U.S. patent application number 11/551622 was filed with the patent office on 2008-04-10 for detecting time periods associated with surgical phases and/or interventions.
This patent application is currently assigned to GENERAL ELECTRIC COMPANY. Invention is credited to Joanne Lynn Messerges.
Application Number | 20080083414 11/551622 |
Document ID | / |
Family ID | 38739318 |
Filed Date | 2008-04-10 |
United States Patent
Application |
20080083414 |
Kind Code |
A1 |
Messerges; Joanne Lynn |
April 10, 2008 |
DETECTING TIME PERIODS ASSOCIATED WITH SURGICAL PHASES AND/OR
INTERVENTIONS
Abstract
Surgical monitoring techniques determine various time periods
associated with various surgical contexts based on information
received from a patient monitor. The time periods can include start
and end times and be determined by comparisons with predetermined
data patterns, which can be based on heuristic and/or statistical
classifications, as well as in conjunction with information
received from other medical equipment. Various monitors and/or
detectors and/or controllers can also operate in conjunction
therewith.
Inventors: |
Messerges; Joanne Lynn;
(Wauwatosa, WI) |
Correspondence
Address: |
PETER VOGEL;GE HEALTHCARE
3000 N. GRANDVIEW BLVD., SN-477
WAUKESHA
WI
53188
US
|
Assignee: |
GENERAL ELECTRIC COMPANY
Schenectady
NY
|
Family ID: |
38739318 |
Appl. No.: |
11/551622 |
Filed: |
October 20, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
11548156 |
Oct 10, 2006 |
|
|
|
11551622 |
|
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Current U.S.
Class: |
600/301 ;
128/920 |
Current CPC
Class: |
A61B 2017/00022
20130101; A61B 5/0205 20130101; A61B 5/021 20130101; A61B 5/024
20130101; A61K 39/40 20130101; A61B 5/7264 20130101; A61B 5/4821
20130101; A61B 5/7267 20130101 |
Class at
Publication: |
128/920 ;
600/301 |
International
Class: |
A61B 5/00 20060101
A61B005/00 |
Claims
1. A surgical monitoring device, comprising: a detector operable in
electronic communication with a patient monitor to automatedly
determine at least one or more time periods associated with at
least one or more of a surgical phase, surgical intervention, or
both, based, at least in part, on information received from said
monitor.
2. The device of claim 1, wherein said time periods include at
least one or more or both of a start time and end time.
3. The device of claim 2, wherein said detector is operable to
determine said time periods based, at least in part, on one or more
comparisons to one or more predetermined data patterns.
4. The device of claim 3, wherein said data patterns are based, at
least in part, on one or more of a heuristic or statistical
classification.
5. The device of claim 4, wherein said detector is operable in
electronic communication with other medical equipment to determine
said time periods based, at least in part, on information received
from said other medical equipment.
6. A surgical monitoring method, comprising: automatedly
determining at least one or more time periods associated with at
least one or more of a surgical phase, surgical intervention, or
both, based, at least in part, on information received from a
patient monitor.
7. The method of claim 6, wherein said time periods include at
least one or more or both of a start time and end time.
8. The method of claim 7, wherein said determining comprises
comparing said information with one or more predetermined data
patterns.
9. The method of claim 8, wherein said data patterns are based, at
least in part, on one or more of a heuristic or statistical
classification.
10. The method of claim 9, wherein said determining comprises
determining said time periods based, at least in part, on
information received from other medical equipment.
11. A surgical monitoring system, comprising: a patient monitor;
and a detector operable in electronic communication with said
monitor to automatedly determine at least one or more time periods
associated with at least one or more of a surgical phase, surgical
intervention, or both, based, at least in part, on information
received from said monitor.
12. The system of claim 11, wherein said time periods include at
least one or more or both of a start time and end time.
13. The system of claim 12, wherein said detector is operable to
determine said time periods based, at least in part, on one or more
comparisons to one or more predetermined data patterns.
14. The system of claim 13, wherein said data patterns are based,
at least in part, on one or more of a heuristic or statistical
classification.
15. The system of claim 14, wherein said detector is operable in
electronic communication with other medical equipment to determine
said time periods based, at least in part, on information received
from said other medical equipment.
16. A surgical monitoring system, comprising: a patient monitor; a
detector operable in electronic communication with said monitor to
automatedly determine at least one or more time periods associated
with at least one or more of a surgical phase, surgical
intervention, or both, based, at least in part on information
received from said monitor; and a controller operable to invoke at
least one or more responses, based, at least in part, on said time
periods.
17. The system of claim 16, wherein said time periods include at
least one or more or both of a start time and end time.
18. The system of claim 17, wherein said detector is operable to
determine said time periods based, at least in part, on one or more
comparisons to one or more predetermined data patterns.
19. The system of claim 18, wherein said data patterns are based,
at least in part, on one or more of a heuristic or statistical
classification.
20. The system of claim 19, wherein said detector is operable in
electronic communication with other medical equipment to determine
said time periods based, at least in part, on information received
from said other medical equipment.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation-in-part of U.S. patent
application Ser. No. 11/548,156, filed Oct. 10, 2006, now
pending.
FIELD OF INVENTION
[0002] In general, the inventive arrangements relate to patient
monitoring, and more specifically, to monitoring patient data in
various surgical contexts to detect surgical phases and/or surgical
interventions and/or the like.
BACKGROUND OF INVENTION
[0003] During the course of a surgical procedure (e.g., liver
transplant, heart surgery, etc.), patients are subjected to
numerous surgical phases and/or interventions as part of the
surgery. For example, in any given surgical procedure, these may
include one or more of the following: i) induction, ii) intubation,
iii) preparation and positioning, iv) incision, v) maintenance, vi)
emergence, vii) recovery, viii) extubation, and/or ix) therapy
administration, etc.
[0004] These surgical phases and interventions are well-known. For
example, referring in general terms, induction commonly involves
administering intravenous and/or inhalational anesthetic agents to
a patient in order to induce a relaxed or sleepy condition and/or
relieve pain in the patient prior to or during surgery. During this
phase, it is also not uncommon, for example, to attach various
patient monitoring devices to the patient, thereby allowing
monitoring of the patient's breathing, oxygen levels, heart rate,
blood pressure, and other bodily functions.
[0005] During this phase, a patient may also be intubated (i.e.,
have a tube inserted into the patient's throat) or have an
anesthetic mask secured thereto to facilitate administering the
anesthetic agent to the patient.
[0006] Thereafter, the patient's body may be prepared and
positioned for the surgery, after which various incisions can be
made to the patient, as appropriate and/or necessary for the given
surgical procedure.
[0007] During the maintenance phase of surgery, the anesthetic
agents can be monitored and adjusted, as needed. In fact, this can
often occur throughout the entire surgical procedure.
[0008] During the emergence phase, the patient can be weaned from
the anesthetic agents as the surgical procedure is completed. In
some cases, reversal agents can also be administered to counteract
the effects of certain anesthetic agents and to reduce the time it
takes for the patient to recover therefrom. In any event, the
patient can be returned to consciousness as the surgical procedures
are completed.
[0009] During the recovery phase, the patient awakens, regains
muscle strength, etc., ultimately returning to a normal, alert
state. This can often occur, for example, in a post-anesthesia or
intensive care unit of a hospital ward or the like.
[0010] During either the emergence or recovery phase, it is not
uncommon to extubate the patient (i.e., remove the tube from the
patient's throat) or remove the anesthetic mask once the patient is
able to again breathe independently.
[0011] In any event, during these (and other) various phases and/or
interventions of surgery, and all therethroughout, patients often
require constant, or near-constant, monitoring and therapy
administration. Oftentimes, for example, an intervention, such as
therapy administration, including administrating a drug bolus
and/or adjusting ventilator settings, etc., can coincide with
measurable changes in physiological parameters.
[0012] In any event, during the surgical procedure, it would be
desirable to automatedly detect the afore-described phases and
interventions. For example, automatedly identifying the various
surgical phases and/or interventions can lead to increasingly
intelligent and dynamic medical device behavior. For example, many
patient monitoring alarms can be triggered once various
physiological signals cross a fixed, and commonly predetermined,
threshold. In practice, clinicians seldom adjust these alarms from
patient to patient, and they may be even less likely to adjust them
from phase to phase during a particular surgery. However, the
threshold for what most clinicians might categorize as normal can
depend on what stage a patient is in for a particular surgery. For
example, a heart rate of 50 beats/minute may be acceptable during
maintenance, but it might be abnormally low during or after
emergence. Or, a patient monitoring system may detect a concurrent
drop in blood pressure and heart rate followed by a concurrent rise
in each. This information, combined with the elapsed time since a
particular surgical procedure began, could denote intubation in
certain contexts. As a result, automated detection of surgical
phases and/or interventions can allow threshold alarms to be
appropriately dynamic throughout a given surgical procedure.
[0013] In addition to threshold alarms, for example, rate of change
alarms can also benefit from surgical phase and/or intervention
contextual information. For example, a rapid increase in blood
pressure may be normal during intubation, but not normal during
maintenance, in which case additional medical attention may be
needed.
[0014] Furthermore, monitoring settings, such as a repeat rate for
non-invasive blood pressure cuff inflation, can also be optimized
for particular surgical contexts.
[0015] In addition, non-physiological factors can also play a role.
For example, electrosurgical knives can induce noise in electrode
dependent signals (e.g., spikes in voltage, current, etc.)
Detecting this noise, perhaps in conjunction with, for example,
perceived rises in heart rates and/or blood pressures and/or
elapsed time into a surgery, could suggest the beginning of a
particular surgical incision.
[0016] In accordance with the foregoing, automated detection of
various surgical phases and/or surgical interventions would allow
sophisticated patient monitoring systems to interpret and/or
appropriately respond to patient data in light of given surgical
contexts, thereby allowing such systems to invoke appropriate
device behavior and responses at appropriate times and enhancing
the overall intelligence of medical devices, decision support
systems, and the like.
SUMMARY OF INVENTION
[0017] In one embodiment, various surgical monitoring devices
include a detector operable in electronic communication with a
patient monitor to determine various time periods associated with
various surgical contexts based on information received from the
monitor. The time periods can include start and end times and be
determined by comparisons with predetermined data patterns, which
can be based on heuristic and/or statistical classifications, as
well as in conjunction with information received from other medical
equipment.
[0018] In another embodiment, various surgical monitoring methods
include determining various time periods associated with various
surgical contexts based on information received from a patient
monitor. The time periods can include start and end times and be
determined by comparisons with predetermined data patterns, which
can be based on heuristic and/or statistical classifications, as
well as in conjunction with information received from other medical
equipment.
[0019] In yet another embodiment, various surgical monitoring
systems include a patient monitor and detector operable in
electronic communication with the monitor to determine various time
periods associated with various surgical contexts based on
information received from the monitor. The time periods can include
start and end times and be determined by comparisons with
predetermined data patterns, which can be based on heuristic and/or
statistical classifications, as well as in conjunction with
information received from other medical equipment.
[0020] And in yet another embodiment, various surgical monitoring
systems include a patient monitor; a detector operable in
electronic communication with the monitor to determine various time
periods associated with various surgical contexts based on
information received from the monitor. The time periods can include
start and end times and be determined by comparisons with
predetermined data patterns, which can be based on heuristic and/or
statistical classifications, as well as in conjunction with
information received from other medical equipment; and a controller
operable to invoke a response based thereon.
BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS
[0021] A clear conception of the advantages and features
constituting inventive arrangements, and of various construction
and operational aspects of typical mechanisms provided by such
arrangements, are readily apparent by referring to the following
illustrative, exemplary, representative, and non-limiting figures,
which form an integral part of this specification, in which like
numerals generally designate the same elements in the several
views, and in which:
[0022] FIG. 1 is a block diagram of a system for detecting surgical
phases and/or interventions;
[0023] FIG. 2 is a flow chart representing one way to develop a
surgical phase and/or intervention algorithm;
[0024] FIG. 3 depicts a fuzzy logic system that can be used with
the inventive arrangements;
[0025] FIG. 4 depicts various alarm and control signals as a
function of various behaviors in a surgery;
[0026] FIG. 5 is a flow chart representing one way to implement the
inventive arrangements; and
[0027] FIG. 6 is a graphical representation of a representative
patient monitor illustrating automated detection of a patient's
blood pressure and heart rate for a particular surgical phase
and/or intervention.
DETAILED DESCRIPTION OF VARIOUS PREFERRED EMBODIMENTS
[0028] Referring now to the figures, preferred embodiments of the
inventive arrangements will be described in terms of patient
monitoring equipment. However, the inventive arrangements are not
limited in this regard. For example, while variously described
embodiments may provide protocols for monitoring patients in
surgical contexts, other contexts are also hereby contemplated,
including various other consumer, industrial, radiological, and
inspection systems, and the like.
[0029] Now then, referring to FIG. 1, a preferred medical system
100 is depicted for detecting various time periods associated with
a surgical procedure and/or phases and/or interventions of a
patient 110 who is, or may soon be, subject to a particular
surgical procedure. In one preferred embodiment, the system 100 can
thus comprise one or more of a patient monitor 112, a detector 114,
and/or a controller 116, which can be separate or integrated units,
as desired.
[0030] For example, the patient 110 may be connected to the patient
monitor 112 for monitoring, displaying, and/or transmitting the
patient's 110 vital signs, such as their blood pressure, heart
rate, oxygen level, and/or other parameters, as needed or desired.
In particular, the patient monitor 112 can be used to reflect the
changing conditions of the patient 110 during the various surgical
phases and/or interventions to which the patient 110 may be
subjected.
[0031] In like fashion, the patient monitor 112 is preferably in
electronic communication with the detector 114, by techniques
well-known in the art, including any wired, wireless, or
combinations thereof, or any other suitable alternatives. In any
event, the detector 114 preferably receives data and/or other
information from the patient monitor 112. In a preferred
embodiment, the detector 114 may include a stand-alone central
processing unit ("CPU"), memory, and/or user interface (none
shown), and it can also be combined, integrated, or the like, if
desired, with other devices as well. For example, one or more of
the following other medical equipment may also interconnect and/or
interrelate with the detector 114: an anesthesia machine 120, an
intravenous ("IV") pump 122, and/or an electronic record system
118, such as a Picture and Archival Computer System ("PACS"),
and/or the like.
[0032] In like fashion, the detector 114 can also be in electronic
communication with the controller 116, again by techniques
well-known in the art, including any wired, wireless, or
combinations thereof, or any other suitable alternatives. In any
event, the controller 116 preferably receives data and/or other
information from the detector 114 and/or patient monitor 112 and/or
other medical equipment (e.g., the anesthesia machine 120, IV pump
122, and/or electronic record system 118).
[0033] In a preferred embodiment, the detector 114 transmits output
control signals to the controller 116 for enabling dynamic medical
device reaction throughout various surgical phases and/or
interventions. The controller 116, in turn, is preferably
interconnected back to the patient monitor 112 and can control the
desired display and alarm parameters during the surgical procedure.
It should be understood that although control signals can be sent
from the controller 116 to the patient monitor 112, the system 100
can also be designed to interact with any other alarm and/or user
interface ("UI") and/or medical device arrangements in suitable
fashion.
[0034] Preferably, the system 100, comprising various ones or
combinations of the patient monitor 112, detector 114, and/or
controller 116, can be designed to identify surgical phases and/or
interventions with a basic confidence. That confidence can be
increased as the system's 100 configuration is expanded to include,
for example, the electronic record system 118, anesthesia machine
120, and/or IV pump 122, as well as any other suitable medical
equipment.
[0035] Preferably, the electronic record system 118, for example,
can be interconnected to the detector 114 to provide information
related to surgical protocols, drug names used during surgery,
patient demographics, etc. Likewise, the anesthesia machine 120 can
be interconnected between the patient 110 and detector 114 to
provide information such as ventilation readings, drug
concentrations used during surgery, etc. The IV pump 122 can also
be interconnected between the patient 110 and detector 114 to
provide information related to infusion rates and the like. It
should also be appreciated that the system 100 can also discern
surgical phases and/or interventions with 100 or near-100 percent
confidence with manual input of surgical phases and/or
interventions to the detector 114 and/or controller 116, as by a
clinician 124 or the like, so as to permit overriding an automated
response, for example, preferably manually.
[0036] In one preferred embodiment, the detector 114 can be either
a heuristic classifier (based on an expert system) or a statistical
classifier. A heuristic classifier mimics procedures used by an
expert (e.g., an anesthesiologist) to discern surgical phases
and/or interventions. For example, a heuristic classifier can use
programmed rules to compare local features with expert-determined
thresholds and determine whether a transition from one phase and/or
intervention to the next has occurred. These programmed rules can
be stored as process parameters in a memory (not shown). In any
event, suitable heuristic classifiers can be used.
[0037] In contrast to the afore-described heuristic classifiers,
which can rely upon expert-defined rules, statistical classifiers
can develop their own classification rules during a training phase.
Statistical classifiers may use, for example, techniques of
multivariate regression, k-nearest neighbor procedures,
discriminate analysis, as well as neural network techniques. Using
local features drawn from representative training populations, for
example, a statistical classifier can be trained to associate
particular patterns in local features with clinical outcomes of
interest. For example, a statistical classifier, in this regard,
can be trained with data derived from a particular clinical phase
to identify patterns of local features associated with, and unique
to, that particular phase. One output of a statistical classifier,
for example, can be a classification statistic that is compared
with a numerical threshold to yield a final decision, e.g., whether
or not a patient is in an intubation phase. For example, a
classifier producing an output that is less than a numerical
threshold "t" may classify the local features as belonging to an
intubation phase, while producing an output that exceeds the
threshold "t" may result in classifying the local features as in a
non-intubated phase. These outcomes can then be stored as process
parameters in a memory (not shown). In any event, suitable
statistical classifiers can be used.
[0038] To perform statistical classifier techniques, an adequate
development database of clinical data should be available. FIG. 2
illustrates a representative method to obtain the development
database and extract known data patterns. With appropriate device
interfaces in place, data can be collected in a step 126
(preferably via data collection software) from at least one or more
devices in an operating suite to build a database comprising many
surgical cases. As respectively represented by steps 128 and 130,
such a database may include, for example, start and stop times of
surgical phases and/or interventions, as determined by a clinician
124 or the like, as well as other relevant surgical information,
such as patient demographics, fluids and drugs administered,
labwork, and the like, and any other measurements taken during a
surgery for a particular surgical case, etc. The database can then
be divided into the data collected during each surgical phase
and/or intervention. The database can also be divided further
according to information known about the case, e.g., types of
surgery, surgical protocols, patient demographics, etc. Thereafter,
a step 132 can identify key features associated with each surgical
phase and/or intervention, such as by using principal component
analysis. Once data is appropriately grouped, classifier
techniques, or the like, can be performed at a step 134 to identify
distinct data patterns in each surgical phase and/or intervention.
Data patterns may consist of, for example, timing information,
common trends, absolute values, noise characteristics observed from
vital signs during induction or maintenance, etc. For example, one
may discover that a rise in blood pressure in the range of 5-20
mmHg within the first 20 minutes of a surgery may consistently
coincide with intubation. Once classifier techniques are formed,
development can include obtaining desired alarm behavior for each
surgical phase and/or intervention, as represented by a step 136.
Likewise, desired instrument behavior, UI behavior, and electronic
documentation can also be obtained for each surgical phase and/or
intervention, as respectively depicted in steps 138, 140, and 142.
Thereafter, data patterns obtained from the development database
can be used to validate performance against a validation database,
acquired during real-time surgery, for example, as depicted in a
step 144.
[0039] The data patterns identified through classifier techniques
can drive the development of an expert system capable of
identifying these patterns in real time. Although numerous expert
systems can be used, implementation using fuzzy logic will be
described. Fuzzy logic systems depend on various rules that can be
evaluated in real-time based on incoming data. Oftentimes, these
rules can be structured as if/then statements, e.g., "If A and/or B
and/or C . . . then D." In implementing surgical phase and/or
intervention detection, the discovered statistical patterns and/or
heuristic classifications can be translated into rules, e.g., "If
blood pressure is rising quickly and surgery just started, then the
surgical phase is induction." In this statement, the terms "rising
quickly" and "just started" are fuzzy, meaning there is a range of
values that one could classify as "rising quickly" or "just
started," as opposed to a specific number. The results from the
statistical and/or heuristic techniques can be used to define the
appropriate ranges for each of these "fuzzy" terms. Accordingly,
fuzzy logic can be well-suited to surgical phase and/or
intervention detection, as different patient cases tend to be
unique, e.g., the rate at which blood pressure rises during
intubation can vary across patients. Thus, fuzzy logic allows for
case-to-case analysis and variability.
[0040] With an established set of expert rules, the input features
to a fuzzy logic system can be defined. For example, if the rules
include statements about changes in blood pressure and time into
surgery, then such a fuzzy system can be provided with blood
pressure trends and surgical time in order to evaluate these rules.
These features can define the signal processing that can be done
before acquired data can be passed to a fuzzy logic system. Signals
can then be processed to provide features such as data trends,
noise content, integrated information, etc.
[0041] Signal processing, followed by fuzzy logic interpretation,
can be used to identify the transition from one surgical phase
and/or intervention to the next. To accomplish this, the fuzzy
logic system could consider the current phase, evaluate rules based
on incoming data, and determine whether or not the data is
indicative of a next phase and/or intervention, as depicted in FIG.
3. If, according to the expert rules, the data is characteristic of
a next phase and/or intervention, then the system 100 can be
identified as having detected a transition point. Otherwise, the
system 100 can assume the surgical phase and/or intervention has
not changed.
[0042] Since surgical phases and/or interventions tend to follow
generally consistent time sequences, e.g., induction before
maintenance, etc., algorithms can be preferably modeled as state
systems, whereby different states represent different surgical
phases and/or interventions and transitions are driven by fuzzy
logic output. In FIG. 4, for example, affected features, such as
possible mean blood pressure threshold alarm limits, possible user
interface layouts, and possible non-invasive blood pressure cycling
times can be considered for each surgical phase and/or
intervention. Furthermore, multiple state transition models can be
stored in a memory (not shown), each corresponding to a particular
surgery type, protocol, patient demographics, etc.
[0043] Alternative methods for phase and/or intervention
determinations include neural networks and Hidden Markov Model
(HMM) techniques. Because of the well-known surgical state
transitions and highly suggestive observable characteristics of
different surgical phases and/or interventions, the HMM technique
may be particularly well-suited.
[0044] FIG. 5 illustrates one example of a flow diagram
representing a possible real-time implementation of a surgical
phase and/or intervention detection system 100 during a particular
surgical procedure. If clinician input is not available, then the
system 100 can acquire data from the patient monitor 112, such as
blood pressure, heart rate, oxygen level, etc., at a step 146. For
enhanced confidence, it can also be desirable to acquire anesthesia
machine 120, IV pump 122, and electronic record system 118 data, if
possible, in respective steps 148, 150, and 152. Thereafter,
physiological signals can be signal processed at respective steps
154 and 156 to remove noise and/or calculate key features. These
physiological signals can also be classified at a step 158 by
comparing relationships and/or trends to known data patterns. Based
on available inputs, a determination can then be made in the
confidence of the surgical classification, at a step 160.
[0045] If it is determined, at a step 162, that the surgical phase
and/or intervention is the same as previously detected, then the
detection process can be repeated, as shown at a step 164. If the
surgical phase and/or intervention is different from the previously
detected phase and/or intervention, then a determination can be
made, at a step 166, as to whether the confidence is above a
predefined threshold. If so, then the newly determined surgical
phase and/or intervention can be stored as the previous phase
and/or intervention, at a step 168. In due course, the system 100
can invoke a clinically desired alarm, instrument, and/or UI
behavior, respectively at steps 170, 172, and 174, and invoke
clinically desired electronic documentation at a step 176, as
desired, then repeat the process at a step 178.
[0046] If the confidence obtained at step 166 is not above a
predefined threshold, then a clinician can confirm the detected
phase and/or intervention at a step 180. If the clinician does not
agree with the detected phase and/or intervention at a step 182,
for example, then the process can be repeated at step 164.
Alternatively, if the clinician does agree with the detected phase
and/or intervention at step 182, then the process can revert to
steps 168-178.
[0047] Also, if a clinician can determine surgical phases and/or
interventions, then steps 168-178 can be carried out, particularly
at the time when it is determined that clinician input is
available.
[0048] Identification of the surgical phases and/or interventions
can also be displayed on the patient monitor 112 (see FIG. 1). An
example of automated detection of surgical phases and/or
interventions is shown in FIG. 6 for an early stage of surgery.
Here, an initial fall in systolic pressure, mean arterial pressure
(MAP), diastolic pressure, and heart rate, then followed by an
increase in each, can signify that intubation followed
induction.
[0049] The present invention enables interpreting patient data in
light of a given surgical context. This presents an opportunity to
improve patient alarms and the decisions of support systems, as
well as automate, standardize, and/or elaborate electronic record
keeping. In addition, optimizing monitoring protocols can also
occur for individual surgical contexts.
[0050] Once the time periods associated with a surgical procedure
and/or phases and/or interventions are determined by a clinician
124 or the like in steps 128 and 130 when obtaining the development
database and extracting known data patterns in FIG. 2, these data
points can be used with the inventive arrangements. For example,
the detector 114 may be able to automatedly detect the start and
end time of a surgical procedure and/or phases and/or interventions
thereof.
[0051] Preferably, these time periods can be determined by
comparing current timing information with predetermined and/or
stored timing information and/or data patterns. For example, if the
detector 114 begins receiving information from the patient monitor
112 and/or electronic record system 118 and/or anesthesia machine
120 and/or IV pump 122, it may determine that a particular surgical
procedure and/or phase and/or intervention has begun. It may also
be able to determine, for example, a length of time of the surgical
procedure and/or phases and/or interventions. Thus, start and end
times of the surgical procedure and/or phases and/or interventions
can be based, at least in part, on information received from the
patient monitor 112 and the like. This can also extend to
determining time periods associated with a particular session on a
particular device, such as the anesthesia machine 120 and/or IV
pump 122. Start and end times associated therewith, for example,
can be determined for various sessions on such devices.
[0052] For example, if the detector 114 does not receive any
information from a particular device for a period of time, it may
conclude that the device is no longer associated with the patient
100. Or, if the detector has not received any information from a
particular device for a period of time and then starts doing so, it
may conclude that the device is now associated with a patient 100.
Depending on the way the timing patterns are established, the
system 100 may be able to determine, for example, if a new patient
100 has been introduced to the system 100 or if a previous patient
100 is undergoing another part of a surgical procedure and/or phase
and/or intervention. In other words, the presence or absence of
data for particular time periods can lead the detector 114 to make
determinations about the surgical procedure in general and/or the
phases and/or interventions of the surgical procedure and/or
sessions associated with the surgical procedure and/or particular
pieces of medical equipment.
[0053] It should be readily apparent that this specification
describes illustrative, exemplary, representative, and non-limiting
embodiments of the inventive arrangements. Accordingly, the scope
of the inventive arrangements are not limited to any of these
embodiments. Rather, various details and features of the
embodiments were disclosed as required. Thus, many changes and
modifications--as readily apparent to those skilled in these
arts--are within the scope of the inventive arrangements without
departing from the spirit hereof, and the inventive arrangements
are inclusive thereof. Accordingly, to apprise the public of the
scope and spirit of the inventive arrangements, the following
claims are made:
* * * * *