U.S. patent application number 11/251555 was filed with the patent office on 2006-06-29 for system and method for repetitive interval clinical evaluations.
Invention is credited to William H. Rice.
Application Number | 20060142645 11/251555 |
Document ID | / |
Family ID | 36612719 |
Filed Date | 2006-06-29 |
United States Patent
Application |
20060142645 |
Kind Code |
A1 |
Rice; William H. |
June 29, 2006 |
System and method for repetitive interval clinical evaluations
Abstract
A healthcare tool allows a patient to record daily parameters
associated with the patient's clinical status, for example, body
weight for congestive heart failure patients. A graph may be
created showing the parameters on a control chart. The parameters
are statistically analyzed against a control range, and when a
parameter moves out of the control range, the system automatically
creates a pop-up window alerting the patient that the parameter is
outside the control range, and that the patient should consider
informing a healthcare professional.
Inventors: |
Rice; William H.; (Austin,
TX) |
Correspondence
Address: |
HULSEY IP;Intellectual Property Lawyers, P.C.
Bldg. 3, Suite 610
1250 S. Capital of Texas Highway
Austin
TX
78746
US
|
Family ID: |
36612719 |
Appl. No.: |
11/251555 |
Filed: |
October 14, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10313820 |
Dec 6, 2002 |
6955647 |
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11251555 |
Oct 14, 2005 |
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10174498 |
Jun 17, 2002 |
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10313820 |
Dec 6, 2002 |
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Current U.S.
Class: |
600/300 ;
128/920 |
Current CPC
Class: |
A61B 5/0002 20130101;
G16H 15/00 20180101; A61B 5/7275 20130101; Y10S 128/92 20130101;
G16H 40/67 20180101; G16H 10/60 20180101; G16H 50/50 20180101; A61B
5/00 20130101 |
Class at
Publication: |
600/300 ;
128/920 |
International
Class: |
A61B 5/00 20060101
A61B005/00 |
Claims
1. A computer implemented method of impeding a progression of a
disease within a patient comprising the steps of: defining a set of
disease associated parameters; performing repetitive measurements
on said set of disease associated parameters; evaluating said
repetitive measurements of disease-associated parameters;
performing statistical analysis on a history of said repetitive
measurements of disease associated parameters; alerting the patient
to those statistical analyses which indicate a potential future
problem that requires intervention; and transmitting said
statistical analyses to a network associated with a plurality of
health care providers; and applying secondary prevention techniques
to address said potential future problem.
Description
BACKGROUND OF THE INVENTION
[0001] More than 90 million Americans live with chronic diseases.
Care for these Americans accounts for more than 60% of the nation's
medical care costs. By definition, a chronic disease progresses
over time with a generally predictable set of costly exacerbations,
complications and recurrences.
[0002] A central precept to the discussions on health care costs is
that there is a cost-quality function from which one may derive a
linear cost-quality curve. On such a cost quality curve, so the
argument goes, any reduction in the planned budgetary growth of
health care dollars will result in lower-quality health care. To
the contrary, however, the actual cost-quality curve for health
care has been shown to be significantly non-linear. FIGS. 1A and 1B
depict the perceived and actual cost-quality curves showing the
relationship between cost and health care quality. FIG. 1A depicts
an expected cost-quality curve 10, while FIG. 1B depicts the actual
non-linear cost-quality curve 12.
[0003] In the actual health care cost-quality curve 12 of FIG. 1B,
increased costs do not always correlate to improved quality.
Instead, there has been shown to be a "quality valley" 14, where
health care quality actually decreases with increased expenditures
for health care. Understanding this potential "quality valley" 14
is essential to the creation of real improvements and cost savings
in health care. That is, if "quality valley" 14 could be either
carefully managed against for either its elimination or, if it
cannot be eliminated, its avoidance, there could be an opportunity
simultaneously decrease costs and improve quality.
[0004] Research for two common medical diagnoses, congestive heart
failure (CHF) and pneumonia, for example, indicates a wide
variation in outcomes among providers. By matching
severity-adjusted mortality data to hospital-specific charge data,
one can observe that higher average charges often associate with a
lower quality of care.
[0005] These results support the conclusion that significant
variation in charges exists among hospitals. These variances may
imply that higher costs associate with lower quality (resulting,
for example, in higher severity-adjusted mortality rates). This
represents unnecessary resource utilization.
[0006] Making comparisons among the ten countries having the
highest Gross Domestic Product (GDP) per capita further validates
this conclusion. Data from the United States Statistical Abstract
indicates that the United States spends the largest percentage of
its gross domestic product (GDP) on health care, while exhibiting
one of the world's lowest life expectancy at birth (LEAB rates).
International health expenditure studies are difficult to conduct,
however, because of factors such as data quality, variable
accounting methods, and significant social-cultural differences.
Despite these shortcomings, a highly reasonable conclusion remains
that, with the present systems and methods for managing diseases
such as CHP and pneumonia, spending more dollars on health care
results in a decrease in health care quality received, as measured
on a large scale, for example, by LEAB rates.
[0007] Although every physician should consider the best interests
of his/her patients, the medical system has evolved with a history
of incentives, threats (e.g., medical malpractice), and customs
that can significantly increase costs, while not improving
quality.
[0008] Additionally, disease intervention processes and treatments,
all too frequently seek to improve patient comfort, longevity, and
physical functioning. These processes and treatments employ
surrogate endpoints based on logical, but unproven, extensions of
an existing, but incomplete, disease process model. A great number
of physician actions are based on these surrogate endpoints. These
surrogate endpoints, however, often lead to increased costs and
examinations without improved results.
[0009] A need exists, therefore, for significant efforts to
optimize the cost and quality relationship of healthcare. Prior
efforts focus on the development of "best practices" protocols,
medical error reduction, bulk purchasing and pharmaceutical
benefits management, new medicine, minimally invasive surgery, and
the redesign of care systems. These efforts seek to more
effectively manage demand for health services. While past practices
are important, these efforts fail to address any way to reduce
costs and improve quality in healthcare. In particular, they
already fail to provide for complication identification and
proactive symptom treatment of chronic disease exacerbation in the
individual patient.
[0010] One avenue of attempting to better practice early
complication identification and proactive symptom treatment has
been through the use of computers. Such attempts to use computers,
for example, seek to automate more routine aspects of medical
processes and treatments. These computerized schemes, for example,
may center on communicating automatically with a patient regarding
a previously diagnosed disease. In such processes, automatic
therapy adjustment becomes responsive to information received from
the patient. Such automated schemes of medical treatment typically
involve the use of computers and the Internet to treat patients
remotely. The purpose of these conventional schemes of remote
treatment by using computers or Internet avoids unnecessary office
visits, thereby effecting savings in overall healthcare costs.
Thereby, a physician may be virtually "present" at the patient's
location and help treat the patient remotely.
[0011] Unfortunately, attempts to automate patient-physician
communications do not change previous paradigms for certain chronic
diseases. With many of these chronic diseases, infrequent physician
visits, either in person or through a virtual office, are accepted
as normal. Thus, it has not been possible to identify evolving
complications, exacerbations or recurrences, within certain classes
of chronic disease patients. At the same time, early interventions
may mitigate a patient's worsening clinical condition. In fact, in
many instances, early interventions may avoid the need for
emergency medical services altogether. Also, disease predictive
models have not proven effective to predict the worsening of a
patient's condition from chronic diseases. Because of these and
other reasons, a standardized therapy based upon broad demographic
models is difficult or impossible to employ remotely.
[0012] A need exists, therefore, for a system and method that allow
early detection of chronic disease exacerbations or complications
in order to decrease the need for emergency medical services while
measurably improving patient outcomes.
[0013] Returning to the above discussion regarding the health care
cost-quality curve, often chronic diseases, such as CHF, exhibit a
non-linear cost-quality relationship. Accordingly, managing a
patient's condition preventively, as opposed to remedially, may
assist in avoiding a "quality valley." That is, such preventive
management could avoid the situation of increased health care
expenditures, ironically, resulting in lower returns in patient
outcome. If it were possible to achieve early detection of chronic
disease exacerbations or complications, well before the greater
cost treatments are necessary, then the health care industry could
avoid troubling regions of a non-linear cost-quality curve. In a
larger sense, therefore, there is a need for an early detection
method and system making it possible to greatly reduce overall
health care costs while improving patient quality of life.
SUMMARY OF THE INVENTION
[0014] The present invention provides a computer-implemented method
for the earliest identification of an exacerbation or complication
relating to a chronic condition within a patient. A series of
regular repetitive measurements are taken on a set of
disease-associated parameters. A history of these parameters is
compiled and evaluated using various statistical methods and
knowledge of the particular disease. Potential worsening conditions
are identified proactively. Once identified, secondary prevention
techniques may be employed to prevent the exacerbation and, in
doing so, reduce the associated health care cost, while improving
the patient's quality of life.
[0015] Another embodiment provides a health parameter statistical
control measurement tool for improving or optimizing chronic
disease care. The system may employ a linear or non-linear
optimization model using repetitive, internal, clinical evaluations
(i.e., repetitive monitoring) as a primary tool for the earliest
possible detection of the onset of a worsening clinical condition.
This is especially true for patients whose conditions are sensitive
to slight changes in their physician and/or emotional conditions,
for example. This condition may be associated with a specific
chronic disease diagnosis of previously unidentified conditions,
the tracking of a critical care pathway, or rehabilitation.
[0016] In some embodiments, patients themselves conduct the
repetitive, interval, clinical evaluations and provide the results
of these evaluations to a statistical or measurement process, such
as a computer program using data associated with the patient's
condition. The parameters are then compiled and compared to
identify statistical trends or clinical conditions. If one or more
parameters fall outside a predetermined statistical control range,
the process alerts patient to follow-up with appropriate healthcare
team practitioners, such as a nurse or physician. Alternately, the
present invention may automatically alert a healthcare practitioner
of a condition requiring or suggesting a direct contact with the
patient.
[0017] Advantageously, the system of the present invention allows
for early detection of chronic disease care exacerbations or
complications. Accordingly, the present invention supports a
decrease in the need for health care services, while measurably
improving clinical outcomes for the most common diagnoses of
chronic disease patients.
[0018] Still further, the present invention promotes general cost
and treatment optimization of health care provisions on a larger
scale, due to the ability to treat patients in a preventive,
instead of a remedially, manner. By identifying trends in an
individual patient's condition, the present invention guides or
directs the use of reduced or preventive healthcare measures. Such
measures frequently are more economical and effective than remedial
treatments. This results in movement of the individual patient to a
more cost-effective position on the health care cost-quality curve.
Therefore, collectively, a great number of chronic disease patients
moving to these more effective areas on the health care
cost-quality curve will normally improve the effectiveness, or
return on expenditure, for health care processes and
treatments.
[0019] In one embodiment of the invention, a process derives a
critical difference as a rolling average of twenty measurements as
the basis for repetitive, interval, clinical evaluations, but using
a seven-measurement lag and three times the moving sigma, based on
twenty prior measurements, as specified in detail below. For
purposes of the present embodiment and in the case of CHF, the
seven-measurement lag may represent, for example, the set of twenty
measurements where the most recent measurement occurred seven days
ago and the least recent occurred twenty-seven days ago, with daily
measurements occurring each of the intervening days. In another
embodiment, a different set of measurements might be more
appropriate to take than the twenty measurements and seven-day lag
used in the CHF case.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The present invention will be described with particular
embodiments thereof, and references will be made to the drawings in
which:
[0021] FIGS. 1A and 1B illustrate perceived linear and actual
non-linear relationship between health care costs and quality of
care;
[0022] FIG. 2 provides a flowchart depicting one embodiment of the
method provided by the present invention;
[0023] FIG. 3 gives a flow diagram illustrating one embodiment of a
process performed by the system of the present invention;
[0024] FIG. 4 shows a set-up process which a patient may employ in
using an embodiment of the present invention;
[0025] FIGS. 5-8 and 9A-B present exemplary screen shots of the
steps performed by the health parameter statistical control
measurement tool according to an embodiment of the present
invention;
[0026] FIG. 10 depicts an exemplary screen shot of a "Report"
according to an embodiment of the present invention;
[0027] FIG. 11 portrays an exemplary screen shot of an additional
alerting step according to an embodiment of the present
invention;
[0028] FIG. 12 shows an exemplary screen shot of an "EXIT" step
according to an embodiment of the present invention;
[0029] FIGS. 13A-B show one view of a computer spreadsheet having
embedded formulae which an embodiment of the present invention may
use to record, manipulate, and present information to an interface
such as those of FIGS. 5 through 12; and
[0030] FIG. 14 illustrates a typical computer system for employing
the many aspects of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0031] The present invention provides a method and system for
improved identification and evaluation of exacerbations and
complications relating to chronic diseases. One embodiment of the
present invention relates to a system to optimize chronic disease
care. For purposes of the present invention, chronic disease care
optimization may be defined as the process of early identification
of exacerbations, complications and recurrences. Early
identification allows a patient to alert his healthcare provider,
receive preventive or early stage remedial treatments, and/or avoid
costly and intensive remedial medical interventions and/or
hospitalizations. The collected data leads to early identification
and the opportunity for alerting the patient or the health care
provider of a situation.
[0032] One embodiment of the present invention may use a non-linear
model, such as a chaotic model. However, various non-linear models
may be envisaged. In chaotic models, a sensitive dependence exists
on model initial conditions and assumptions. Mathematically, the
initial conditions of a system, when varied by an exceedingly small
amount, can result in widely variable outcomes without a
distinguishable pattern.
[0033] In a chaotic or complex system, repetitive measurements
improve the ability to model and predict future conditions. Weather
prediction provides a classic example of non-linear systems with
"chaotic" or "complex" components. The National Oceanographic and
Atmospheric Association (NOAA), a component of the U.S. Department
of Commerce, gathers data and predicts the weather. Several decades
ago, as mainframe computers became available to solve large data
set problems, programs to model weather systems began to evolve
from improving NOAA's weather predicting ability. Soon, NOAA
discovered that if the input data of the program varied by some
exceedingly small amount (e.g., if barometric pressure at some
location increased by an un-measurable thousandth of an inch), then
the model output differed drastically.
[0034] Optimizing a non-linear system with a "chaotic" component
employs repetitive data sampling where the critical element is the
periodicity of the data sampling. The best possible weather
predictions, for example, depend on frequent measurements over
time. More intensive measurements taken less frequently are not a
reliable approach for optimizing weather prediction.
[0035] Now, a repetitive data sampling system has direct
applications to healthcare. For example, one embodiment of the
present invention may be used to identify problems associated with
the care of a patient diagnosed with congestive heart failure
(CHF). CHF is characterized by a heart muscle that cannot pump
blood effectively. Patients with CHF generally have difficulty
breathing because excess fluids "behind" a weakened heart
accumulate in the lungs. Care for CHF patients includes medicines
such as diuretics to improve breathing by removing excess fluids.
With the removal of excess fluids, the patient's lungs become
"clear," which allows the patient to breathe more normally.
[0036] Because water is the primary component of the human body,
body weight measurements (on an ongoing basis) are an excellent
indicator of the clinical status of a patient with CHF. Current
care of most CHF patients includes visits to physicians' offices
approximately every 3 to 6 months, depending on the severity of
symptoms. By monitoring body weight twice a week, hospitalization
rate and corresponding costs can be reduced by approximately
50-90%. Thus, cost has been reduced and quality of life has
improved. Repetitive clinical monitoring of body weight, for
example, twice a week, in CHF patients should be the "standard of
care."
[0037] Just as weather prediction may be viewed as a chaotic
system, so too may prediction of emergency conditions with chronic
diseases be considered a chaotic system problem. It should be noted
here, however, that the present invention is not limited to
applications in CHF, but may have use in applications to other
chronic diseases such as, but not limited to, diabetes, asthma,
emphysema, cancer, and other cardiovascular diseases known to those
skilled in the art.
[0038] Nonetheless, CHF provides an excellent case for applying the
teachings of the present invention, since CHF patients represent
the largest disease class and the most commonly hospitalized group
of individuals over the age of 65 in the United States. Just as two
weather conditions may, in almost all salient aspects, appear
virtually identical, two CHF patients may appear much the same on
one day, but exhibit drastically different conditions in only a
very short span of time. In one example, two CHF patients could
"look" clinically identical in two discrete observations having the
exact same medical histories, lifestyle, and clinical findings and
can be seen, diagnosed and treated at the exact same time in the
exact same way. However, these discrete observations lack any
historical trends. One cannot accurately predict which patient will
progress with an uneventful clinical course and which patient will
deteriorate and need intensive care without additional data.
[0039] This example of two "identical" patients may be considered
as analogous to the weather system model in that the two
"identical" weather conditions exhibiting two seemingly identical
initial conditions (differing barometric pressure by an
un-measurable thousandth of an inch). Only repetitive monitoring
will cause historical trends to distinguish between patients in
many instances.
[0040] Interestingly, an individual's body weight provides an
easily measured parameter that enables prediction of likely
exacerbations and complications in CHF patients. CHF patients have
occasional exacerbations that require hospitalization and intensive
care. However, a predictable sequence of symptoms and findings
precedes the patient's "decomposition." CHF patients often begin a
pattern of weight gain. This progression of an easily measured
parameter provides a window of opportunity for emergency condition
prevention in CHF patients. Mitigation of disease exacerbations
consists primarily of alerting the patient, and eventually, the
healthcare provider team of the weight gain trend. When the
healthcare team knows that a CHF patient is gaining weight, the
treatment can be changed. For example, incremental doses of
diuretics, changes in diet and other measures can very effectively
prevent the acute clinical exacerbation.
[0041] Thus, in chronic disease care, more frequent data inputs can
result in earliest detection of clinical exacerbations and
complications. In this instance, secondary prevention can address
an evolving problem before the problem incapacitates the patient
and requires intensive, expensive, and, often times, less
successful medical intervention.
[0042] The present invention, therefore, includes a method and
process to support the more frequent collection of relevant chronic
disease data, which may avoid the need for such interventions.
Referring to FIG. 2 to understand the present inventions, there
appears a flow chart depicts a repetitive, interval, clinical
evaluation method 20 consistent with the teachings of the present
invention. In step 22, a patient may be diagnosed with a chronic
disease or condition. This disease or chronic condition may have a
specific set of disease-associated parameters that may be measured
by the healthcare team in a clinical environment or the patient at
home.
[0043] These parameters may be either objective measurements, such
as the patient's weight, as discussed previously, or subjective
measurements, as when dealing with other conditions such as mental
disease. The patient or healthcare provider in step 24 measures the
parameters. These measurements are then compiled by a computer
program as part of the patient's historical record. The instant
measurements are evaluated for potential data entry errors or
indication of immediate healthcare problems in step 26. In step 28,
the overall history of measurements is studied to identify
statistical or medical indicators of worsening conditions or
potential problems. The patient or healthcare team is then alerted
at step 30 to potential future problems. This alert allows
secondary prevention techniques to be applied to the patient's
condition. This allows the disease condition to be treated in a
proactive rather than reactive manner, such as through the
application of secondary prevention techniques at step 32.
Furthermore, this allows patient quality of life to increase while
reducing healthcare costs. Furthermore, this approach, when taken
on a macroscopic scale, can significantly decrease healthcare costs
of an individual medical practice, a hospital system or
geographical region.
[0044] FIG. 3 illustrates the flow of one embodiment of a process
40 that a computer may implement as part of the present invention.
In step 42, process 40 starts by downloading a program application,
for example, a JAVA applet from a Web server. The JAVA applet may
run on a patient's computer using a JAVA-compatible Web browser,
such as Netscape Navigator or Microsoft Internet Explorer. It
should be noted that if a second patient desires to also use the
system, the program application may be written to accommodate
additional patients or, alternatively, the second patient may
download the JAVA applet another time, in order, for example, to
keep the associated patient information separate.
[0045] In step 44, after the JAVA applet is downloaded, the patient
initially sets up the system. In step 46, the process creates a
desktop icon. FIG. 4 illustrates an example of the set up process
50 according to an embodiment of the present invention. So,
referring to FIG. 4, in step 54, a first time patient inputs an
identifying name. The process then continues to the health
parameter statistical control measurement tool. A repeat patient,
in step 56, simply double clicks on a desktop icon to enter the
program, and then the process goes, via step 58, to the health
parameter statistical control measurement tool.
[0046] Referring back to FIG. 3, process 40 proceeds to the is
health parameter statistical control measurement tool at data
tracker step 60. The health parameter statistical control
measurement tool receives inputs or parameters associated with a
particular patient's health condition or clinical status. The
health parameter statistical control measurement tool will be
described in more detail below with respect to FIGS. 5-9.
[0047] In step 62, process 40 generates a report, which may include
a graph covering a desired time frame selected by the patient. In
exit system step 64, the process reaches an endpoint. These steps
will be explained in more detail below with respect to FIGS.
10-12.
[0048] FIGS. 5-9 present exemplary screen shots, such as screen
shot 70 of FIG. 5, to illustrate the steps performed by the health
parameter statistical control measurement tool of the present
invention. After a patient signs into the system, the system goes
to health parameter statistical control measurement tool as
indicated by the highlighted "TRACKER" button 72 of screen shot 70
of FIG. 5.
[0049] The system will be here described in conjunction with an
application for a CHF patient, wherein the system tracks the
parameter of a CHF patient's body weight as a way to prevent
chronic disease condition exacerbations. Because many other chronic
diseases have easily measured parameters highly associated with the
patient's clinical status, the system of this invention can be
broadly applied to the care of these diseases as well. Chronic
diseases in the United States that may be tracked include, but are
not limited to: [0050] asthma, for which peak flow can be measured;
chronic obstructive pulmonary disease (emphysema), for which flow
can be measured; [0051] diabetes, for which glucose can be
measured; [0052] other cardiovascular diseases such as arrhythmia,
infarction, ischemia, arteriosclerosis for which number of
nitroglycerin tablets taken daily, number of chest pain episodes,
ambulation distance without pain, minutes walking without pain,
etc. can be measured; [0053] rehabilitation, such as from hip and
knee replacements, for which ambulation paces/activity can be
measured; or [0054] cancer, post chemotherapy/post radiation of
toxicity such as food/liquid intake, etc. can be measured.
[0055] Following a prompt from a computer supporting the present
invention's process, a CHF patient or healthcare worker may enter
the patient's measured body weight or other measured parameters. In
the embodiment shown in FIG. 5, the patient clicks on number pad
74, which appears on screen 70 to enter the weight, which appears
in display area 76.
[0056] FIG. 6 illustrates an example in which the patient entered a
weight of 125 in display area 76. Once the patient enters the
weight, the button 78 labeled "Done" may be pressed to continue. It
should be noted that in other embodiments, the patient might be
asked to confirm the entry. Other methods of data entry, either
manual or automated, as known to those skilled in the art, may be
used to facilitate the process.
[0057] FIG. 7 provides the next exemplary screen shot 80 where the
patient may confirm that he has completed the weight entry for the
day. Next, the process prompts the patient to click on the
appropriate tab to continue. As shown, the patient may have several
options. For example, the patient may choose to receive a report by
clicking on the "REPORTS" icon 82, information on "WHY THIS
MATTERS" by clicking on icon 84, or exit the system by clicking on
the "EXIT" icon 86.
[0058] In FIG. 8, screen shot 90 indicates that the patient entered
a weight of 145 the next time. Once the patient enters the weight,
icon 92 labeled "Done" is clicked to continue. It should be noted
that in other embodiments, the patient might be asked to confirm
the entry.
[0059] FIGS. 9A and 9B illustrate further exemplary screen shots
100 and 102, respectively. In FIG. 9A, the body weight entered of
185 exceeds a control range for the particular patient, causing the
system to give the patient an "Alert" report 104, for example, with
the words that "Bill, the weight you entered is a large change from
recent entries, we recommend you consider calling your healthcare
provider." Next icon 106 allows the patient to progress to screen
102 of FIG. 9B.
[0060] Because the weight of 185 is entered after the initial entry
of 125 on the same day, FIG. 9B shows a subsequent screen shot with
a message 108 stating, for example, that "Bill, you have already
entered the following weight for today." Icons 110 and 112 permit,
respectively, the patient to confirm that the entry is correct by
clicking "If Correct, Click Here" or to modify by clicking "To
Modify, Click Here." A message 110 guides the patient with the
statement that "For best results, try to weigh yourself at about
the same time each day, wearing about the same amount of clothing.
For instance, in your underwear when you first get up in the
morning." Other steps, as known to those skilled in the art, and
messages may be taken to ensure the coherency and integrity of the
data collection process.
[0061] The present invention performs a statistical analysis on the
data collected through the above screens using an averaging program
and self-comparison of data. The system may use a control range
established by the Deming statistical method, or other
methodologies as known to those skilled in the art. In one example,
when the weight of the patient exceeds about three percent of the
control range, the system produces an Alert to the patient.
[0062] Statistical analyses steps for congestive heart failure may
include establishing a base line weight associated with an initial
stable condition for the patient. The system will then perform an
analysis under consistent guidelines to establish weight data for
future measurements. Then, the process will have the patient record
his weight data and compare the data to baseline. This will permit
a determination of a percentage weight change from the base line.
In the preferred embodiment, if the percentage weight change
represents a weight greater than a set percentage for the patient,
the present invention will generate an alert.
[0063] These control limits may be based on the individual and the
population as a whole. For example, the system may identify a trend
of increasing weight for the individual or the fact that the
individuals weight has exceeded an accepted value based on the
individual's sex/height and age.
[0064] Statistical analysis for other disease conditions can be
approached in a similar manner. That is, with other diseases a
baseline for one or more parameters may be set. Frequent subsequent
data may then be collected from the patient relating to or
containing measurements of the specific parameters. Statistical
changes for parameters then may be established, based in part upon
the character of the disease process and the particular details of
the patient. The statistical changes that are used to analyze the
patient will be dependent upon the disease, the volatility inherent
in the data being measured, and other factors as known to those
skilled in the art.
[0065] In FIG. 10, exemplary screen shot 120 presents a graphical
report that the present invention may provide. As discussed
previously, the patient may choose to obtain a report by simply
pressing "REPORTS" button 82. The report may track parameter(s)
associated with the patient's clinical status. In the example
shown, a graph of the measured body weight over a specified period
of time is provided. The patient may choose the period of time
reported, such as ten days, or thirty days, or another time
interval.
[0066] In FIG. 11, screen shot 122, explains the importance of
tracking those parameter(s) to the patient. The patient may obtain
more information on the significance of the tracking of the
parameters by simply pressing "WHY THIS MATTERS" button 124.
Exemplary screen shot 122 explains the importance of tracking
weight in CHF patients and prompts the patient to call a physician
or healthcare provider if the records indicate that his body weight
is increasing. In another embodiment, the system may send an alert
to the patient's healthcare team to initiate the process where the
healthcare team then contacts the patient to schedule a physical
examination.
[0067] In FIG. 12, exemplary screen shot 130 appears when the
patient desires to exit the system. Screen shot 130 provides a
disclaimer or warning to the patient in window 132 that the program
does not replace medical care. The patient then exits the system by
clicking "EXIT" icon 134, or may return to system operations by
clicking "BACK" icon 136.
[0068] In another embodiment, the present invention takes
information from a remotely located patient for statistical and
medical analysis. The system then determines whether or not that
information indicates a worsening medical condition that may
require intervention by a healthcare professional. Instead of
treating the medical condition from a remote location by using
computers and the Internet with conventional schemes, the present
invention informs the patient and/or healthcare team of the fact
that there may be cause for additional review of the patient. This
intervention is based upon the results of statistical or medical
analysis of one or more pre-selected parameters associated with a
diagnosed condition. As a result of this notification, the system
encourages, or may actually schedule, the patient to visit a
physician or other health care professional, rather than attempting
to avoid office visits. As a result, the patient may receive more
prompt and, perhaps, more effective, less intensive medical
attention.
[0069] FIGS. 13 and 14 illustrate one embodiment of the statistical
or medical analysis step 16 of FIG. 2 performed by the present
invention. Through the analysis of a patient's condition, the
present invention determines whether a violation has occurred of
one or more rules that would give rise to an early-stage alert
condition, as stated with reference to step 18 of FIG. 2.
[0070] In essence, the calculations of the present embodiment may
be understood with reference to spreadsheet 140, which shows two
exemplary rules for which the present embodiment may test. Clearly,
although the rules here stated relate to a CHF patient, similar or
different rules could be established and tested consistent with the
scope and purposes of the present invention.
[0071] A first rule, then, for which spreadsheet 140 tests has to
do with a patient's weight gain from one day to another. Rule 1
tests the deviation in daily weight against a minimum and a maximum
weight gain. The minimum weight for which the system generates
first alert is three pounds change in body weight. This amount may
be based on such sources as the medical or scientific literature
relating to the patient's condition. The maximum weight gain in
this instance is five pounds, again, here based on the particular
patient's condition and relevant scientific or medical literature.
Rule 1 further calculates, using a value here called sigma. The
value of sigma changes according to the patient's average weight
over twenty consecutive measurements. From the sigma value a
critical difference value of 2.88 times the square root of 2, which
product is further multiplied by the relevant value of sigma value
at the time of the patient weight measurement to yield a test
value.
[0072] By initializing the below-described sigma at 0.98, an
initial critical difference of 4.0 pounds over a one-day interval,
for example, results. Thus, in the event of a weight change of 4.0
pounds, the present invention will transmit an alert to the
patient.
[0073] A second rule for which this instance of the present
embodiment tests in deviations in daily weight is also based on a
moving or rolling twenty-weight measurement set. Such a set of
measurements may be obtained, for example, through twenty days of
continual daily weight measurements. Under this second rule, the
present invention determines whether a minimum difference of two
pounds is measured. No upper limit pertains to this second rule.
The process derives a critical difference as a rolling average of
twenty measurements, but here using a seven-measurement lag and
three times the moving sigma, based on twenty prior measurements,
as specified in detail below.
[0074] For purposes of the present embodiment and in the case of
CHF, the seven-measurement lag may represent, for example, the set
of twenty measurements where the most recent measurement occurred
seven days ago and the least recent occurred twenty-seven days ago,
with daily measurements occurring each of the intervening days.
[0075] In another embodiment, a different set of measurements might
be more appropriate to take than the twenty measurements and
seven-day lag used in the CHF case. Different diseases may develop
acute exacerbations over varying amounts of time. It is important
to exclude the timeframe of the evolving change from the baseline
measurements. For example, in diabetic ketoacidosis, the time of
evolving symptoms might be three days. So, in that example, it
would be best to exclude the past three days measurements from the
baseline data. This would have the effect of assuring the most
effective early warning. In other words, data arising during the
evolution of the exacerbation will not contribute to an
artificially elevating baseline.
[0076] With more particular reference to spread sheet 140 of FIG.
13A-B and to further explain the application of the two rules
mentioned above, notice that there appears information, including
the date of a patient's weight measurement of column 142 and the
location of which the weight measurement occurred of column 144.
For the exemplary patient "Bill Price," the weight measurements
(e.g., 156 pounds taken at Dr. Minor's office on Nov. 29, 1999)
appear on column 146. Column 148 shows the results of a rolling
twenty-day average of patient Bill Price's weights (e.g., a weight
of 165.375 calculated on Oct. 1, 2002). In Column 150 appears a
further set of data which includes a rolling twenty-day average of
patient Bill Price's weight, but measured with a twenty-day lag.
That is, the data represents for the current day that information
for which the most recent of the twenty days occurs twenty days
prior.
[0077] A daily difference of measurements appears at column 152,
followed by a scalar number, in column 154, representing the
magnitude of the difference of the current day's measurement from
the lagged twenty-day measurement from column 150. Column 156
calculates the average of up to the prior twenty-days measurements
of the absolute value measurements appearing in column 154. The
values for column 158 derive from the rules, and have the column
title UCLmt, depicting a limit calculation based on the value of
3.27 times the MrBar value. Column 160 presents the number MR, as
from column 154, but here revised according to comparison of if the
MR value is greater than the UCLmt value, then the column 160 value
is given as the MrBar value. Otherwise, the process uses the MR
value for its further calculations. After twenty measurements,
column 162 presents a further revised MrBar value, similar to that
derived in column 156 and revised as the average of the past twenty
values of Revised MR of column 160. These cumulative calculations
derive the above-mentioned sigma value as the corresponding value
of the Revised MR divided by 1.128, which column 164 contains.
Then, based on the existing sigma value, the calculated value of
the above-mentioned formula of 2.88 times the square root of 2
further multiplied by the sigma value of column 164 appears in
column 166 as the critical value to be tested against. The rule one
minimum appearing in column 168 is the greater of 3 or the critical
difference value in column 166. Column 170 shows the determined
value for weight according to the first rule limit. This value
ranges from three to five pounds.
[0078] At column 172, a weight measurement moving average is taken
for use in further calculations. Column 174 shows the results of a
calculation for the moving average maximum variation from the
moving average. In column 176 appears the critical difference
calculation for the measurements against the rule two limits. The
results of passing or failing the boundaries of rules one and two
are shown in columns 178 and 180, respectively.
[0079] As should be clear from the above, the particular values for
the rules and the number of rules may change depending on the
particular chronic disease and the associated parameters for the
disease for which early detection proves beneficial. Nonetheless,
the clear import of the above description is that the present
invention, through a potentially wide variety of embodiments
provides a system and method of modeling chronic disease using a
non-linear model together with a set of optimization routines to
reduce healthcare costs and improve quality at the same time.
[0080] For many chronic conditions, the worsening of a patient's
health does not follow a predictive model, and standardized
therapies based upon broad demographic models are not suitable.
These conditions make it difficult to treat some types of chronic
diseases remotely.
[0081] In general, certain parameters are associated with certain
types of chronic diseases. For example, a patient's weight is
generally associated with congestive heart failure, whereas peak
flow is generally associated with asthma. Glucose is generally
associated with diabetes, whereas mood and depression charts are
generally associated with mental health problems.
[0082] In an embodiment, statistical models that have been applied
to chaotic systems, such as to weather forecasting by NOAA, are
applied to one or more selected parameters of the patient
associated with a chronic disease to determine the probability of
worsening medical condition of the patient. By alerting the patient
or their healthcare providers of the potentially worsening medical
condition, the condition may be diagnosed, treated and managed
early on by a healthcare professional, thereby avoiding more
catastrophic and costly medical intervention later where the
potential outcomes are not as favorable.
[0083] The methods and apparatus of the present invention, or
certain aspects or portions thereof, take the form of program code
(i.e., instructions) embodied in tangible media, such as floppy
diskettes, CD-ROMS, hard drives, or any other machine-readable
storage medium, wherein, when the program code is loaded into and
executed by a machine, such as a computer, the machine becomes an
apparatus for practicing the invention. The methods and apparatus
of the present invention may also embody the form of program code
transmitted over some transmission medium, such as over electrical
wiring or cabling, through fiber optics, or via any other form of
transmission, as known to those skilled in the art, wherein, when
the program code is received and loaded into and executed by a
machine such as a computer, the machine becomes an apparatus for
practicing the invention. When implemented on a general-purpose
processor, the program code combines with the processor to provide
a unique apparatus that operates analogously to specific logic
circuits.
[0084] FIG. 14 illustrates a typical computer system including
traditional components of a personal computer. The present
invention can have components similar to those shown, and
furthermore, through accessing the Internet, the system may
interact and interface with components on larger computers similar
to examples illustrated in FIG. 14.
[0085] A general-purpose workstation computer 190 comprises a
processor 192 having an input/output ("I/O") section 194, a central
processing unit ("CPU") 196 and a memory section 198. The I/O
section 194 is couples to keyboard 200, display unit 202, disk
storage unit 202 and CD-ROM drive unit 204. The CD unit 204 can
read CD-ROM medium 206 that typically contains programs and data
208. The disk storage unit can be, or is connected to, a database
or network server 210. The connection can be via a modem or other
digital communication devices, such as wireless receiver and
transmission components as used in PDAs and wireless communication
devices known to one of ordinary skill in the art. The database
server and network server 210 can be the same device or two
separate but coupled devices.
[0086] Computer 190 may be a network appliance, personal computer,
desktop computer, laptop computer, top box, web access device (such
as WEBTVO (Microsoft Corporation)), or any like device. Use of
computers also contemplates other devices similar to or
incorporating computers, such as personal computers, television
interfaces, kiosks, and the like.
[0087] Embodiments of the present invention may be implemented in a
standalone system, entirely on the patient's computer hard drive so
that there are no privacy or security concerns. The method
according to embodiments of the present invention does not
necessarily need a computer at all. A person may use a telephone, a
personal digital assistant (PDA), or other means to record the data
measurements described above. The patient also could be alerted by
telephone, or such other means.
[0088] The present invention provides a computer-implemented method
of impeding a progression of a disease condition or tracking the
rehabilitation of a patient in order to reduce healthcare costs and
improve patient quality of life. To accomplish this, a set of
disease or condition-associated parameters is defined. These
disease-associated parameters may be unique to a specific diagnosed
disease such as, but not limited to: congestive heart failure,
diabetes, asthma, emphysema, cancer, infarction, ischemia,
arteriosclerosis, toxicity, mental disease, depression or
arrhythmia. These parameters include but are not limited to: body
weight, peak flow, glucose, number of nitroglycerin tablets taken,
number of chest pain episodes, minutes walking without pain,
ambulation distance without pain, number of emesis, number of
episodes of diarrhea, mood charts, depression charts, and
food/liquid intake.
[0089] Once a patient has been diagnosed with a specific disease
condition, a series of repetitive measurements on a set of
disease-associated parameters associated with the patient's
diagnosed disease are collected on a frequent basis. The data may
either be automatically entered or manually entered into the
computer system shown in FIG. 13A-B. For example, as previously
discussed, the patient may enter their body weight through a
patient interface presented on the computers display, via keyboard
200, or through a data collection device, such as a scale, directly
coupled to I/O interface 194. This illustrates an objective
measurement. However, some measurements, such as those associated
with mental disease, may rely on subjective measurements taken by
the patient or health care provider.
[0090] This data may first be evaluated to eliminate data entry
errors or health problems indicated by that single data point.
Next, the computer, either locally or remotely, may perform a
series of analyses to identify potential future problems that may
require medical intervention. By early identification of these
potential problems, the patient or their healthcare provider may
apply secondary prevention techniques to address or reduce the risk
of these potential future problems. The failure to identify and
take secondary prevention actions in many cases accounts for the
"quality valley" 14 shown in FIG. 1B that is associated with
increased medical costs and reduced outcomes. Early identification,
and proactive measures helps to reduce medical costs and increase
the likely of favorable outcomes, or impede the progression of a
disease. The failure to address these potential problems will often
result in later more invasive medical intervention with less
favorable outcomes. This condition often accounts for "quality
valley" 14.
[0091] The statistical or medical analysis performed on the data
may compare the data to predetermined control limits, trend
analysis, tests for special cases, such as the Western Electric
Rules, or other such analyses as are known to those skilled in the
art.
[0092] The present invention may be implemented by a computer
program executed within a computer, such as a personal computer,
personal data assistant, network appliance, web access device,
computer kiosks, television interfaces or like device. The program
may comprise instructions that enable to processor to perform the
tasks of: (1) collecting and evaluating the repetitive measurements
supplied by the patient or healthcare provider; (2) performing
statistical analysis on a series or history of repetitive
measurements; and (3) alerting the patient or health care provider
to those analyses which indicate a potential future problem. These
steps allow the patient and/or healthcare team to apply secondary
prevention techniques that address the potential future problem.
Thus, allowing the patient to enjoy a more favorable outcome and
reduced health care expense.
[0093] In one embodiment, a computer performs the process of
collecting clinical parameters, processing, data alerts, and
subsequent data. Another embodiment uses an automated telephone
system coupled to a computer system. In such a system, a patient
"signs up" for the service and receives a password. As part of the
sign-up process, the patient's disease, home phone number and
preferred call times are submitted. The automated system then calls
the patient on a predetermined schedule. A computer-generated voice
asks for the patient's password, and then prompts entry of the
patient's data. For example, in the case of congestive heart
failure, the collected data is body weight. The automated system
verifies the integrity of the data and ends the phone call. An
"alert" advises the patient of an abnormal reading, and may
transfer the patient directly and automatically to a physician's
office, answering service, or other requested number. The automated
system may also automatically re-call the patient to confirm
understanding of the alert.
[0094] Such a system may use voice recognition and synthesis in all
or part as the patient interfaces. Similarly, other information
transactions can be accomplished on various wireless and PDA-type
devices.
[0095] From the above description of the invention it is manifest
that various equivalents can be used to implement the concepts of
the present invention without departing from its scope. Moreover,
while the invention has been described with specific reference to
certain embodiments, a person of ordinary skill in the art would
recognize that changes could be made in form detail without
departing from the spirit and the scope of the invention. The
described embodiments are to be considered in all respects as
illustrative and restrictive. It should also be understood that the
invention is not limited to the particular embodiments described
herein, but is capable of many equivalents rearrangements
modifications, and substitutions without departing from the scope
of the invention.
* * * * *