used together allowing the
healthcare worker to navigate through the relational pyramid
vertically (drilldown through levels of analysis) and horizontally
(through filters/aspects).
[0129] In one embodiment the relational pyramid may be manipulated
by the healthcare worker and/or researcher to consider hypothetical
scenarios or scenarios based on the rejection of certain test
results or events which may be considered in error, anomalous or
otherwise inaccurate. Alternate pyramids may be stored in whole or
as differential images. Alternate pyramids may be compared against
the working pyramid to understand the results of the altered
data.
[0130] In one embodiment, the processor 304 will automatically
consider alternate pyramids under certain conditions--such as the
existence of perturbation for which no precursors may be
identified. The sudden existence of perturbation or of divergence
may, by considering the range of possible precursors, suggest
anomalous conditions: inaccurate diagnosis, faulty monitoring
equipment, labeling mistakes, the failure of a patient to take
medication as prescribed, to name a few.
[0131] According to one aspect, the values and/or patterns of the
blood tests such as the inflammatory mediators is/are compared to
the image(s) of physiologic perturbation or to the pattern(s) or
values of at least one physiologic parameter, such as the pulse
rate, respiration rate, and/or ventilation oximetry index to name a
few. Upon the detection of an apparent relationship, the processor
may automatically order a sufficing number of sequential blood
tests to confirm that the pattern of the parameter is convergent
with the pattern of the blood test thereby providing strong
supporting evidence, reinforcing redundant evidence, that the
physiologic parameter and the mediator have a common physiologic
failure based linkage, such as the failure of sepsis for example.
One embodiment extends that analysis to incorporate specialized
inflammatory mediators into the moving picture of failure so that
comprehensive comparison of the marker or indicator to the image of
the physiologic parameters and treatment is provided.
[0132] FIG. 4 shows a UML Static Diagram of one embodiment of the
relational binary processor 348, which defines relational binaries
to thereby organize the complexity of the electronic medical record
for the timely detection of failure image components. According to
this embodiment a relational binary is defined by first detecting
an alpha event object, which is defined in terms of its channel
(e.g., oximetry) and its characteristics (e.g. slope, magnitude,
duration.). Then the companion (relational) beta event object is
defined, again in terms of its channel, (e.g., pulse or oximetry)
and its characteristics (e.g. slope, magnitude, duration) including
the spatial and/or temporal relationships to the alpha event. In
other words, the beta event may also be a specified as function of
the magnitude, slope, timing, or other relationships of the alpha
event. Alternatively or in combination the beta event may be
identified as a being between two values each of which may be a
function of the magnitude of the alpha event.
[0133] The actual relationship between the alpha and beta events,
which comprise the object binaries, is not defined by cause and
effect (which may not be known with complete certainty) but is
rather defined by the pattern relationship such as a temporal,
spatial, and/or frequency relationship of the events, or simply by
their prior designation as a relational pair. For example, the
actual relationship between the alpha and beta events comprising a
given relational binary could be a cause and effect, two effects
resulting from an unmonitored cause, a relationship between two
monitoring technologies measuring the same physiological
phenomenon, an expected compensatory response, or a pathologic
response, to name a few. One object is to identify the pattern
relationship of aggregate objects that include a plurality of
relational binaries so that the actual relationships may be
defined.
[0134] An alpha event is defined as a perturbation, which is
defined in terms of its channel (e.g., oximetry) and its
characteristics (e.g., a slope, magnitude, duration, and/or
threshold breach). A beta event may be defined as an expected
response event, defined again in terms of its channel (e.g., pulse
or oximetry) and like the alpha event, in terms of its
characteristics (e.g., slope, magnitude, duration). In addition,
the beta event may also be defined by the spatial and/or temporal
relationships to the alpha event or a component or portion of the
alpha event. For example, when the beta event is an expected
response event, the beta event may be specified as function of the
magnitude, slope, timing, or other relationships of the alpha
event. Alternatively, in another example, the expected response
event may be identified as a being between two values each of which
are a function of the magnitude of the perturbation event. The
alpha event and/or the beta may, for example, be a perturbation
event, treatment event, or a diagnostic designation event to name a
few.
[0135] According to one embodiment, there are three basic
relational binary types; the convergent relational binary, the
divergent relational binary and a null relational binary. (Although
others relational binaries may be provided). A convergent binary is
an alpha event combined with an expected beta event response. If
the channel of the expected response is present and uncorrupted,
but the expected response is not found, then a missing event
(comprising, for example, a wave segment or test result of the
region of the expected response) is specified and the relational
binary that includes the alpha event and the missing beta event is
called a divergent binary. If the channel of the expected response
is not present or the wave segment or test result is corrupted in
the region of the expected response then the relational binary that
includes the alpha event and the untested beta event is called a
null binary.
[0136] An event may be the alpha event of a first relational binary
and the beta event of second relational binary (provided the alpha
event and beta events are each along different parallel channels).
With some physiologic processes, relational binaries cycle or
repeat with a certain pattern and this produces a special case of
relational binary clusters or patterns.
[0137] In one-embodiment event characteristics may be defined in
terms of modifiers defined by patient conditional values such as
anthropomorphic values, age, sex, or preexisting disease, such that
the presence of these modifiers (as for example provided by a rule
system in combination with the event definition menu) causes a
change in the event definition parameters and/or threshold
values.
[0138] In one embodiment the events and/or the relational binaries
(convergences and divergences) are aggregated to construct a global
factorable object to derive a factorable objectified timeline. The
factorable objectified timeline may be rendered graphically or
provided by a nomenclature for example, which identifies the events
and the time from the onset of the closest preceding event to the
onset of the following event.
[0139] According to an embodiment, relational binary objects or a
specific aggregation or pattern of relational binary objects may be
pre-designated by the user to define a failure image component. The
processor may then automatically and timely identify the occurrence
of the failure image component by searching the event streams,
divergence binary streams, and convergent binary streams, which are
stored for each patient. In the alterative or in combination, all
such streams or a portion of specific streams or a grouping of
streams filtered for severity of divergence (for example) may be
aggregated and rendered for periodic viewing wherein, for example,
the temporal relationships of for example divergent binaries or of
the occurring failure image components are easily recognized or
specifically indicated.
[0140] FIG. 4 shows a convergence analysis static Model according
to one embodiment including UML Static Diagram of the classes (and
relationships) which the Relational binary processor uses during
the processing, analyzing and synthesizing of, in this case,
electronic medical record input streams. Objects created from these
classes represent the identified perturbations as well as attempted
identifications, which failed due to the absence of data streams.
User-interfaces, reporting systems, business intelligence and data
warehousing sub-systems, notification mechanisms, alarms and other
human or software application interfaces access this analysis
structure to aggregate, further analyze, store and/or react to the
results of analysis.
[0141] In the depicted embodiment, the case 404, channel 408 and
time series 412 classes represent the data streams from which the
analysis may be derived. These classes may be defined as disclosed
in U.S. Pat. Nos. 6,609,016 and 7,081,095 and U.S. patent
application Ser. Nos. 11/431686, 11/351449, and 11/148325 the
specifications of each of which are incorporated by reference for
all purposes in their entirety. For each case 404, one or more case
analyses 416 may be constructed. A case analysis 416 is the result
of a case 404 being submitted to the relational binary processor
348 with a specified binary definition set. A single case 404 may
be analyzed with multiple binary definition sets resulting in one
case analysis 416 per binary definition set applied.
[0142] A case analysis 416 is primarily composed of the relational
binaries identified during processing. In one embodiment, the
relational binaries are one of three types--convergent binary 440,
divergent binary 456 and null binary 428. The case analysis 416
contains a collection of each of these pairs and may have zero of
more pairs in each of those collections. As discussed, relational
binaries are composed of relational events 444. The structure of
the relational binary (i.e., the type of events which compose the
relational binary) is defined by its type and the classification is
provided to fix the structure of these relationships. In one
embodiment, all relational binaries contain an alpha event, which
is a true event (e.g., represents the identification of a pattern
or a threshold violation, see FIG. 5). In an embodiment, the type
of beta event identified makes the distinction between object
binary types. For example, a convergent binary 440 represent a
relational pair of events wherein the beta event has an expected
relationship to the alpha event as described in the binary
definition set. A relational binary may have either a true event
444 or a missing event 460 as a beta depending on what has been
specified as the expected condition. If a true event 444 was
specified in the relational binary definition then the associated
convergent binary 440 may have a true event 444 as a beta event. If
a missing event 460 was specified then the associated convergent
binary 440 may have a missing event 460 as a beta event. The class
structure therefore allows for zero or one event 444 and zero or
one missing event 460. In a presently preferred embodiment a
convergent binary 440 may not contain two beta events.
[0143] Divergent binaries 456 represent a relational pair of events
identified in a relationship that contradicts the expected
relationship as described in the binary definition set. Therefore a
divergent binary 456 may have either a true event 444 or a missing
event 460 as a beta depending on what has been specified as the
expected condition. If a true event 444 was specified in the binary
definition then the associated divergent binary 456 may have a
missing event 460 as a beta event. If a missing event 460 was
specified then the associated divergent binary 456 may have a true
event 444 as a beta event. The class structure therefore allows for
zero or one event 444 and zero or one missing event 460. According
to one embodiment, a divergent binary 456 may not contain two beta
events.
[0144] Null binaries 428 represent the existence of a condition in
which an alpha event was identified but the data stream from which
the expected beta event is to be derived is unavailable to the
relational binary processor 348. Events 444 may be isolated (e.g.,
not part of any identified relational pair) or part of one or more
binary. The channel event stream 424 provides an aggregation of
events 444 ordered by time and separated by channel 408. A true
event 444 is a wave segment 448 (e.g. inherits wave segment) while
a missing event 460 is associated with a wave segment 448 that
represents the section of the channel 408 that was searched for the
event described as expected in the binary definition set. Null
events 432 are not associated with wave segments 448 because the
channel 408 to which they would have been attached or the relevant
section of that channel 408 is unavailable or corrupted. The
relational binary processor 348 will convert null binaries 428 to
convergent 440 or divergent 456 binaries as channels 408 of data
become available.
[0145] The analysis contains aggregations of binaries, which repeat
(e.g., cycling reciprocations) in three aggregation classes:
repeating convergence 436, repeating divergence 452 and repeating
null 420. To further clarify this structure it may be useful to
describe the order of operation within an exemplary embodiment of
the relational binary processor as it constructs the analysis
according to one embodiment. [0146] 1. Each channel 408 in turn is
iterated through and named events 492 and threshold violations 484
(Events which may be identified without reference to relational
pairs) are identified and placed into channel event streams 424
[0147] 2. The channel streams 424 are iterated through to match any
identified events 444 with candidate alpha events (as defined in
the specified binary definition set). A single event 444 may match
any number of alpha event definitions and each one is considered a
candidate alpha event. [0148] a. For each candidate alpha event,
the specified search region is examined for the expected beta event
[0149] i. If the channel 408 in which the expected beta event is
unavailable or corrupted [0150] 1. A null binary 428 is created
(along with its associated null event 432) [0151] 2. The conditions
are examined to determine whether a Repeating Null 420 should be
created or appended to [0152] ii. If the expected condition is
found [0153] 1. A convergent binary 440 is created [0154] 2. If a
relational event 488 was identified in the process it is created
and added to the channel event stream 424 [0155] 3. The conditions
are examined to determine whether a repeating convergence 436
should be created or appended to [0156] iii. If the expected
condition is not found [0157] 1. A divergent binary 456 is created
[0158] 2. If a relational event 488 was identified in the process
it is created and added to the channel event stream 424 [0159] 3.
The conditions are examined to determine whether a repeating
divergence 452 should be created or appended to [0160] 3. Failure
image components and aggregate failure image components are
identified (See Below)
[0161] FIG. 5 shows an event type static model. According to an
embodiment, events may be represented as one of three types:
threshold violation 484, named event 492, and relational event 488.
Threshold violations 484 represent the existence of a breach of
some specified, calculated or derived limit within an associated
channel 408. Named events 492 and relational events 488 represent
an identified unipolar pattern within a channel 408. Named events
492 differ from relational events 488 in that the parameters with
which the pattern is identified is not a function of elements of an
associated event (e.g. a limiting event 496). Limiting events 496
within the context of a convergent binary 440 are the alpha event
of the related relational binary.
[0162] A limiting event 496 may be either a threshold violation 484
or a named event 492, but, in one embodiment, not a relational
event 488. In an embodiment limiting events 496 may be relational
events 488 and the relational binary processor employs recursive
algorithms to determine a comprehensive set of events. Threshold
violations 484 and named events 492 may be isolated events (e.g.
identified independent of a relational binary). Alpha events of a
relational binary may be either a threshold violation 484 or a
named event 492 but, in this embodiment, not a relational event
488. In an embodiment this rule is relaxed to provide the ability
to produce a relational cascade. In an embodiment, alpha events may
be relational events and the relational binary processor may employ
recursive algorithms to determine a comprehensive set of
events.
[0163] FIG. 6 shows an aggregate failure image component static
model, which provides further clarification to the presently
preferred embodiment of the patient safety processor. The aggregate
failure image is one type image which will be searched for. After
the relational binaries are identified, the relational binary
processor 348 may aggregate these identified pairs into aggregate
failure image component objects, which represent the identification
of patterns of events and binaries. The aggregate failure image
components 524 are created with respect to a failure image
component definition set. A failure image component definition set
is associated with a single binary definition set, but multiple
failure image component definition sets may be created for a binary
definition set.
[0164] An aggregate failure image component 524 has two collections
of failure image component elements 528. The first is a set of
failure image component elements 528 that was identified in a
specific sequence. The second represents failure image component
elements 528 that simply fell within the specified search window
(e.g., existence, not sequence is sufficient for aggregation).
Aggregatable 532 is one embodiment of a lightweight interface,
which allows the analysis objects (536, 540, 544, 548, 552, 556,
560, 564, 568) to participate in the aggregation. The analysis
objects include convergent binaries 536, divergent binaries 540,
null binaries 544, repeating nulls 548, repeating convergences 552,
repeating divergences 556, events 560, missing events 564 and null
events 568 may all participate in an aggregate failure image
component 528.
[0165] FIG. 7 shows a binary definition set static model. The
binary definition set model represents the objects that are part of
the binary definition sets used by the relational binary processor
348 to create the convergence analysis. A binary definition 590
represents the parameters used to identify a relational binary. A
binary definition 590 is made up of four key elements--the binary
type 606, the search window definition 618 and the definitions of
the alpha 630 and expected beta events 594.
[0166] FIG. 8 shows an embodiment of a convergence editor, which
provides the ability for the creation, and modification of a binary
definition set, which may be used by the relational binary
processor to create the convergence analysis. A binary definition
set may be represented as a convergence model--a visual
representation of the object instances shown in FIG. 7. The user
interface includes a design surface 764 and an element toolbox 700,
which allows for the drag-and-drop creation and manipulation of a
subset of the convergence model called a binary diagram. The
aggregation of all binary diagrams created with a single name
constitutes the entire convergence model and may be persisted as a
binary definition set in the relational database or in an XML file
to name a few. Breaking a convergence model into binary diagrams
allows for multiple views into the model. These views are not
mutually exclusive (i.e., the same binary definition may be
represented in multiple diagrams) and therefore provide views into
model at various levels of complexity and points of reference.
[0167] The box on the left is the convergence element toolbox 700
which presents the visual elements which may be added to the design
surface and therefore to the binary diagram. The shapes represent
events that may be added. The three event types available
correspond with the event definition classes in FIG. 7: named event
598, relational event 602 and threshold violation 622. The
relationships 768 section of the toolbox 700 presents a set of
lines, which may be used to connect two events to create a
relational binary. The line chosen determines the binary types 606,
binary types include: expected 716, analogous binary 720, possible
cycling 724, verify non-existence 728, reoccurring verification
732. The visual icon attached to the line may cue the user to its
type. The binary type 606 determines the type and frequency of
search that may occur when the candidate alpha event is identified.
For example, the reoccurring verification type 732 may generate
multiple binaries for a single candidate alpha event because it
directs the relational binary processor to search for the expected
event with a specified frequency, generating binaries at each
interval. Some binary types may be used in combination (e.g.,
reoccurring verification 732 and verify non-existence 724). Each
relationship added to the design surface 764 must have at least one
time interval provided (e.g., 768) which represents the search
window definition 618 for the binary definition 590. Each
relationship may be directional. The line includes an arrow
end-style on the end that represents the beta definition 626. The
end without an arrow represents the alpha definition 630.
[0168] Each pair of events, which has a connecting relationship,
represents a single binary definition 590. In the above figure, the
following seven binaries: [0169] 1. An analogous binary between
nasal pressure fall and oxygen fall (736, 772, 740) [0170] 2. A
possible cycling binary between oxygen fall and oxygen rise (740,
773, 748) [0171] 3. An expected relationship between oxygen floor
breech threshold violation and oxygen rise (744, 768, 748) [0172]
4. An expected relationship between oxygen rise and oxygen fall
(748, 770, 740) [0173] 5. An analogous binary between oxygen rise
and nasal pressure rise (748, 774, 752) [0174] 6. A verify
non-existence binary between oxygen fall and pulse rise (740, 771,
756) [0175] 7. A verify non-existence binary between oxygen fall
and pulse fall (740, 769, 760)
[0176] This diagram does not represent all of the relationships of
each of these events. It is an example of a subset view into the
overall convergence model with a focus on sleep apnea.
Relationships and elements may be removed from this diagram without
removing them from the entire model (i.e., the editor distinguishes
between "Remove" which removes the element from the diagram but not
the model and "Delete" which removes the element from the diagram
and the model [including all other diagrams]). A diagram may be
constructed that shows all of the events and relationships, but it
would likely be so large and complex as to be unreadable.
[0177] The editor will check the diagram for validity before
persistence or at the user's request. For example, a relationship
without a beta event would invalidate a diagram. An invalid diagram
may invalidate the convergence model. It is preferred that a
convergence model cannot be persisted into a binary definition set.
The editor allows for an invalid state to provide flexibility
during diagram construction. Further, if the target binary
definition set is associated with failure image component
definition sets that are available to the editor, the editor may
warn of conflicts with associated models by changes to the diagram.
Depending on editor settings, these changes are disallowed, or the
changes may be propagated into the failure image component.
[0178] Each diagram element may be manipulated in a more detailed
way through property editors associated with the element type. The
property editors provide access to all editable properties of the
associated definition objects such that the editor is sufficient to
construct a complete binary definition set. The editor provides for
adding text, notes, lines and other visual elements to the diagram
to increase human readability and to communicate between users.
These additional visual elements have no affect on the binary
definition set.
[0179] This structure may be understood within the context of the
user interface modeled in FIG. 7 that may be used to visually
construct the binary definition set. Specifically, FIG. 7 depicts a
binary diagram within the convergence editor which pertains to the
monitoring of sleep apnea. Each pair of events (e.g., 744, 748),
which has a connecting relationship (e.g., 754), represents a
single binary definition 590. The connecting line between the two
events represents the binary type 606. Binary types may include:
expected 716, analogous binary 720, possible cycling 724, verify
non-existence 728, and reoccurring verification 732. The binary
type 606 determines the type and frequency of search that may occur
when the candidate alpha event is identified. For example, the
reoccurring verification 732 type may generate multiple relational
binaries for a single candidate alpha event because it directs the
relational binary processor to search for the expected event with a
specified frequency, generating relational binaries at each
interval. In an embodiment, some binary types may be used in
combination (e.g., reoccurring verification 732 and verify
non-existence 728). The box containing a pair of time offsets 768
represents the search window definition 618. This definition
contains the start and end time offsets from the end point of the
alpha event for which the beta event should be searched in the
target beta channel. Finally the shapes represent the alpha and
beta event definitions. These definitions provide the parameters
with which the relational binary processor may search the
identified wave segment for the existence of a unipolar pattern
(i.e., meeting the criteria defined by named event definition 598
or relational event definition 602 or threshold violation (i.e.,
meeting the criteria defined by threshold violation definition
622).
[0180] FIG. 9 shows a failure image component definition static
model. The failure image component model represents the classes
that are part of the failure image component definition sets used
by the failure image processor to identify and create aggregate
failure image components. A failure image component definition
represents the set of element definitions and their relationships,
which allow the failure image processor to determine whether the
pattern of elements meets the criteria of the specified failure
image component. FIG. 10 shows an embodiment of the aggregate
failure image component editor that provides the ability for the
creation and modification of a failure image component definition
set, which will be used by the failure image processor, in
coordination with a binary definition set, to create a convergence
analysis. A failure image component definition set may be
represented as a failure image component--a visual representation
of the object instances shown in FIG. 9. The user interface
includes a design surface 832 and an element toolbox 780, which
allows for the drag-and-drop creation and manipulation of a subset
of the failure image component called a failure image component
diagram. The aggregation of all failure image component diagrams
created with a single name constitutes the entire failure image
component and may be persisted as a failure image component
definition set in the relational database or in an XML file to name
a few. As with the convergence model, failure diagrams are views
into the model that provide visualizations at various levels of
complexity and points of reference.
[0181] A failure image component definition set is associated with,
and dependent upon, a specified binary definition set. A failure
image component definition set, and therefore a failure image
component and all its corresponding diagrams, cannot be created
without the specification of a binary definition set. Further the
specified binary definition set provides and limits the events and
binaries that may be used to create the failure image component
diagrams.
[0182] This structure may be understood within the context of the
user interface in FIG. 10. Each diagram represents a single failure
image component definition 650. In this embodiment, a failure image
component element definition 662 may either be a binary definition
674 or an event definition 678 (but in one embodiment, may not be
both). These failure image component element definitions 662
represent the existence of a specific event or relational binary.
If a specific sequence of elements is defined to identify the
failure image component then the sequence is specified with
connectors and time offsets (e.g., 812, 824, 816, 828,and 820).
Each shape container (a shape that contains other shapes)
represents a failure image component element definition 662. A
failure image component element definition 662 includes both a
binary definition 674 and a binary mode. The binary mode 666
indicates the type of binary that must be created by the binary
definition 674 within the analysis (e.g., convergence, divergence
or null). Within FIG. 10, the mode is specified by selecting the
binary container (e.g., 784, 788, and 792) from the toolbox 780. An
isolated shape without internal shapes represents an event failure
image component element 678. An event failure image component
element 678 includes both an event definition 678 and an event mode
670. The event mode 670 indicates the type of event that must be
created by the event definition 678 within the analysis (e.g.,
event, missing event or null event).
[0183] Failure image component element toolbox 780 in FIG. 10
presents the visual elements that may be added to the design
surface 832 and therefore to the failure image component diagram.
The large bold-lined container shapes (784, 788, and 792) represent
failure image component elements that refer to a binary while the
smaller shapes (796, 800, 804) represent failure image component
elements that refer to events (isolated or part of a binary). The
three binary element types available correspond with the available
binary modes 666: Convergence, divergence and null. Each binary
dropped on the surface may subsequently lead to the selection of a
binary definition 674 from the associated binary definition set.
The design surface is split into two sections--sequenced and
non-sequenced. Elements in the sequenced area correspond to the
sequenced mode aggregation 654 in FIG. 9. These elements involve a
relationship in time and therefore a relationship may be specified
between them (e.g., 824). The relationships section 836 of the
toolbox presents a set of lines, which may be used to connect two
failure image component elements (either binaries or events) as
part of the overall aggregate. Each relationship added to the
design surface must have a time interval provided (e.g., 828) which
represents the search window definition associated within the
sequenced mode aggregation 654. Each relationship is directional
indicating precedence in the sequence 654.
[0184] Zero or more sequences may be specified, but if an element
is placed in the sequenced section it is defined as part of a
sequence. Elements placed in the non-sequenced section cannot have
relationships. Only existence is specified within the overall
time-frame specified for the failure image component. The failure
image component diagram differs from the binary diagram in that the
diagram itself represents an entity--the failure image component
definition 650--and is not simply a collection of other entities
(e.g., binaries in the case of the binary editor). Removing
elements changes the definition of when a failure image component
will be identified. All elements added to the failure image
component diagram represent an "and" relationship for
identification purposes (i.e., all elements and sequences must
exist for the failure image component to be identified). In one
embodiment, to create "Or" scenarios, multiple failure image
component diagrams are created with variation representing the "or"
combinations. The editor may check the diagram for validity before
persistence or at the user's request. The editor allows for an
invalid state to provide flexibility during diagram
construction.
[0185] Each diagram element may be manipulated in a more detailed
way through property editors associated with the element type. The
property editors provide access to all editable properties of the
associated definition objects such that the editor is sufficient to
construct a complete failure image component definition set. The
editor provides for adding text, notes, lines and other visual
elements to the diagram to increase human readability and to
communicate between users. These additional visual elopements have
no affect on the failure image component definition set.
[0186] FIG. 11 provides an example of a binary diagram referring to
heparin therapy in which the following binary definitions are
specified: [0187] 1. A reoccurring verification binary 854 between
heparin therapy 850 and ptt rise to therapeutic range 858. [0188]
2. A verify non-existence binary 866 between heparin therapy 850
and pulse rise 862. [0189] 3. A verify non-existence binary 882
between heparin therapy 850and blood pressure fall 870. [0190] 4. A
verify non-existence binary 886 between heparin therapy 850 and
hemoglobin fall 874. [0191] 5. A verify non-existence binary 890
between heparin therapy 850 and platelet count fall 878.
[0192] FIG. 12 provides an additional example of a binary diagram
referring to insulin therapy in which the following binary
definitions are specified: [0193] 1. An expected binary 922 between
insulin therapy 920 and blood sugar fall 924 to therapeutic range.
[0194] 2. A verify non-existence binary 926 between insulin therapy
920 and blood sugar breech 930. [0195] 3. A verify non-existence
binary 926 between insulin therapy 920 and confusion 928.
[0196] FIG. 13 provides an additional example of a binary diagram
referring to narcotic therapy in which the following binary
definitions are specified: [0197] 1. A reoccurring verification
binary 944 between narcotic therapy 940 and pain score fall to
therapeutic range (948) [0198] 2. A verify non-existence binary 952
between narcotic therapy 940 and oxygen fall 956. [0199] 3. A
verify non-existence binary 960 between narcotic therapy 940 and
blood pressure fall 961. [0200] 4. A verify non-existence binary
962 between narcotic therapy 940 and respiratory rate fall 964.
[0201] 5. A verify non-existence binary 966 between narcotic
therapy 940 and confusion 967.
[0202] FIG. 14 provides an additional example of the failure image
component editor in which three non-sequenced binaries (970, 971,
972) are defined as sufficient to identify possible heparin-induced
hemorrhage.
[0203] FIG. 15A shows a failure image frame 973 of a patient's
physiologic system and care and demonstrates one exemplary image
according to one embodiment as generated by the failure image
processor. The image shown is indicative of dynamic progression
from an image suggestive of stability to an image suggestive of a
failure cascade of septic shock. This is the one of image for which
the patient safety processor is deployed as a "search engine for
pathophysiologic cascades" may continuously search when deployed
into use with a patient. The image displays objectified events that
met criteria as up and down arrows indicating whether they are rise
events or fall events respectively. Minor time series variations
(such as detected minor rises or falls typical of signal noise,
which fail to meet criteria by the objectification processor as
events) are represented on each time-line as open circles along
parallel time lines. (The visualization of such variations may be
turned on or off as desired). The detected events are combined with
other events to form binaries which are then combined to produce an
image of relational patterns that include aggregate binaries and
individual events defining the dynamic state of the patient's
physiological system and of the medical care applied to the
physiologic system during the time interval of each respective
image. Within the complete image, smaller failure images aggregate
to produce the larger image of aggregate failure (in this case, of
septic shock). In real time this is a motion picture image which
may be shown with this rendering or with an alternative rendering,
such as an actual digital motion picture of the patient within
these parameters reanimated in the MPPC.
[0204] Since FIG. 15A is a late "time lapsed" frame of a MPPC that
has exhibited many earlier frames, the patient safety processor
output provided that confidence that the cascade image detected
search engine is septic shock was high. Representations of rise
events or fall events are depicted as up-arrowheads and
down-arrowheads respectively on each time line 974. The timelines
974 are grouped into categories 975. The first event detected
within the time interval of the image is a perturbation event--a
rise event of the neutrophil count 976 shown by the upward pointing
arrowhead on the neutrophil timeline. This perturbation event is
combined by the relational processor to a second perturbation
event--a rise in respiratory rate 977 also shown by an upward
arrowhead, to generate the first relational binary 978 (combined in
the figure by the arrow connecting 976 and 977). Each subsequent
perturbation in the image is designated by its timeline and
arrowhead. An arrowhead with a circle around it designates
perturbations determined by testing automatically ordered by the
patient safety processor in response to the detection of a
particular image. In an example the rise event in inflammatory
mediators or indicators 979 was ordered by the patient safety
processor to better define the inflammation portion of the image
which was somewhat obscured because the early images demonstrated a
rise in neutrophil count, a rise in pulse, and a rise in
respiration rate but with a normal temperature. Since this
ambiguous image must be better defined to decide care, testing for
inflammatory mediators/indicators is automatically ordered by the
processor to better complete the image.
[0205] Using these basic designations the image of FIG. 15A reveals
a clear image frame (a time lapsed snap shot) detected by searching
of an MPPC that includes perturbations of inflammation, followed by
a hemodynamic perturbations, followed closely by respiratory
perturbations, and then renal perturbations in an expanding and
linked cascade 980. The initial rise in Neutrophil count 976, the
first detected perturbation event, will have completely disappeared
later in the cascade such that frames late in a failure process are
best viewed with the sufficient scale to observe the onset of the
cascade 980. The image shows a complete lack of any events along
the temperature timeline 981. In the absence of the analysis
provided by the processor 304, the lack of a fever may mislead a
healthcare worker, who may think of fever as a reliable indicator
for the early detection of sepsis. However, the processor 304 is
programmed to recognize that it has rendered or found an incomplete
image and then seeks to complete the image by ordering testing for
inflammatory mediator 979. This testing serves as a "surrogate
image components" for a rise in temperature thereby establishing
that the entire failure image does in fact exhibit an early
component of inflammation.
[0206] Two drug treatments are evident in the image, the
antibiotics vancomycin 982, designated by its dose on the time
line, and levofloxacin 983, similarly designated. Also a rise in IV
fluids in the form of normal saline 984 is indicated. All of these
treatments come late after the image has long been indicative of a
high probability of sepsis. (This delay, which may be detected in
real-time by the patient safety processor, suggests poor and
ineffective care, which has ignored or otherwise been poorly
responsive to the patient safety processor. The processor may be
programmed to provide an indication of the quality of the care
provided. Time lines, which include the care worker or ward may be
provided so that delays may be linked to particular locations or
care workers).
[0207] The image of the progressive cascade 980 shows the drug
treatments components 982, 983 of the image are too late because
they appear within the image very late along the cascade 980. The
late portions of the image of the cascade 980 also include a very
ominous beta comprising a rise in anion gap 985. The addition of
this new image component provides a mature image of cascade 980,
which is now strongly indicative of a highly fatal stage of septic
shock. Other image views may be for example; specific expanded
portions of the time lines, specific expanded views of aggregate
failure components along the timeline portions, specific groupings
of the timelines, overviews of perturbation progression from group
to group (an example of this is shown in FIG. 19), to name a
few.
[0208] FIG. 15B is the failure image frame of FIG. 15A with
portions of the image being separated into sequential states of
inflammation 986, systemic inflammatory response syndrome 987,
presumptive severe sepsis 988, presumptive severe septic shock
989.
[0209] FIG. 15C is an early failure image frame from real time
imaging of the process in FIG. 15A that demonstrates that there may
be little in these first perturbations to warn of the impending
cascade towards sepsis. The first "spark", a rise in neutrophil
count 990, evident in this image is entirely non-specific despite
the fact that it, in retrospect, heralds the onset of septic shock,
completely disappears by the time this motion picture has reached
the point illustrated in FIG. 15D (see below) in which focused
testing, more frequent CBC testing, and/or more frequent vital sign
measurement to determine the significance of this rise in
Neutrophil count may be suggested or ordered by the processor 304
to expand the image to more quickly move toward a more specific
image.
[0210] FIG. 15D is a failure image frame from real time imaging of
the process in FIG. 15A. This frame demonstrates early image
components of inflammatory, hemodynamic, and respiratory
augmentation 991 combined with early immune failure 992. As
indicated by the image, serious sepsis is highly likely if
treatment does not occur by the time this frame of the MPPC has
passed.
[0211] FIG. 15E is a failure image frame from real time imaging of
the process in FIG. 15A. This frame demonstrates demonstrate the
image components of inflammatory, hemodynamic, and respiratory
augmentation 991, with immune failure 992, but now with image
components indicative of a decline in respiratory gas exchange 993
and fall in platelet count 994. As indicated by the image, serious
sepsis is even more likely than or the stage shown in FIG. 15D if
treatment does not occur by the time these frames of the MPPC have
passed.
[0212] FIG. 15F is a failure image frame of FIG. 15A to demonstrate
that the image now shows expansion the failure cascade from the
frame in FIG. 15E to now include the image components of metabolic
failure 995, renal failure 996, hemodynamic failure 997 and
respiratory failure 998. This is the point wherein medical
intervention for sepsis begins in many patients monitored by
today's electronic medical record and monitoring systems. The
introduction of treatment at this point of the movie is often
entirely ineffective. The introduction of fluid resuscitation 999
at this late frame of the image will likely have little effect on
progression of the patient.
[0213] FIG. 16 shows a time lapsed failure image frame of the
failure cascade of congestive heart failure. Note the first
perturbation event detected by the processor is hemodynamic (a rise
event in pulse rate 100), rather than inflammatory as in FIG. 15A.
The next detected perturbation event is respiratory, a rise in
respiratory rate 102 which combined with the rise in pulse 100
produces the first relational binary 104. There is a fall in the
ventilation indexed oximetry value 106 producing a second
relational binary 108 with the rise in respiratory rate 102. The
rise in respiration rate 102 is the beta event of the first
relational binary 104 and the alpha event of the second relational
binary 108. Together these two joined relational binaries form an
image component 110, which may be followed back to the initial
onset of the image of the nascent cascade 112. Treatments including
furosemide 114 and metoprolol 116 are initiated fairly close to the
onset of the image of the nascent cascade 112 but are not effective
in preventing subsequent occurrence of an image of a progressive
cascade 118. This image of a progressive cascade 118 is defined by
the both the components and length of the MPPC. The processor 304
upon detection of this failure image may search for the fundamental
cause of the cascade progression by automatically ordering cardiac
enzyme (not shown) and other tests if the safety committee of the
hospital desires these types of proactive measures. Note the
cascade 118 includes the development of atrial fibrillation 120 and
subsequent further deterioration.
[0214] FIG. 17 shows a failure image frame of sleep apnea. The
first perturbation events occur in a group that includes cycling
events of pulse rate 122, respiratory rate 124, SPO.sub.2 126, and
pulse upstroke 128. These occur after the initiation of a narcotic
dose of 3 mg IV 130. The aggregated image components showing
cycling 132 then repeats to produce second such image components g
133 and third such image components 134. The SPO.sub.2 cycle 135
portion of the third image components showing cycling 134 becomes
more severe with recovery failure 136. CPAP treatment 137 is given
timely and no further narcotic is given. In this case, there is no
image of an expanding cascade or progressively declining
respiratory rate or declining SPO.sub.2 to indicate
life-threatening narcotic induced sustained hypoventilation. On
later review, as in morning report or with teaching rounds, the
entire MPPC, which contain this frame, may be reviewed by moving
along a fast framed image to better visualize the subtleties of the
progression furthermore the physician or nursing group may drill
down to see that actual time series (as, for example, by right
clicking on the SPO.sub.2 cycling symbol 137). The decision as to
whether or not the treatment in this case rendered timely care may
be assessed. In an example, the physicians in the session may
petition the patient safety committee to adjust the patient safety
processor to provide a recommendation for earlier automatic RT
department notification, along with the nurse notification when
images such as those defined in the early portion of this motion
picture are present. In this way the Patient Safety Processor
becomes an integral part of the continuous quality improvement
actions of the hospital system with the goal being to move
treatment and testing leftward into the earliest frame which
provides sufficient image support for the treatment or testing. The
goal is to a continuing move toward earlier treatment of the source
of the early perturbations before the cascade develops. According
to one aspect, the processor 304 is integrated into the continuous
quality improvement process and the processor 304 becomes an
integral part of a hospital's quality improvement committee
meetings and a major source of hospital-wide as well as focused
analysis and a mechanism to rapidly institutionalize quality
improvement focused change.
[0215] FIG. 18 shows a failure image frame indicative of a high
confidence of thrombotic thrombocytopenic purpura (TTP) a rare
thrombotic and inflammatory condition which mimics the image of
septic shock. TTP may be caused by the inhibition of ADAMTS enzyme
by autoantibodies but this disease may also be rarely triggered by
the very common drug clopidogrel. TTP often occurs within 2 weeks
of drug initiation and may result in serious adverse events if not
detected. Unfortunately, TTP shares many of systemic response
features of the very common disorder of sepsis (FIG. 15A) which
also causes thrombocytopenia. Since sepsis is a much more common
condition, misdiagnosis of sepsis in the presence of TTP is a high
possibility; furthermore, as with most pathophysiologic failures,
both processes may coexist in a single patient along with other
related conditions such as systemic lupus erythematosis and
pancreatitis. Despite the fact that the moving images of failure in
TTP and sepsis are similar, misdiagnosis of sepsis in the presence
of TTP may be serious, since TTP cannot be expected to respond to
antibiotic treatment and misdiagnosis of TTP s in the presence of
sepsis may also be serious, since sepsis cannot be expected to
respond to plasmaphoresis without antibiotics.
[0216] TTP is associated with the accumulation of large multimers
of Von Willebrand factor, which damages red blood cells and induces
extensive micro vessel thrombosis, resulting in confusion, renal
failure and microangiopathic anemia, which is associated with
sentinel schizocytes that may be detected in the peripheral smear
of blood (if the diagnosis is suspected and the test is ordered).
Thrombocytopenia, renal failure, and hematuria may appear earlier
in this process than with sepsis but these early findings are only
an image clue and do not differentiate two MPPCs.
[0217] The MPPC suggestive of TTP is generated by the processor
304, with the processor 304 indicating a failure image consistent
with the possibility of sepsis and/or TTP and other less likely
conditions such as an acute vasculidity. The processor 304 may
output non-specific characterizations of the image such as "image
consistent with a life threatening acute or sub-acute thrombotic
and inflammatory augmentation" and may present a differential
diagnosis of the processes that may generate such an image.
[0218] Also, as for example upon the detection of a threshold frame
or frames, the processor 304 may automatically order the peripheral
smear, blood cultures, urine cultures, sputum cultures, chest
x-ray, ANA, pancreatic enzymes, renal sediment, and ANCA study to
enlarge and fill in the gaps of the image as rapidly as possible.
It is the hospital experts who will ultimately decide the
cost-effective balance of ordering these tests as defined by the
position the tests are ordered along the cascade. If desired, the
reports form the chest x-ray may include a section that will appear
as a time series (as for example, a step function). The radiologist
in the interpretation may enter an indication of pulmonary
infiltrate, pulmonary edema, and the like and may indicate worse or
better which may result in a step change from the last test. In
this manner, the results of studies such as chest x-rays become a
source for time series rendering and incorporation into the failure
imaging process.
[0219] The presence of an image that includes image components
defining a failure cascade 139 that includes
inflammatory--hemodynamic respiratory--augmentation (IHRA) 140 with
an early fall in platelet count 142, a fall in the ventilation
oximetry index (VIO) 144, a fall or threshold value of hemoglobin
146, an rise or threshold value of a confusion score 148, and/or a
rise or threshold value of red blood cells in the urine 150, and/or
a rise or threshold value of creatinine 152. Together the
combination of image components produces a MPPC suggestive of the
possibility of TTP and/or sepsis and/or other less common
processes. For example, if the patient had just received blood it
would suggest a possible transfusion reaction.
[0220] The processor 304 may indicate to the healthcare worker the
gravity of the image, a differential diagnosis as suggested by the
image, the general type and/or physiologic description of failure
cascade present, as well as a notification that the detection by
the patient safety processor of this type of image may lead to
prompt notification of the attending physician and transfer to ICU.
If the image has insufficient binaries because results are not
available to define enough beta components to define the presence
of the failure image suggestive of TTP with a sufficient confidence
level to take action, the unavailable tests are ordered upon the
detection of the partial image in an attempt to complete the image.
Note in FIG. 18, the detection of the image components suggestive
of the possible presence of a complete MPPC of TTP triggered the
test for schizocytes 152 in an attempt to complete the TTP image.
The detection of a threshold value step function, and/or rise in
schizocytes combined with the rest of the image triggers the
warning of the potential presence of TTP. FIG. 18 reflects
suboptimal care in a retrospective case that was detected by the
processor because the plasmaphoresis 154 order was carried out too
late. Such delays may be detected in reviews of medical history
data, and the processor may be configured to provide an automatic
report of variance to the quality improvement department of the
hospital.
[0221] In certain embodiments, human delay in physically following
the orders of the patient safety processor may be addressed by
building escalating alarms into the processor 304. The time in
carrying out the order is determined by the processor 304, and the
processor 304 may be programmed to up-indicate the warning upon
increasing delay. To prevent this delay, the processor 304 may be
programmed notify another station if action is not taken in
response to detection of various evolving failure images such as
the one in FIG. 18. These may be decided, for example, by the
hospital quality improvement committees or by individual physicians
or nurses if desired so that the patient safety processor improves
over time and may be adjusted to compensate for the diligence of
the healthcare worker. The patient may receive Levofloxacin early
to cover the possibility of sepsis as the image was also consistent
with sepsis and the healthcare workers decided to empirically treat
for sepsis (albeit with somewhat limited antibiotic coverage).
However, the cascade proceeds despite antibiotic therapy. Since a
cascade is an image component and the relationship of the cascade,
its growth, and its features and its timing within an MPPC in
relation to the dose, timing, and type of treatment also forms part
of the MPPC, these relationships may be automatically assessed by
the processor in real-time to determine if treatment is effective.
The hospital safety committee or infectious disease committee may
decide whether or not to reprogram the patient safety processor to
make antibiotic suggestions based on various ranges of failure
images before the results of cultures are known.
[0222] FIG. 19 shows an overview image of perturbation onset and
progression as derived from the time lapsed MPPC of FIG. 15A
wherein the perturbations in each grouping are incorporated into an
aggregate index along a single smoothed time series for each group.
Note this is a typical progression of sepsis with initial
involvement of the inflammatory group 160 then each other group is
involved in progression. Note the late timing of the treatment 162
is particularly evident in this summary view derived form the more
complex images.
[0223] Rather than, or in combination with, an index, if desired
the processor 304 may be programmed to provide an indication of the
severity and number of the aggregate perturbations in each group.
These may be for example designated by many enlarging or colored
arrows, other icons, and/or timed instability scores, to name a
few. Many such options may be included so that the user may define
his or her preference to visualize the sequence and patterns of
cascade progression across groups.
[0224] A range of expert and pattern recognition systems may be
applied to analyze the images and the image components generated by
the failure image processor. These comprise the image
identification processor. In one embodiment the image
identification processor works with the failure image editor, which
allows the user to select the images for detection using for
example from a drag and drop interface. In an embodiment the drag
and drop interface provides for the discretionary selection of, for
example, the time-series type to be selected, then the events and
binaries are selected on each time-series type in order and the
ranges of relative positions and orders of the events and binaries
is selected. In this example, the failure image editor allows
customization of the desired ranges for the components of the
images (and therefore the ranges of the images themselves) to be
selected as well as the response of the image identification
processor to the detection of a given image and/or images. The
failure image editor may allow for selecting the ranges of timing
and order of the events and binaries to generate a specific output
such as a proposed diagnosis, warning, order for more testing or
imitation or termination of treatment. The image identification
processor may also be adaptive such that a physician inputs the
diagnosis present, such as for example septic shock, with a given
image. The physician may also capture a given image or set of
images into the failure image editor to then select ranges about
the events and binaries within the image which also would have
indicated the presence of septic shock so that the adaptive image
processor may learn more quickly.
[0225] FIGS. 15A-F, 16, 17, 18 and 20 represent a 2 dimensional
"time lapsed" snapshot view four MPPC after they have proceeded to
advance states. This view also provides an alternate user interface
for the creation and editing of the Failure Image Definition Set.
Researchers may use a failure image editor to create and manipulate
failure models.
[0226] In one embodiment researchers work from the top down to
define failure images. Researchers begin by selecting a set of
channels in which they want to "paint" the failure image. FIG. 20
depicts the failure image editor being used to "paint" the
narcotic-induced ventilation instability failure image. Channels
(100, 102, 104, 106, 108) may be ordered in any number of ways, by
sorting, categorizing or by simple drag-and-drop selection of
location within the failure image editor. Channels may be
duplicated (e.g., 100, 102, 104, 106) to expand the image so that
the relationships may be defined in a non-overlapping way for
complex definitions that define multiple relationships. The failure
image editor maintains the relationships within and between defined
elements within the channels regardless of their vertical location
within the editor. Researchers then select a channel and the
failure image editor presents a set of events and binaries that are
available which apply to the given channel. Researchers may select
any of these elements and drop them on channel. Also, the
researcher may create a new element (event or binary) at any point
within a channel (for example using a right-click menu editor).
Locations within the editor indicate relative locations in time
between selected and/or created elements. If a binary is dropped
upon a location, the failure image editor determines whether the
beta or the alpha event belongs on the channel selected and places
the event within the channel and the corresponding event (beta or
alpha) on the channel indicated by the relational binary
definition. If the channel is not currently in the failure image
editor then it is added. Relational binaries that collapse down to
a single icon (e.g., cycling within a single channel) will show the
single icon 110, 112 rather than the alpha and binary events. The
location of the corresponding event is determined as the midpoint
of the search window definition. The entire window is shown as a
set of parenthesis 116 indicating the range of the search window
relative to the corresponding event, in this case a treatment event
with an IV narcotic 114. Search windows are shown only within the
beta channel of the relational binary and the event itself is show
within the midpoint of the search window. If an event is both a
beta and an alpha event the search window displayed is around the
event is specific to the event when it is participating as a beta
event. Search windows may be suppressed within the editor and/or
shown only within the relational binary currently selected due to
the fact that a single event may be the beta of any number of
binaries. Individual events may be dropped onto a channel or
created on a channel. New event types may be defined within the
failure image editor. Events may be connected with a drag-and-drop
selection or with an alpha and beta click selection, for example to
define new relational binary types.
[0227] The entire image or sections of the image may then be
persisted as an aggregate failure mode. The failure image editor
works in concert with the aggregate failure mode editor to create
and modify failure image definition sets. Furthermore, the
aggregate failure mode editor works in concert with both the
convergence editor and the event editor to create and modify the
binary and event definition sets. In FIG. 20, the definition of
aggregate failure modes is accomplished with a split-screen view
showing the failure image editor in the top pane 118 while the
aggregate failure mode editor is in the lower pane 120 showing an
alternative type of failure mode diagram. These two models are
completely synchronized with changes in one immediately reflecting
the change in the other.
[0228] In one embodiment researchers work from the bottom up to
define failures from a set of time series. Researchers may begin
with a set of actual time series from patients diagnosed with known
failures, with a set of time series generated by the processor to
simulate certain conditions or a set of time series simulating no
perturbation at all within a patient. This set of time series may
be designated as immutable (for example with the set of actual time
series) or may be edited to provide a sample of the patterns being
defined. Researchers may select portions of the time series which
the failure image editor then will analyze to provide candidate
event definitions. Alternatively the researcher may select
parameters to define an event and the time series displayed will
indicate the results of that definition overlaid on top of the time
series to provide visual guidance to the researcher. Once the
researcher completes the definition of an event the failure image
editor will compare that definition with other definitions within
the same channel. If similar patterns are found the researcher is
alerted and allowed to create a new event type or select one of the
event types already selected. If the event is a relational event,
the researcher may select a corresponding event from which
relational parameters may be defined and experimented with or the
researcher may simply define a function (e.g., >2.times.relative
magnitude). Once an event has been fully defined then the
researcher may choose to relate the event to another event within
the image or to a search window within the image (e.g., to indicate
a missing or null event). The researcher may indicate that a
processor-ordered event as the beta of a relational binary. Groups
of events and relational binaries may then be selected to define an
aggregate failure mode.
[0229] In one embodiment, the failure image editor may be presented
with a large collection of time series sets provided with the
indication of the presence or absence of a particular known failure
image. The failure image editor creates a set of candidate
definition sets refining them to create the right specificity and
sensitively to match the sample set. Once the best-fit definition
sets are created, a second large collection of times series sets
are provided with the indication of the presence or absence of a
particular know failure image. The failure image editor first uses
the candidate definition set, determining sensitivity and
specificity, and then refines the definition set to be better
suited if possible to both the first and the second collection of
sample data. This process may be executed iteratively until a
best-fit set of definition sets is created or the process is deemed
not to be asymptotic and is abandoned.
[0230] In one embodiment the failure image may be "played" or
executed by the image editor as a MPPC to provide further
time-specific markers. A default execution of a failure image is
"played" by placing all events as specified in their default (e.g.,
midpoint) location within their respective search windows as
defined by the image definition. A sample result of this is
displayed in FIG. 15A. Once the image is played vertical markers
are placed within the timeline as in FIG. 15A to indicate
progressive states within an evolving image. In this way, the image
definition may be provided the specifications by which the image
state may be identified and displayed within the patient safety
monitor.
[0231] In an alternate and/or complimentary embodiment, the image
editor provides the ability to split the execution of an image into
multiple intermediate and/or end states. Each different branch
within the failure image definition may be defined as a state
within a failure image or a different, albeit related, failure
image. Trees of related images may be composed to provide
alternative evolutions of failure within the failure image
definition.
[0232] FIG. 21 is a frame from a time lapsed motion failure image
that includes a plurality of timelines from the patient illustrated
in the failure mode diagram of FIG. 1. In this image, a patient who
has experienced a stroke has developed a condition associated with
serum inappropriate antidiuretic hormone (SIADH), which induces a
induced fall in serum sodium and confusion. The patient presented
with an acute stroke but was recovering and alert. Then he slowly
began to develop confusion and less alertness. As the stroke was
large, the nurses and physicians managing the case thought that the
patient's confusion and obtundation was due to brain swelling. The
patient SPO.sub.2 and ventilation rate were normal, he had no signs
of sepsis and because of recently normal electrolytes, the
attending physicians did not think that a metabolic cause for the
confusion was a reasonable option. In other words they misdiagnosed
the pathophysiologic failure pathway (illustrated on the failure
mode diagram 200 of FIG. 1) and they thought the pathophysiologic
pathway was following the direct connecting line 170 between stroke
208 and confusion 220 as shown in the failure mode diagram 200 in
FIG. 1. However, prior to the onset of the confusion the patient
was receiving 0.5 NS in spite of the fact that that he was eating
and drinking. Repeat serum sodium confirmed a fall in sodium and
SIADH was confirmed with additional testing. Cautious correction of
his sodium resulted in rapid recovery and resolution of the
confusion and obtundation.
[0233] Since the stroke caused the SIADH (which cased the fall in
serum sodium), the actual modes of failure were significantly
different than suspected by the hospitalist in this case. Referring
again to FIG. 1, the actual failure followed from the stroke 208 to
the hyponatremia 242 and then followed from the hyponatremia 242 to
the confusion 220. In this case the patient survived the missed
diagnosis but he experienced several extra days unnecessary days in
the hospital because of delay in detection and treatment of this
failure.
[0234] FIG. 21 shows an image frame 2100 of a failure image editor
for constructing a range of MPPC for recognition by the processor
304 for the patient described in FIG. 1. In this case the failure
image shown is consistent with presumptive severe sepsis. The
inflammatory/hemodynamic/respiratory augmentation 2110 is followed
in the image by a fall in VIO 2114 and metabolic failure with a
rise in anion gap 2116. Note that if the
inflammatory/hemodynamic/respiratory augmentation 2110 is
unassociated with a rise in temperature 2118 (a null binary 2120 is
identified), inflammatory mediator markers 2123 are ordered to
confirm the presence of the inflammatory component of the failure
image. The typical sequence of binaries is shown but these events
may occur in any order. The processor 304 may provide greater
confidence if the order is as shown and lesser confidence if the
order is different that shown. As noted failure images may overlap
such that patient with preexisting hemodynamic instability may
become septic, for this reason, in this case the order is not
deemed pivotal. However, for some failure images the order of
events may provide much greater specificity (in which case the
parentheses may be adjusted accordingly. At first the failure image
editor may be set to be more liberal and then adjusted as hospital
experience and quality improvement may dictate.
[0235] Now referring to FIG. 22, this exemplary image is derived
from a patient with the failure mode diagram of FIG. 1 having a
timeline for a stroke, diabetes, atrial fibrillation, a history of
congestive heart failure, and sleep apnea (in this case
superimposed sepsis is not present). These correspond to the
failure mode diagram of FIG. 1 illustrating potential relationships
between stroke 208, diabetes 202, atrial fibrillation 206,
congestive heart failure 204, and sleep apnea 210. Note that
patient safety processor is ordering routine confusion scores 185
because of the timeline indicating a stroke. The detection of an
increase in confusion 185 or the presence of hypotonic saline
administration 186 to a patient with a stroke timeline
automatically triggers a measure of electrolytes 187 and upon the
detection of a low serum sodium 188 the processor orders a urine
osmolarity 189 and indicates a high probability of SIADH 190 and
recommends an adjustment in fluid therapy 191. Here the problem is
simple but the early signs of failure were at first subtle at a
time when intervention would have prevented the increased length of
stay later the pathways of failure were confused leading to further
delay and considerable family since they were told the incorrect
diagnosis. In this case the nurses and physicians may have been
busy or may have been inexperienced or simply not familiar with the
subtle decline in mutation which may attend the development of
SIADH in a stroke patient. The reason subtle findings are missed is
myriad. Note also, as illustrated in the failure mode diagram of
FIG. 1, this is simply one failure and there are very many
potential failures for this complex patient. Furthermore, in this
case the serum sodium was nearly normal when the low sodium was
finally detected so many physicians may not think the level was
sufficiently low to cause these symptoms or warrant intervention.
However, the sodium had dropped from a high normal to just below
normal and in patient with brain edema the magnitude of the fall in
serum sodium may be more significant than the absolute value and
this variation in vulnerability from patient to patient and within
the same patient depending on coexisting disorders, diseases, and
medications are not concepts which are easily grasped by some
healthcare workers who have observed patients with very low sodium
without any change in mentation. This illustrates the value of
generating and recognizing a moving picture of the failure and
care. The patient safety processor does not trigger an alarm or
define a diagnosis by a single threshold breach, because the system
analyzes the entire failure and care image over time and is
programmed to recognize that this image indicates vulnerability to
a fall in serum sodium, even a fall that does not go below
threshold. The patient safety processor provides the advantage of
continued vigilance and continuous consideration of all of the
potential physiologic failures which are consistent with the
images. According to one aspect, failure mode diagrams, such as the
one in FIG. 1, may be used to construct prospective or
retrospective failure images as in FIG. 22 by applying the
cascading binary relationships between diseases, treatments, and
perturbations to construct failure images and image ranges using
the failure mode editor.
[0236] The processor 304, as applied to the disclosed embodiments,
is not constrained by the exemplary definitions provided herein,
but may rather compare actual data to a plurality of MPPC images
(stored or real-time) and image states to find best-fit matches. In
one embodiment, the best-fit matches may be determined by image
registration techniques. In embodiments, the matches may be made by
image similarity measures that include cross-correlation, mutual
information, sum of squared intensity differences, and ratio image
uniformity. The processor 304 may indicate all possible images and
image states ranked by level of confidence. For example the
processor 304 may indicate that a MPPC is consistent the systemic
inflammatory response syndrome with a high degree of confidence and
early septic shock with a medium degree of confidence and that TTP
(and other potential alternatives) or overlapping failure modes are
remotely possible in view of the image and remain to be excluded.
The physician may be asked if it is desired to order the focused
testing to exclude these remote alternatives or overlaps and/or the
processor may be programmed to automatically add this testing based
on a specific range of images (as defined, for example, using the
drag-and-drop editor discussed previously).
[0237] The identification of failure within the processor 304 is
not the single selection of a failure mode or a failure state, but
the ranking of a set of images with regard to their fit within the
data presented. The identification of multiple failure images is
not simply the selection of alternatives. Multiple failures may, in
fact, exist and be interacting with each other. Early states of
some failure images may be very similar, or in fact exactly the
same, as the early stages of other failure images or of a
combination of failure images. The processor 304 provides the
analysis and visualizations that may allow the health worker to
understand the current state of the patient (and patient
environment) in terms of possible future states--alternatives and
candidate overlaps--along with confidence levels as to their
specification. Further, the processor 304 allows the health care
worker to query the patient's condition with regard to confidence
levels and, in particular, the comparative confidence level between
two images and/or image states. For example, the confidence level
for sepsis is low with the frame shown in FIG. 15B, whereas it is
intermediate for fame in FIG. 15C and high for FIGS. 15D-15F. These
confidence levels, along with the action desired, may be programmed
into the patient safety processor in advance by specialty groups,
hospital safety committees, and/or may be customized and "tuned" by
individual physicians and or may be applied adaptively by the
processor by comparing the entered new diagnosis with the present
image and recoding that image as indication of that state. In the
adaptive mode, the processor may be programmed to ask "is this
failure image indicative of a failure process defined by this newly
entered diagnosis and, if so, please specify the first event,
binary or image component which in retrospect was part of this
specific failure process".
[0238] In one embodiment, the processor 304 may be trained by a
pathophysiological engine (such as a human simulator, as is known
in the art) for the creation of failure and response images. Given
a specified event definition set and binary definition set, the
patent safety processor provides a dynamic image derived from the
input of the pathophysiologic engine and the processor is
instructed as to the nature of the images so that when these images
are detected in the future they are recognized. In one embodiment,
a human simulator is connected to the patient safety processor to
provide an improved teaching tool for healthcare workers.
Researchers may select to be presented with a normal, unperturbed
patient with various conditions. Once a dynamic image of the
patient is displayed researchers may introduce perturbation into
the pathophysiological engine which will result in new dynamic
images from the processor 304. For example, a research may select
relationships presented according to a convergence and toggle them
to divergence. Also, random divergence may be configured into the
system. Divergence with respect to a single or a set of response
system(s) may be specified to model the breakdown of systemic
response. Divergence may be configure globally or for a specific
timeframe indicating that systemic response fails, or is delayed.
In this way, both perturbation and failure of systemic response may
be selectively introduced to create failure images. These failure
images may be persisted to be further edited within the failure
image editor. The researcher may select several different
variations and save them as failures and/or failure states. These
failures and/or failure states may be persisted within a failure
component definition set to be used by the failure image processor.
Further, resultant failure images may be compared with actual
patient data to refine event and binary definition sets.
[0239] Alternately or in combination, according to one embodiment,
an MPPC from the processor 304 may be simulated by a processor
driving the human simulator so that healthcare workers may observe
the reanimation of the MPPC of the patient safety processor either
as a digital animation or as a reanimation derived from output of a
human manikin. One utilization of the embodiment that combines the
pathophysiological engine to the processor 304 is to model
treatment protocols. The engine may output expected or unexpected
parameters (divergence) in response to treatment and the image
output of the patient safety processor may be observed, and/or
recorded for protocol modeling. Further, using the ability to
introduce divergence, allows processed protocols or other protocols
to be verified for reasonable redundancy to cover failures of
systemic response.
[0240] This aggregation of data, analysis and metadata provide the
source of data for the patient safety visualization processor 372.
In one embodiment, the patient safety visualization process 372
provides a visualization of a patient's condition in a
comprehensive grouping defined by rows of timelines of specific
signals and/or grouping and/or categories of signals and/or
signals. In one embodiment the global state of each row is
represented by color in a spectrum with a different color for each
of: sustained stability, stability, convergence, perturbation,
divergence, null, failure, cascading failure.
[0241] In another embodiment colored arrows, icons, text, and/or
other visual representations along each time line represent these
states. In one embodiment the patient safety visualization
processor represents the patient condition as a set of pixel
streams moving from left to right to show evolution of condition
over time. The processor provides the navigation backward and
forward in time as well as up and down through levels of analysis
within the patient safety image database 368. In an embodiment the
levels of analysis may be, for example:
[0242] Time Series--Unanalyzed data streams in the form of time
series
[0243] Events and Perturbation--Events and threshold violations
characterized within their respective channels as to whether they
represent clearly defined perturbation according to the event
definition set 332
[0244] Systemic Response--Convergent, divergent and null binaries
representing the relationships between events, threshold
violations, perturbations and expected elements according to the
binary definition set 344
[0245] Failure--aggregate failure objects representing images of
failure that have been identified within a single patient
[0246] System Failure--aggregate failure objects within a specific
category (such as the respiratory system) representing images of
failure that have been identified within a single patient
[0247] Failure Patterns--Trends of failure and failure images
within patient population or a specific region, such as a specific
hospital ward for example.
[0248] In one embodiment the patient safety visualization processor
372 composes an image on computer monitor (the patient safety
console 384), which is composed by a series of pixels oriented
horizontally representing data and analysis streams. These pixel
streams are stacked vertically with the position on the x-axis
representing a specific point in time. The processor provides for
the movement of the pixel streams horizontally to provide a pan
through time. Each pixel stream is composed of a set of pixels,
which indicate the state of the data and/or analysis at the
specified point in time. The pixel has a state (e.g., represented
by color) and granularity (the length of time it represents [for
example 1 minute]). The size of the view as well as the selected
span of time determines the granularity of the pixel. In an
embodiment, the pixel is displayed by the highest level of
instability found within the time span represented by the single
pixel within the pixel stream.
[0249] Further, each pixel has a level of abstraction, which
determines which objects from the patient safety image database 368
contribute to its state. The contributing objects are shown below
by level of analysis: [0250] Time Series--data points within the
channel (e.g., oxygen saturation values) [0251] Events and
Perturbation--Events and threshold violations [0252] Systemic
Response--Relational Binaries [0253] Failure--Aggregate Failure
Objects [0254] Failure Patterns--Failure Trends and
Correlations
[0255] In an embodiment, groups of pixel streams are stacked
vertically to create a patient safety visualization. Patient safety
visualizations may be composed of pixel streams of different
patients or of data and analysis streams within a single patient.
Patient safety images provide the ability of the care worker to
filter the analysis quickly to identify problem areas or areas of a
specific nature. Sorting may be provided highlight emerging failure
cascades or other pattern failures.
[0256] In an embodiment patient safety images may be composed of
different levels of analysis displayed on the patient safety
console 384 at the same time correlated by time. The use of
mixed-analysis level visualizations provides the careworker with
the ability to quickly understand the relationship between the
lower levels of data (e.g., incomplete recovery within oximetry)
and the higher levels of analysis (e.g., the identification of
narcotic-induced ventilation instability).
[0257] In an embodiment the patient safety console 384 provides the
user the ability to trace a failure condition back to the earliest
events associated with the failure to provide a visual display of a
failure cascade. Alternatively, individual events and threshold
violations may be selected to identify which higher-level objects
in which they played a part. In other words, low-level events may
be traced forward to understand their relationship within evolving
patient instability. This tracing, both backward and forward, is
provided by the fact that alpha events of a relational binary are
often the beta event of a preceding relational binary. This chain
of relational binaries provides a powerful tool of analysis. The
patient safety visualization processor provides the ability to
isolate these binary chains showing their origin, evolution and
resolution. In one embodiment, visualizations may be filtered by
the existence and character of binary chains.
[0258] In one embodiment, and if selected by configuration, the
patient safety visualization processor provides the ability to
navigate into the metadata models at any point within the
visualization. Event, convergence and failure image component
diagrams are accessible from objects, which were composed using
specified elements within these diagrams within the event
definition set 332, binary definition set 344 and failure image
component definition set 356. Navigation into the metadata models
provides expert care workers and researchers the ability to further
understand and/or alter the analysis.
[0259] The patient safety console 384 presents a complex set of
data and analysis that meets the immediate need of the busy care
worker. In one embodiment, analysis at the highest levels may be
collapsed into a single pixel stream or group of pixel streams per
patient that provides a simple representation of the evolution of
overall patient safety. Within and from that pixel stream the care
worker may drill down into the most complex displays: multiple
levels of analysis, binary chains and metadata models to name a
few. Alternatively this drill down may be provided by for example
mouse over, touch screen, or may appear automatically when the
processor detects certain adverse patterns or thresholds.
[0260] In one embodiment, the object stream visualization focuses
on the relationships and cascading of the onset of perturbation
within the patient. This is an alternate, and complimentary, view
to the pixel streams described above which focus to a greater
extent on the state of discrete elements within the system at
various levels of analysis. These two visualizations may be used in
parallel and/or provide navigation between them.
[0261] In an embodiment, the object stream visualization represents
events and threshold violations as icons along a time series in
which the icon is placed at the first point in time in which the
event or threshold violation occurred. Icons indicate their
character by color, size and decorations. The basic icon is an
arrow pointing either up or down (as in FIG. 15A). An up arrow
indicates a positive movement, which triggered an event whereas the
down arrow indicates a negative movement. Boolean changes will be
indicated as an up arrow when moving from false to true and a down
arrow when moving from true to false. The thickness and/or color of
the arrow may be used to indicate the extent of that movement.
[0262] Decorations on the arrow may be presented to provide visual
cues as to the nature of the event. A line underneath the head of
the arrow indicates that he event that occurred was a threshold
violation. A circle around the arrow (see 979 of FIG. 15A) may be
used to indicate that the event was the result of a action or test
ordered by the Patient safety processor. Decorations and/or
matching colors and/or flashings may be used to indicate a
relationship warning by the processor, as in the warning of the
potential relationship between the low platelet count and the
medication clopidogrel in FIG. 18.
[0263] In one embodiment, the patient safety visualization
processor 372 will provide automated visual navigation for a
specified period of time and/or specified images. This automated
visual navigation acts as an analysis-driven video playback of the
selected period of time. The healthcare worker selects "Play" and
allows the patient safety visualization processor to move visually
through the evolution of a specified condition. The healthcare
worker may choose navigation movements including "Play", "Pause",
"Fast-Forward", "Rewind", "Skip Forward", "Skip Backward", to name
a few. In an embodiment, during Play mode the patient safety
visualization processor moves at different speeds through the
automated visualization depending on the severity of the condition
being displayed. If the timeseries being displayed have little
perturbation (or little perturbation related to the specified
failure cascade) the processor will move very quickly through time
(i.e., from left to right). When an area of interest, as determined
by the processor, comes into vision the patient safety
visualization processor will slow the movement from left to right.
Further, the patient safety visualization processor will highlight
elements that indicate, clarify and specify the evolution and/or
cascade of failure as well as their relationships with other
elements. The patient safety visualization processor will further
display translucent pop-up panels that provide further textual
and/or visualization elements to describe the current view and
elements within the current view. At any point, the healthcare
worker may "Pause" the automated visual navigation to review the
displayed data and/or drill into what has been displayed.
[0264] In an embodiment, the healthcare worker may select from a
summary view a timespan to review and also indicate sections of the
timespan for which they are interested. The patient safety
visualization processor will slow for the areas selected that are
of interest and will increase the textual and visualization display
appropriately for the highlighted sections.
[0265] In one embodiment the patient safety visualization processor
304 selects the object streams to display and may include or remove
streams as they become important in the video navigation. The
healthcare worker may choose to include additional streams or to
"pin" streams so as to make them always available in the video
navigation. Missing streams are also indicated.
[0266] The patient safety visualization processor 372 may further
indicate to the healthcare worker the time estimated for automated
visual navigation (e.g., "Standard visual navigation estimated at 2
minutes and 37 seconds"). The patient safety visualization
processor may include audio and visual elements corresponding to
and synchronized with the timeseries data along with timeseries
data if video and audio feeds are available. In an embodiment,
healthcare workers may include audio and/or video comments into the
data streams to communicated and collaborate regarding elements
displayed within the patient safety visualization processor. The
patient safety visualization processor may be directed to include
all or a specified subset (e.g., "Include Comments from Doctor X")
of these elements within the automated visual navigation or may be
directed simply to indicate their presence such that the healthcare
worker may invoke them as needed.
[0267] In an embodiment, the patient safety visualization processor
372 may "record" an automated visual navigation session into a
non-interactive video format which may be viewed on standard video
equipment, with streaming technology or in a standard media player
such that automated visual navigation sessions may be shared with
healthcare workers who do not have access to the patient safety
image database or the patient safety visualization processor (e.g.,
as an attachment to an e-mail or accessed from a video-enabled
phone).
[0268] In one embodiment, the processor 304 may use an archive or
database of retrospective and/or theoretical model MPPCs as a
source for determining best-fit image matches or as an ongoing
model to improve such matches. As shown in FIG. 23, one embodiment
is a patient safety processor network 2310 for archiving and
cataloging a database of MPPCs and for developing improved failure
mode recognition, improved protocolization, and improved access of
rural and underserved hospitals to timely failure mode detection
and intervention. As shown, the network 2310 may allow each
hospital 2312, which are each in turn connected to respective
patient safety processors 2314, to be connected to a central image
archive, such as an MPPC archive 2316. The MPPCs from each patient
safety processor 2314 are uploaded to the central MPPC archive 2316
from each hospital. The central MPPC archive is connected to the
database processor 2318, which serves to process the MPPC from the
central MPPC archive 2316 and to improve MPPC recognition and to
develop new failure mode recognition and treatment protocols. MPPCs
from a hospital patient safety processor 2314 that are classified
as associated with an objectively known case, for example one that
is confirmed independently through additional tests (e.g.,
histopathology, genetic testing) or autopsy results, such as a MPPC
suggestive of pulmonary embolism including a positive pulmonary
angiogram, are input to the processor 2318 to build an objectively
defined MPPC database to further build the scope and specificity of
the MPPC of pulmonary embolism. In the alternative, MPPCs that are
classified as associated with an subjective final diagnosis, such
as an MPPC suggestive of SLE induced alveolar hemorrhage, for
example followed by a opinion of a consensus group that this was
the final diagnosis, may be added to the subjectively defined MPPC
database case database to further build the scope and specificity
of the MPPC of SLE induced alveolar hemorrhage. In this manner, a
large database may be derived from MPPC and image components of
MPPC for the worldwide management of disease. International testing
and treatment protocols based on the real-time MPPC detection may
be developed that may potentially set a minimum standard of
detection of catastrophic events even in rural hospitals with a few
beds, in urban hospitals which are poorly staffed, and in
environments wherein physician and nurse experience may be very
low. New protocols may be derived and uploaded to these hospitals
for their discretionary use as analysis of the MPPC results in
response to older protocols or new or additional treatment outside
the protocols reveals potential for improvement. The approach has
the potential to provide improved surveillance of drug reactions
and efficacy after, for example, the introduction of a new drug
into a protocol that may be an experimental protocol. Missing
portions of the MPPC may also be identified to support the
development of new tests which fill in the gaps or perhaps reduce
the number of tests ordered to define cause(s) of the failure. Cost
comparison of different testing and treatment protocols may be
performed.
[0269] The bandwidth of the MPPC may include tests, historic data,
and treatments that become objects in the MPPC. When potentially
clinically significant images of perturbation are identified in an
MPPC, the patient safety processor is programmed to quickly broaden
the bandwidth to investigate the alternative causes. This is
important because the longer the duration an undetected failure
mode the greater the increase in cost and mortality because
complications develop with widen the cascade and make salvage more
expensive and difficult. A narrow bandwidth (fewer tests) is, on
the other hand (without considering the cost of allowing a longer
duration of failure), less expensive than a broader bandwidth. The
"effective bandwidth" includes those components of the bandwidth
that actually contribute to characterize the factors actively
defining the failure image components of the MPPC. Poorly conceived
testing and treatment increases the bandwidth and the medical cost
but may not increase the effective bandwidth. One object of the
patient safety processors 2314 is to increase the effective
bandwidth as rapidly as possible without broadening the bandwidth
inordinately. In an embodiment, a patient safety processor medical
system monitors with a few monitors and tests but uses these as
sentinels, increasing the number of monitors and tests
automatically if a MPPC begins.
[0270] Therefore, it may be advantageous to provide a mechanism to
automatically increase the effective bandwidth of the MPPC at any
time (for example, during low staff times in a rural hospital), to
optimally shorten the duration of failure without the application
of a continuously wide and expensive bandwidth. One mechanism to
broaden bandwidth is with improved testing, such as focused tests
that have a high sensitivity and specificity for a specific failure
mode. The MPPC archive 2316 of the patient safety processor network
2300 may be examined for opportunities to increase the motion
picture bandwidth and achieving a balanced mechanism for mortality
and cost reduction by shortening the duration of failure through
earlier detection and improved treatment response.
[0271] As discussed, according to one embodiment the patient safety
processing network includes a set of local patient safety
processors located at a hospital ward or unit. The local patient
safety processor is under the direction of the healthcare workers
at that location. This allows the local healthcare workers to
control the treatment and testing protocols, and variation of the
testing bandwidth, deployed for the patient under their control.
The local attending physicians individually or as a group as well
as the hospital pharmacists and nurses may prescribe these
protocols though the use of the Local patient safety processors.
The local patient safety processor records the healthcare worker(s)
(for example as a step time series of with an rise event occurring
when the physician, or nurse for example assumes responsibility and
a fall event when he or she is replaced by another. Those caring
for the patient are therefore part of the MPPC. Protocols may be
decided by a group or by an individual physician caring for the
patient. The extent to which a particular healthcare worker or
group is statistically or otherwise associated with favorable or
unfavorable MPPC may be assessed by the processor. The protocol
choices for the local patient safety processors may be made through
the use of pre prepared MPPC protocols as previously discussed.
[0272] The local patient safety processor may recognize the
physician time-series and adjust the protocols and MPPC to match
those selected by this physician. The physician may override the
patient safety processor and if this occurs this override is an
event rendering a new time series until the override is withdrawn.
The extent to which a particular override is statistically or
otherwise associated with favorable or unfavorable MPPC may be
assessed by the hospital patient safety processor, the hospital
group patient safety processor, or the database processor 18. These
may provide modifications in future protocols, and even
incorporation of the modification of the override or even the
prevention of this type of override may be made accordingly.
[0273] The local patient safety processor s communicate with a
hospital wide or hospital patient safety processor which is
preferably under direction of the quality improvement committee and
the hospital experts in each field. The hospital patient safety
processor communicates with all the local patient safety processor
s and may be used to upload treatment/and or testing and/or
bandwidth adjustment protocols and or comparison MPPC, which have
been agreed upon for application hospital-wide to the local patient
safety processors.
[0274] The hospital patient safety processor s of single hospitals
communicate with (and may be controlled by) central organization
patient safety processor. The organization patient safety processor
allows standardization of the hospital protocols through the
Hospital patient safety processor s under its control to set a
minimum safety treatment and testing standards and may be
controlled by a centralized quality assurance group with expert
representatives form all of the hospitals. Since the individuals
caring for the patient represent at least one time series and the
ward represents at least one time series and the hospital
represents at least one time series and the organization represents
at least one time series. The MMPP therefore includes all of these
locations. If the Patient is wearing a monitored GPS unit this may
comprise a location time series which provides continuous real time
location as part of the MMPP. The patient safety processor s will
compare with the entered locations to identity convergence.
[0275] One embodiment demonstrates an example of how a new set of
time series derived from testing devices and provided to the
patient safety processor may be evaluated for cost effectiveness.
In this example, a pulse oximetry reflectance probe is mounted (as
by hat or headband or other fixation device above at least eye to
the patient's head and the probe is wirelessly or otherwise
connected to pulse oximeter and the local patient safety processor
(as by Bluetooth for example). The transmitter may be mounted in
the probe, on the headband or hat or behind the ear in the position
of a hearing aid if desired. A position sensor may also be provided
mounted on the patient. A maneuver such as a change in body
position may be detected and included as an event by the patient
safety processor and a fall a component of the
photoplethysmographic pulse (indicative of the perfusion of the
capillary bed distribution of the supra-orbital artery, a distal
branch of the internal carotid) in relation to a maneuver. In this
way the flow of the capillary bed above the eye becomes a surrogate
marker of other capillary beds supplied from the internal carotids.
Real time perfusion may be compared with that of the ear,
fingertip, or the pulse pressure (as by an invasive arterial line
for example) to identify disparate in perfusion in one or both of
the internal carotid distribution. The local patient safety
processor processes the MPPC with these as additional time-series.
The local patient safety processor uploads the MPPC to the Hospital
patient safety processor, and the hospital patient safety
processor, organizational patient safety processor, and/or database
processor 18 where the MPPCs may be evaluated to determine if after
adjusting for disparities in the MPPCs as a function of
co-morbidities. The MPPCs that include the time series derived from
the supra-orbital plethysmographic pulse may be associated with a
reduction in the number of falls in the hospital. If this is
statistically significant, these time series may be automatically
added (by automatically ordering the intermittent or continuous
supra-orbital monitoring used the study) to increase the testing
and bandwidth when it is detected that the MPPC of a given patient
is similar to those of the study population where the addition of
those processed time-series data had a positive impact on
outcome.
[0276] The database processor 18 is preferably connected to all the
organization patient safety processor s (or hospital patient safety
processor s if the hospital is not under a central organization).
The database processor 18 is preferably controlled by a healthcare
information corporation which maintains the database processor 18
and the network. Each patient safety processor below the database
processor 18 is capable of operating independent of the patient
safety processor network so extensive redundancy, lack of
subordinate dependency, and therefore greater safety against
network failure is built into the patient safety processor
network.
[0277] This patient safety processor network structure allows
diverse minimum standards to be set by each government and allows
the monitoring of the effects of these diverse minimum standards to
determine cost and benefit. The database processor 18 preferably
includes a comparison processor that compares the MPPC and all of
the objects of the MPPC, such as events, binaries, image
components, and cascades, to other MPPCs all of the objects of the
other MPPCs to identify statically differences between the MPPC
which are associated with improved or adverse cost, outcome, length
of stay, morbidity, mortality, resource consumption, and/or
complications. One advantage of the patient safety processor is
that the objects of the MPPC are discrete and are therefore readily
incorporated into statistical software components of the PSCP. The
statistical software components may include a wide array of
statistical software products as are well known in the art for
identifying differences in discrete time related data collections.
The objects also comprise organized collections of an ascending
hierarchy of complexity and the organized collections which may be
compared statistically at each ascending level of complexity to
identify associated differences. In one embodiment the PSCP divides
the MPPCs into groups having a least apportion of substantially the
same image components. For example a grouping may be derived having
substantially the same initial sepsis cascade picture and similar
co morbidities and age and sex but different physicians, hospitals
and/or treatments. Differences in length, progression, compilations
and mortality associated with the cascade may be identified and
statistically compared with the differences in physicians,
hospitals, treatments, testing, and/or treatment timing.
[0278] When a particular testing, treatment, bandwidth variation,
ward location, or hospital location is identified as statistically
associated with improved outcome then the database processor 18 my
offer, as for download, new protocols which incorporate those
identified particulars into the hospital patient safety processors
and/or Organization patient safety processors for their
consideration. New medication or treatments may be assessed in this
way with blinding of the data accommodated by the patient safety
processor such that the time series of the experimental medical is
labeled with an experimental code.
[0279] While the disclosed embodiments may be susceptible to
various modifications and alternative forms, specific embodiments
have been shown by way of example in the drawings and have been
described in detail herein. However, it should be understood that
the disclosure is not intended to be limited to the particular
forms disclosed. Indeed, disclosed embodiments may not only be
applied to clinical diagnosis of systems of physiological failure,
but may be applied to any clinical condition that may be
represented by images as provided herein. Indeed, the disclosed
embodiments may be applied to monitor and/or diagnose conditions in
which a patient's condition is generally improving, such as
post-surgical monitoring. Rather, the disclosure is to cover all
modifications, equivalents, and alternatives falling within the
spirit and scope of the disclosed embodiment and as defined by the
following appended claims.
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