Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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CHRONIC DISEASE OUTCOMES, EDUCATION AND
COMMUNICATION SYSTEM
CROSS REFERENCE TO RELATED APPLICATIONS
Priority is claimed from provisional application Serial No. 60/270,329, filed
February 21, 2001.
COPYRIGHT NOTICE
A portion of the disclosure of this patent document contains material that is
subject to copyright protection. The copyright owner has no objection to the
facsimile
reproduction by anyone of the patent document or the patent disclosure, as it
appears in
the Patent and Trademark Office patent files or records, but otherwise
reserves all
~ 5 copyright rights whatsoever.
FIELD OF THE INVENTION
This invention relates to data processing systems, and more particularly, to
an
2o integrated system and method for managing chronic disease that allows
patient outcomes
analyses, communications, and education.
BACKGROUND OF THE INVENTION
25 The concept of cost containment and efficiency of medical care services,
commonly known as managed care, has taken on significant importance in the
health care
industry. Pay providers, in the form of employers, government agencies,
insurance
companies, health care maintenance organizations, and the like, frequently set
forth a
series of thresholds which must be established before a patient may have
covered access
30 to medical services. Communication of the patient's etiology, treatment
plan and
updating any changes thereto, is tremendously cumbersome, requiring countless
hours by
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medical providers and their staff to insure this information is organized and
accurately
communicated to the pay provider, as well as the patient, so that the patient
may access
covered services and optimize treatment. Further, it is often difficult for
the medical
provider and/or pay provider to measure the success of the services rendered
to the
patient and/or the patient's own follow up with the treatment plan.
Certain chronic diseases, such as diabetes, have known etiologies and
associated
risk factors. Guidelines for treatment have been promulgated by, e.g. the
American
Diabetes Association, the National Commission for Quality Assurance (NCQA) and
Diabetes Quality Improvement Project (DQUIP). These guidelines incorporate
known
complications associated with diabetes such as retinopathy, neuropathy,
nephropathy,
Pulmonary Vascular Disease (PVD), Cardial Artery Disease (CAD), and cerebral
vascular disease. In addition to various tests associated with monitoring the
diabetes,
such as HbAlc (measuring glycosolated hemoglobin levels), microalbumin (blood
I 5 protein), lipids (cholesterol), etc., the physician must typically perform
routine eye and
foot examinations to monitor the progress of the disease. These tests are in
conjunction
with those examinations normally associated with an office visit, i.e. blood
pressure,
temperature, weight, pulse, etc. In addition, there is a significant education
and behavior
component to the treatment of the disease which can encompass such items as
nutrition
2o counseling, smoking cessation, and self education about the disease. The
Center for
Disease Control estimates that diabetes is reaching epidemic proportions in
the United
States. Effective treatment centers on the known parameters and risk factors
associated
with the disease, and insuring that the patient is meeting the objectives of
the treatment
plan.
The patient's ability to self monitor blood glucose values at home has
significantly improved the ability of the patient (and medical provider) to
control the
progress of the disease. Hand held monitoring units, such as disclosed in U.S.
Pat. No.
4,731,726 to Allen, III, allows the patient to have a portable monitor which
generates test
3o values for the blood glucose level and stores the test results. The data
may then be
downloaded and/or transferred to a computer. The monitor may generate a
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recommendation to the patient based on patient data, physician input data, and
test
results, such as an increased insulin dosage. U.S. Pat. No. 5,251,126 to Kahn
et al
illustrates another diabetes data analysis and interpretation method that
identifies insulin
intake regimens and identifies statistically significant changes in blood
glucose levels in
relationship to the insulin levels.
The use of computers to generate a patient record registry and to record data
associated with the treatment of those patients enhances the provider's
ability to assess
the patient's health and generate an assessment plan. U.S. Pat. No. 5,262,943
to Thibado
et al discloses a system that receives standardized test data as well as a
therapists's
subjective evaluations to generate an assessment report for the care of an
individual in the
mental health field. U.S. Pat. No. 5,265,010 to Evans-Paginelli discloses a
hospital
patient document method and apparatus that is used to generate an initial
patient health
care plan, identifying the patient's problems, expected outcomes and
interventions to
achieve those outcomes.
The use of statistical analysis to create a diagnostic model for a given
disease has
been employed to create trained neural networks. U.S. Pat. No. 5,769,074 to
Barnhill et
al, discloses a computer based method which employs the steps of collecting
data about
2o patients (such as biological, physical, demographic, racial,
environmental); digitizing the
data and medical historical data; selecting digitized values that are
associated with the
diagnosis of a disease; scaling the data; performing tests to analyze the
discriminating
power of the data; grouping individual data values; preprocessing the data;
inputting
selected data to make pre-processed values into a computer based neural
network in order
to train the neural network; analyzing the contributions of the individual
data inputs to the
network; selecting the optimally trained neural network based on the
performance,
accuracy and cost; and inputting other patient data into the neural network to
produce an
output value which indicates whether the patient may have or be susceptible to
the
disease. Such technology has application to diagnostic patterns that are too
subtle or too
complex for humans and conventional computational methods to identify and
allow for
the provider to access large neural networks that are capable of recognizing
diagnostic
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patterns. U.S. Pat. No. 5,860,917 to Comanor, et al, discloses such a neural
network with
a statistical model derived using a robustified similarity metrical least
squares (SMILES)
analysis.
In contrast to the neural network developed through statistical analysis of
patient
data and risk factors to create a diagnostic protocol, certain chronic
diseases, such as
diabetes, have a known and highly defined treatment protocol. Though
incurable, the risk
factors associated with diabetes and the complications of diabetes have been
well studied.
The diabetic patient, however, must be closely monitored to control the
disease. It is
1 o estimated, however, that physicians associated with the treatment of
diabetes do not use
computer based data systems to manage and maintain their files with respect to
the
diabetic patient. Indeed, it is estimated that less than ten percent (10%) of
all physicians
use computers in the treatment of their patients for purposes other than
billing.
According to the Center for Disease Control (CDC), advances in diabetes
research
now provide the clinical and therapeutic means to improve outcomes for people
with
diabetes. The 1993 landmark study, the Diabetes Control and Complications
Trial
(DCCT), conclusively showed that improved glucose control can retard the onset
and
progression of diabetes complications affecting the eyes, kidneys, and nerves.
A second
study in the United Kingdom, entitled United Kingdom Prospective Diabetes
Study
(UKPDS), released in 1998, confirmed the results of the DCCT and left little
doubt about
the benefit of lowering blood glucose levels as close to normal as possible.
In addition,
new medications are available to lower blood glucose and methods for improving
glucose
levels have greatly improved. The key factor in accomplishing improved results
is being
able to support the delivery of care that is based on achieving these clear
and critical
goals.
For providers of diabetes care, these two recently completed studies have now
established that there is great personal and economic benefit for diabetic
patients to
reduce and maintain blood glucose levels as close to normal as possible. For
people with
Type 2 diabetes, who constitute 90-95% of all diabetic patients, (ADA),
aggressive
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reduction and control of blood glucose levels reduces the risk of blindness
and kidney
failure by 25%. For patients who also have high blood pressure and
aggressively reduce
it, major reductions in risk of stroke (44%) and heart failure (56%) can be
achieved.
(UKPDS Preliminary Results 1998).
With the scientific basis supporting the need for as close to normal blood
glucose
control now established, the opportunity to improve results begins in an
environment that
currently falls far short of this goal. The need for great improvement in
diabetes care is
evidenced by the following assessment from CDC: "Nonetheless, research
advances in
1 o diabetes are not being communicated effectively and diabetes is not being
managed
aggressively. The U.S. is far from reaching the objectives set in the U.S.
Department of
Health and Human Services' Healthy People 2000. Physician practices often do
not meet
recommended standards of diabetes care. Many patients do not manage their
diabetes
well. Furthermore, the health care system, which is designed to treat acute
and episodic
15 illnesses, is poorly equipped to manage a complex, multi systemic chronic
disease like
diabetes . . . "
HEDIS (Health Plan Employer Data and Information Set) serves as the clinical
performance measurement and data repository for private and federal health-
care buyers.
20 HEDIS is a database of quality measures developed by NCQA and used as a
standard
evaluation tool for health plans. National quality reporting has established
that the
patient eye exam, the initial and single standard quality measure for
diabetes, is still not
completed each year for more than half of all patients. Without tools to plan
for the care
and to collect and monitor data, diabetes care providers continue to struggle
to improve
25 their performance with this single basic measure.
Thus, what is needed is a data processing system and method for managing
diabetes care where utilizes known medical standards adopted by the American
Diabetes
Association, among others, to customize a treatment plan, which can interface
with the
3o physician, health care plan and patient, and defines a set of criteria
which defines a high
risk patient and which continually monitors the patient, setting forth alarms
when the
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patient fails receive a planned examination or service and/or the examination
does not fall
within an expected range.
SUMMARY OF THE INVENTION
The present invention is an improvement of the type disclosed in U.S. Patent
No.
6,277,071 to Hennessy et al., also assigned to the assignee of this
application. The
present invention provides a system that performs real-time monitoring,
analysis, and
reporting of patient outcomes, illustrating and substantiating the clinical
effectiveness of
medications, devices, and other chronic disease clinical and educational
interventions.
This automated process analyzes changes over time in software defined and
stored
clinical indicators. Outcomes are monitored, analyzed, and reported for single
patients or
patient populations. Patient data can be aggregated from multiple locations,
and standard
or custom (both tabular and graphical) reports can be generated. Aggregated
data is
~ 5 stripped of patient identifiers to protect confidentiality.
Moreover, the system enables tracking of educational intervention outcomes. By
tracking changes in patient clinical measures, the effectiveness of the
educational
intervention can be measured empirically. The system also automates the
development
and updating of individualized assessment and educational plans for each
patient.
The system produces automated, context-driven patient communications and
education. It will produce multi-language correspondence that reminds patients
about
planned care events and offers results-based feedback about the most recent
changes in
their glycemic control. Digitally stored education materials are produced and
scheduled
for patient delivery based on a care plan customized for each patient.
The system integrates comprehensive clinical management with the delivery and
assessment of patient education, consistent with the National Standards for
Diabetes Self
3o Management Education and other national standards.
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The above discussed and other drawbacks and deficiencies of the prior art are
overcome or alleviated by the chronic disease monitor of the present
invention.
According to the present invention, there is provided a system for monitoring
a
chronic disease including a database for storing a plurality of patient data
entries. Each
of the patient data entries includes personal information of a patient and a
set of
guidelines concerning the patient's care. A user interface is included for
displaying the
patient data entries stored in the database and entering the patient entries
for storage in
the database. A processor retrieves the patient data entries selected by the
user interface
from the database and stores the patient data entries in accordance to an
algorithm. The
algorithm comprises a plurality of rules for comparing patient data entries to
the
guideline to determine whether a test threshold has been exceeded
The processor separates the patient entries designated by the user according
to a
test threshold stored in the guideline. The test thresholds represent known
parameters
associated with the chronic disease, such as blood glucose, lipids, liver
enzyme, and
microalbumin for the disease of diabetes. If the test threshold value derived
from the
guideline is exceeded, an alert sequence is activated, in which the patient is
categorized
as a high risk patient, the physician is notified, the patient is notified,
the health care
provider is notified, and the patient's treatment plan is altered to treat the
high risk
patient.
In accord with the present invention, an integrated patient monitoring,
communication, and education system comprises a database for storing a
plurality of
patient data entries, each of the patient data entries including personal
information of a
patient and a guideline concerning the patient's care, the guideline including
a default test
value associated with monitoring the chronic disease, a user interface for
entering patient
entries for storage in the database and queries related to patient monitoring,
communication and education, and a processor for retrieving the patient data
entries from
the database in response to the queries from the user interface and storing
the patient data
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entries in the database. The processor provides patient and population
outcomes analysis
in accordance with an algorithm.
Also in accord with the present invention, a chronic disease outcomes,
communication, and education system comprises a database for storing a
plurality of
patient data entries, each of the patient data entries including personal
information of a
patient and a guideline concerning the patient's care, the guideline including
a default test
value associated with monitoring a chronic disease, a user interface for
entering patient
entries for storage in the database and queries into the database related to
patient
monitoring, communication and education, and a processor for retrieving the
patient data
entries from the database in response to the queries from the user interface
and storing the
patient data entries in the database. The improvement comprises a computer
program
embodied on a computer-readable medium for operating the processor to provide
patient
and population outcomes analysis according to an algorithm.
Further in accord with the present invention, a computer program is embodied
on
a computer-readable medium for operating a computer system including a
database for
storing a plurality of patient data entries, each of the patient data entries
including
personal information of a patient and a guideline concerning the patient's
care, the
guideline including a default test value associated with monitoring a chronic
disease, a
user interface for entering patient entries for storage in the database and
queries into the
database related to patient monitoring, communication and education, and a
processor for
retrieving the patient data entries from the database in response to the
queries from the
user interface and storing the patient data entries in the database. The
computer program
includes a subroutine for operating the processor to provide patient and
population
outcomes analysis according to an algorithm.
Still further in accord with the present invention, a chronic disease
outcomes,
communication, and education system includes a database for storing a
plurality of
3o patient data entries, each of the patient data entries including personal
information of a
patient and a guideline concerning the patient's care, the guideline including
a default test
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value associated with monitoring a chronic disease, a user interface for
entering patient
entries for storage in the database and queries into the database related to
patient
monitoring, communication and education, and a processor for retrieving the
patient data
entries from the database in response to the queries from the user interface
and storing the
patient data entries in the database. The improvement comprises a computer
data signal
embodied in a carrier wave representing sequences of instructions which, when
executed
by the processor, cause the processor to provide patient and population
outcomes analysis
according to an algorithm
1 o Still further in accord with the present invention, in a computer
processing
system, an integrated method for monitoring, communicating with and educating
a
patient with a chronic disease comprises the steps of storing a plurality of
patient data
entries, each of the patient data entries including personal information of a
patient,
storing a treatment guideline defining at least one treatment and a
predetermined
15 treatment monitoring threshold for the treatment, measuring a condition of
the patient in
accordance with the treatment, selectively querying the database for factors
related to
patient and population outcomes analysis in accordance with an algorithm, and
retrieving
the patient data entries from the database in response to the queries from the
user
interface and storing the patient data entries in the database.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. I is a block diagram of a chronic disease monitor of the type embodying
the
invention.
FIG. 2 is a block diagram illustrating a patient record.
FIG. 3 is a graphical window displayed to a user entering a patient record.
FIG. 4 is another graphical window displayed to a user entering a patient
record
for complications.
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FIG. S is another graphical window displayed to a user entering a patient
record
for office visits.
5 FIG. 6 is another graphical window displayed to a user entering a patient
record
for a patient quality plan.
FIG. 7 is another graphical window displayed to a user entering a patient
record
for a patient therapy plan.
FIG. 8 is a graphical window displayed to a user entering a provider record.
FIG. 9 is a graphical window displayed to a user entering a health plan
record.
FIG. 10 is an algorithm for creating the guideline applied to the patient data
record.
FIGS. 1 lA-11C is an algorithm illustrating the application of the guideline
to the
patient record.
record.
record.
3o record.
FIG. 12 is a graphical window displayed to a user entering a risk manager
record.
FIG. 13 is another graphical window displayed to a user entering a risk
manager
FIG. 14 is another graphical window displayed to a user entering a risk
manager
FIG. 15 is another graphical window displayed to a user entering a risk
manager
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FIG. 16 is a graphical window displayed to a user entering a provider record.
FIG. 17 is a graphical window displayed to a user entering a quality report.
FIG. 18 is a graphical window displayed to a user entering a high-risk patient
report.
FIG. 19 is a graphical window displayed to a user entering a quality report by
provider.
FIG. 20 is a graphical window displayed to a user illustrating warning
symptoms
and signs for diabetic foot problems.
FIG. 21 is a graphical window displayed to a user entering a patient history
record.
FIG. 22 is a block diagram representing the logic sequence for generating a
high-
risk rule.
FIG. 23 is a continuation of the sequence of FIG. 22.
FIG. 24 is a block diagram representing the logic sequence for a generation
liver
enzyme testing high-risk rule.
FIG. 25 is a block diagram representing the logic sequence for a patient
outcome
analysis.
FIG. 26 is a block diagram representing the logic sequence for patient
communications.
FIG. 27 is a block diagram representing the logic sequence for patient
education.
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FIG. 28 is a graphical window displayed to a user for entering a patient
medication record.
FIG. 29 is a graphical window displayed to a user for entering clinical
measure
criteria for a patient.
FIG. 30 is a graphical window displayed to a user showing the effects of the
clinical measure criteria of FIG. 26.
to
FIG. 31 is a graphical window displayed to a user showing a medication outcome
for a selected medication.
FIG. 32 is a graphical window displayed to a user showing another medication
outcome for another medication.
FIG. 33 is a graphical window displayed to a user showing an outcome for
patient
weight versus educational intervention.
2o FIG. 34 is a graphical window displayed to a user showing patient
information.
FIG. 35 is a graphical window displayed to a user showing an education content
summary for patient medical history.
FIG. 36 is a graphical window displayed to a user showing an education content
summary for patient diabetes history.
FIG. 37 is a graphical window displayed to a user showing an education content
summary for patient social history.
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FIG. 38 is a graphical window displayed to a user showing an education content
summary for patient education intervention.
FIG. 39 is a graphical window displayed to a user for entering an
intervention.
FIG. 40 is a graphical window displayed to a user showing an education content
summary for patient evaluation progress.
FIG. 41 is a graphical window displayed to a user showing further details of
the
education content summary for the patient evaluation progress of FIG. 38.
FIG. 42 is a graphical window displayed to a user for entering a diabetes
registry.
FIG. 43 is a graphical window displayed to a user for entering information in
a
diabetes registry.
FIG. 44 is a graphical window displayed to a user for entering information
regarding an educational class.
FIG. 45 is a graphical window displayed to a user for showing information
regarding class schedules.
FIG. 46 is a graphical window displayed to a user for showing information
regarding class instructors.
FIG. 47 is a graphical window displayed to a user for entering more detailed
information regarding the instructors of FIG. 44.
FIG. 48 is a graphical window displayed to a user for showing information
regarding class attendance.
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FIG. 49 is a graphical window displayed to a user for creating patient
communications.
FIG. 50 is a graphical window displayed to a user for showing patients
selected
by certain criteria.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
Refernng to FIG. 1, a chronic disease monitor in accordance with a preferred
embodiment is generally shown at 10. Chronic disease monitor 10 includes a
central
database 12 that electronically stores chronic disease information and enables
a system
user to access the stored information to monitor a chronic disease. Central
database 12
includes computer memory in the form of RAM and ROM memory and is located in
the
computer hardware or deposited on a readable storage media. Guideline 14
comprises an
algorithm representing known parameters of a chronic disease, including risk
factors and
complications associated with that disease, may be tailored by the medical
provider to
implement a facility wide treatment plan to a given patient population as well
as on an
individual patient basis. Patient record 16 information, such as demographic
information
100 and insurance information 102, is inputted by user at user terminal 18,
such as a
computer terminal, a personal computer interfaced within a local area network,
and the
like. Site information 17 comprises data associated with the location of the
installation
(e.g. location, licensee, etc.). Patient information 16 is updated in a
variety of ways. For
example, a user may enter progress notes and/or test results at user terminal
18. Meter
device 20, such as a blood glucose monitor, may provide test results in
electronic data
form. Processor 22 comprises a central processing unit, such as a
microprocessor, which
stores and accesses the information in central database 12 (such as a patient
record 16).
Database interface 13 comprises a plurality of operating systems and programs
allowing
monitor 10 to store and retrieve data stored in database 12. Patient record 16
is applied to
an algorithm within guideline 14. If a test result exceeds an expected
threshold, an alert
3o is generated and a notation is stored in risk manager 24. The alert may be
communicated
to an off site location 26, e.g. via e-mail 27, such as to an employer, health
maintenance
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organization and the like, and/or a letter may be printed to the patient via
printer 28.
Additionally, if a patient fails to attend a scheduled service, an alert is
similarly
generated. Processor 22 may optionally be linked to a central database 29
(offsite) via a
TCP/1P link as is known in the art.
Provider information 30 (e.g., a physician) and health plan information 32 are
also
stored in central database 12, to enable communication with medical providers
and third
parties. While the chronic disease monitor of the present invention may be
used for other
chronic diseases, chronic disease monitor 10 is particularly relevant with
respect to
10 diabetes and therefore, hereinafter, the chronic disease monitor will be
described with
respect to the monitoring and control of diabetes.
Referring now to FIG. 2, patient record information 16 is generally shown in
block diagram form and is described as follows. Monitor 10 incorporates a
window
15 format and is programmed in Microsoft Visual Basic~ to operate in a
Windows~
environment. It will be appreciated by those of ordinary skill in the art that
other
programming formats and/or languages may be employed. Patient record 16 is
entered
by a user at user terminal 18 and includes the patient's demographic
information 100 e.g.,
salutation, name, gender, year of diagnosis, diabetes type (type 1, type 2,
gestational),
2o address, contact information (e-mail, work and home phone), initiation of
care date,
health plan, health plan id, provider, employer and language. Insurance
information 102
is also recorded in patient record 16. An identifying number for the patient
is stored in
the database. Additionally, complications, risk factors/co-morbid conditions
104 such as
retinopathy, neuropathy, nephropathy, PVD, CAD, and cerebral vascular disease
are
recorded.
Patient record 16 also include test data 106. Test data 106 comprises the
office
visit date, practitioner, office visit comments, such as progress notes and
patient
concerns, are recorded. Clinical information, i.e. weight, height, blood
pressure, smoking
3o status, blood glucose recordations (SMBG), lipids profile, liver enzyme,
foot exams,
neuropathy, skin condition, eye exam, are stored. It will be appreciated to
those skilled in
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the art that the blood glucose information may be entered manually or
electronically
transferred from a blood glucose-metering device 20, such as a Life Scan
OneTouch.
Data may also be transferred directly from a laboratory, such as via an RS-232
port or
TCP/1P (FIG. 1) in HL7 (or other standard data format). Quality of life
indicators, such
as number of emergency room visits, days of hospitalization, days lost from
work, and
activities, provide important outcome information. By storing this information
in patient
record 16, reports may be generated comparing changes in these factors over a
given
period of time and/or for a selected treatment therapy. Combinations may be
applied.
Further, a patient's own self assessment is recorded as diabetes is such that
success in
treatment is heavily dependant on the patient's active participation.
Patient record 16 also includes a quality plan 110. Monitor 10 generates
quality
plan 110 from a selected guideline 14 and allows the user to customize the
quality plan
by selecting frequencies, thresholds and goals for a series of tests, which
are required to
be performed on the patient, setting alert values if thresholds are exceeded
or if tests are
not undertaken. For example, tests for HbAlc, lipids (to measure cholesterol),
blood
protein (microalbumin), eye and foot examinations are recommended by the
American
Diabetes Association. As described in greater detail below, the frequencies
for these
examinations are defaulted to the recommended ADA values (but may be over-
written by
the user). Additional tests may be programmed, such as a stress test for
cardiovascular
disease. The frequency of office visits may be stored. Monitor 10 notifies
providers,
health care plans, and patients via letters, e-mail, etc. Letters may be
stored in the form
of reminders, and/or report letters, indicating test results, a missed
appointment, an alert
and the like. Patient services 108 including self education, nutrition
counseling, smoking
cessation; patient satisfaction, flu vaccine, and pneumonia vaccine are also
stored in
patient record 16. Patient record 16 also includes a patient's medications,
therapies, and
treatments (such as medication, dosage, frequency start date, a nutrition plan
and exercise
plan).
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It will be appreciated by those of ordinary skill in the art that the window
environment allows the user to access this information from window to window
and that
additional information may be optionally stored "behind" the window in layered
fashion.
As shown in FIGS. 3 through 7, patient data 16 is presented to a user in a
window
format, though other known program formats may be used. FIG. 3 illustrates the
patient
setup, where the user may input the patient's demographic information 100,
such as
salutation, name, gender, date of birth, year of diagnosis, diabetes type,
address, contact
information, registry ID, health plan, quality guideline, provider, employer
and language.
FIG. 4 illustrates a second portion of the patient setup where the user may
select
complications, risk factors/co-morbid conditions 104 which the patient
suffers, such as
retinopathy, neuropathy, nephropathy, PVD, CAD and cerbro vascular diseases.
FIG. 5 illustrates a window, which is prompted when office visit data is
entered
into patient record 16. The user may enter the office visit date,
practitioner, weight,
height, blood pressure, smoking status, blood glucose (SMBG) and daily range,
foot
exam (PVD, neuropathy, poor skin condition, podiatric referral), quality of
life indicators
(number of emergency room visits, days of hospitalization, days lost from
work) and the
patient self assessment. FIG. 6 illustrates a window, which is prompted for
the creation
of a patient quality plan 110. The tests to be preformed on the patient are
selected for
enablement, frequency, alert (where a value is exceeded), threshold, and goal.
As
described in greater detail below, the values for the threshold default to the
guideline
value located in guideline 14 generated for the patient population in risk
manager 24.
The user may enter a different value for a given threshold and override the
guideline
default. The user is prohibited from entering a threshold value which would be
impossible (outside of permissible test ranges, for example) and which is
greater than the
patient population threshold. FIG. 7 illustrates a current therapy plan data
record in
patient record 16 as presented to the user in a window format. The user may
input
comments. The information is classified by medication type, medication,
dosage, and
frequency and start date. The nutrition plan summary and/or exercise plan
summary may
also be entered.
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Referring now to FIG. 8, provider information, such as for a physician,
including
the name, address, identification number, contact information, beeper number
and the
like stored in database 12 as provider record 30 is generally illustrated. The
user is
prompted with a window, which allows the user to enter the information. FIG. 9
illustrates a window for receiving health plan records 32. The user may input
comments
concerning specific policies, which are recorded in health, plan record 32.
Referring again to FIG. 1, guideline 14 comprises an algorithm, which
represents
the diabetic treatment model recommended by the ADA. Guideline 14 represents
the
recommended tests (and frequencies), alert thresholds, and goals for the care
of the
diabetic patient. A user may use an ADA default, may program a different set
of
thresholds for a patient population, and may adjust the parameters for each
patient to
establish a quality plan. The ADA publishes standards entitled HEDIS (Health
Plan
Employer Data and Information Set) 3.0, HEDIS 99, and the ADA Provider
Recognition
Program. These standards contain recommended (and accepted) treatment
schedules for
the diabetic patient. For example, HEDIS 99 requires quarterly HbAlc testing,
annual
eye exams, annual lipid profile, and annual microalbumin exams. Typically, the
alert
threshold for the HbAlc test is 9.5%, with a goal of at least 7.0%. Similarly,
the annual
lipid profile typically has a goal of 130 Mg/dl and an alert threshold of 160
Mg/dl.
Microalbumin exams have a goal of 9, with an alert threshold of 25 Mg/L. Thus,
these
parameters are incorporated into a rule structure for the monitoring of the
diabetic patient.
As described in greater detail below, the user may select a rule for the
treatment of the
patient population. For example, the data may sorted for all patients having
hypertension
and having a blood glucose test level exceeding 9% HbAlc. For each patient
data entry, a
comparison is made between a guideline value (measure value) in guideline 14
and the
test data from patient record 16 to determine if the rule is satisfied and/or
whether a
thresholdlevelhas been exceeded.
Turning now to FIGS. 10, 1 1A, 11B, and 11C, the logic sequence of guideline
14
and risk manager 24 will be explained in greater detail. The ADA has published
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recommended guidelines for the treatment of diabetes. These guidelines are
based on
years of study of the disease and comprise the recommended treatment for
individuals
suffering from the disease. For example, the HEDIS 99 guideline sets as an
alarm
threshold for microalbumins greater than 25 Mg/L, which indicates a patient
who
requires immediate medical assistance. This value is stored in guideline
record 14 as a
default value. The user may select a default guideline, such as the ADA HEDIS
3.0,
HEDIS 99, and/or ADA Provider Recognition Program. Alternatively, the user may
create a guideline for any and/or all of the measures (HbAlc, eye exam, lipid
(LDL),
microalbumin, liver enzyme, self education, nutrition counseling, smoking
cessation,
1 o patient satisfaction, flu vaccine, and pneumonia vaccine. The user may
also modify the
ADA rule and enter a new threshold limit in place of the ADA guideline default
in
guideline 14 for a given patient population, e.g. for all patients covered
under a certain
plan, associated with a certain physician, and the like. Additional rules,
representing
threshold values, may be selected. For example, patients with HbAlc value of
greater
than 9.5%, or patients who have not been tested, who also have an associated
risk factor
for hypertension, may be selected by the user to create a rule within
guideline 14 to be
applied to the database 12. A HbAlc reading of 9.5% or a patient having the
risk factor
of hypertension would mean that the patient is at high risk and needs
immediate medical
treatment. Incorporated in the guideline 14 are known parameters for the
disease of
diabetes so that a user may not input a value which would be outside of
possible testing
values. If the ADA guideline value is not adjusted, then it will be the
default value for
the individual patient for the generation of a patient quality plan.
Alternatively, the
patient population default value may be adjusted for an individual patient for
incorporation into the patient's quality plan. The value assigned in the
patient quality
plan is the measure value against which test results and clinical events are
compared.
A test result may be communicated from Laboratory 34 via an RS-232 port
directly to the processor 22, may be blood glucose data generated from a hand
held blood
glucose meter device 20, and/or may be manually inputted by a user at user
terminal 18
and recorded in record 16. A clinical finding or notation, such as a missed
service, a new
complication, a measurement and the like may also be entered and stored to be
applied to
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the guideline 14. The value is compared against the measure value derived from
the
patient's quality plan 110. If the test result/clinical event exceeds the
expected measure
value (or a scheduled service or event is missed or omitted), a series of
program functions
are performed. The functions resulting from the threshold value being exceeded
and/or
5 from the application of a rule may be generally described as an action
sequence. Whether
the action sequence is activated or no action is taken, the patient's record
16 is updated to
reflect the test resuldclinical event. If the alert function has been selected
in the patient
record 16, an alert for the patient to alert the system manager and/or medical
provider/physician of the event is registered. Also, the patient's name is
added to the risk
manager 24, a letter is generated to send to the patient (and/or another
physician or
caretaker), the information is communicated off site 26, such as to a health
maintenance
organization, provider, and the like. Also, the patient's quality plan 110 in
patient record
16 may be updated to reflect the necessity for additional tests and/or a
different frequency
or parameter for the tests as a result of the exceeded threshold. The clinical
schedule is
15 also updated to reflect the patient's need for additional services.
Alternatively, a report
letter, with encouragement concerning the test result, and/or explaining the
test result,
may be sent to the patient.
Refernng now to FIGS. 1 1A through 11 C, the user logs onto the system from
user
2o terminal 18 and selects an action. The user may access the data records
stored on central
data base 12 and/or may chose to enter data (a patient record 16 is then
selected). The
test type is selected. The test date is then entered. If the date is invalid,
i.e. a date in the
future or an impossible date, e.g. 1867, Monitor 10 will prompt the user to
reenter. The
user then enters the test value. If the test value is outside an acceptable
range (known
physical parameters) the user is prompted to again reenter. Similarly, if the
test value
exceeds normal values, but possible values, the user is prompted to confirm
the value. If
the data is entered via a meter/device 20 or other electronic device, an alert
report is
generated (if the data exceeds known parameters). After the data is stored it
is compared
to the guideline value (measure value). As set forth above, the guideline
value may be a
threshold for a test result and/or may comprise a rule combining a test
threshold and a
complication. If the threshold is exceeded and/or if the rule is satisfied,
the action
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sequence is undertaken (i.e. alert, letter, offsite, update quality plan,
update clinical
schedule, add to risk manager, etc.). The data is then measured against the
high-risk
monitor. If the high-risk threshold is crossed, the high-risk status is
updated. Next, the
planned events data within the patient record 16 is updated based on the
criteria for the
quality plan. For example, if the test is scheduled "as needed," the user is
prompted to
select a date. If there is another selected frequency for the test, such as
quarterly,
Monitor 10 will automatically schedule the test. If the test result
communications is
enable, the patient then receives notification of the test result and/or new
test date. If it is
a test result communication, the trend is determined (positive/negative).
Next, the patient
e-mail enablement is determined. If yes, the communication is e-mailed. If no,
a letter is
generated. Patient record 16 is then updated.
Thus, for example, a user selects the high-risk monitor in the menu driven
format
at user terminal 18 and adopts a rule for HbAlc >_ 9.5% (or untested) for
patients with the
~ 5 risk factor of hypertension. As shown in FIG. 12, which illustrates the
data entry prompt
for the user when accessing the risk manager 24, the user may create a series
of rules to
be applied to a patient population by entering a threshold, risk factor and
the like. For
example, as illustrated in FIG. 12, if Rule 1 set the threshold for
microalbumin levels
>50.0 mg/L and Rule 2 set HbAlc>9.5% (or untested) for patients with
hypertension, if
2o either condition is met, the patient's name will be added to the high risk
patient list (and
the action sequence will be applied). Thus, if the microalbumin level exceeds
50 mg/L
(Rule 1 ) or if the HbAlc test value exceeds 9.5% and the patient has the
complication of
hypertension (Rule 2), or if the patient has not been tested (Rule 2), the
patient's name is
added to risk manager 24. The action sequence is also initialized (i.e.,
alert, quality plan
25 is updated to reflect the need for additional services, such as greater
frequency in testing
blood glucose, information is sent off site 26 to a pay provider, employer,
health
maintenance organization and the like, a letter is generated to the patient,
and the
appropriate physicians receive an alert concerning the test result/clinical
event). If a test
result/clinical event was less than the measure value of less than 50.0 mg/L
microalbumin
30 (Rule 1) and 9.5% for HbAlc (Rule 1), patient record 16 is updated and the
action
sequence is not executed.
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Referring to FIGS. 13 and 14, risk manager 24 data is inputted by a user at
user
terminal 18. A window may be employed to prompt the user with respect to the
data to
be entered. The user may add a rule, deleted a rule, analyze the patient
records 16 by
rule, and change a rule.
Referring to FIG. 15, an example of the type of report, which may be generated
as
a result of the chronic disease monitor 10, is illustrated. It will be
appreciated to those of
ordinary skill in the art, that by applying the guideline and logic sequence
described
t 0 herein, that various reports may be generated to assist the physician,
patient, and/or pay
provider to monitoring the chronic disease. The user, via user terminal 18,
selects the
menu setting forth the provider record 30, which associates information with
respect to a
medical provider such as a physician. For the physician, the alerts are
categorized by
patient, date, test type, detail (goal, threshold, result). Reminders are also
listed for the
~ 5 respective physician, indicating the date created, schedule, patient name,
author, and the
subject. As illustrated in FIG. 16, the patient population may be viewed
globally, settling
forth the number of patients seen by the provider, the test frequency, test
results, with
graphical illustrations. Quality reports for patients, setting forth the
patient population,
including the category of diabetes, the number of patients and percentage of
the patient
2o population, the tests undertaken on the patient population, and the average
result of those
tests, are illustrated in FIG. 17. As shown in FIG. 18, a high-risk patient
list can be
generated for distribution to providers, payers, etc. The background criteria
are stored
behind each high-risk patient in window format. As illustrated in FIG. 19, a
quality
report by provider, setting forth the category, patients, percentage of
patients by diabetes
25 type, test (eye exam, foot exam, lipids, etc.) may be accessed.
As shown in FIG. 20, additional information, such as warning signs concerning
symptoms and signs of foot disease may be stored (and optionally communicated
to the
patient).
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As illustrated in FIG. 21, a report representing the patient's history using
chronic
disease manager 10 is shown. The information is categorized by date, event,
and detail.
If an alarm has been generated, it is also illustrated. For example, if the
patient goal for
HbAlc, was 7%, and the test result value was 20%, applying the logic sequence
as herein
described using the guideline algorithm results in the action sequence causing
the
generation of the alert and the placement of the patient's name in the risk
manager. The
user may elect to see all entries. Alternatively, the user may select test
results for a given
patient by category, i.e. HbAlc, eye exam, lipids, microalbumin, may view
quality
guideline, quality plan, therapies, office visits, notes, reminders, patient
communications,
and meter selection by entering a check, such as with a mouse.
The user may also elect to change the rule and/or threshold in the risk
manager. If
so, the user will receive a warning, advising the user that the high-risk
patient list will be
changed and the previous high-risk patient list will be deleted. The user will
receive a
prompt, asking if the user wishes to continue. The user may elect not to
continue and
may then select another option in the risk manager or exit to select another
function. If
the user changes the threshold value (and/or rule) such as for lipids, HbAlc,
etc., the new
threshold, rule, and/or Boulian combination thereof, will then be compared to
the patient
data. If the value is exceeded for the patient (or if the rule is satisfied),
the action
2o sequence is undertaken, and the alert, letter, offsite, clinical schedule,
and risk manager
are updated.
Now turning to FIGS. 22 and 23, the logic sequence for generation of high-risk
monitor rule criteria is illustrated. The user enters the high-risk editor and
may edit an
existing rule or add a new rule (or criteria). The criteria includes patient
parameters such
as, HbAlc, lipid CDL), Microalbumin, Liver Enzyme (ALT), Liver Enzyme (AST),
Complication, Comorbidity, Risk Factor, BMI, BP, ER Visits, Days Hospitalized,
Days
Lost From Activities, SMBG Daily Min, and SMBG Daily Max. Next, the user is
prompted with respect to whether the criteria are date deliminated or numeric
deliminated
(and/or both). If a test result, patients who have not been tested may be
included.
Refernng now to FIG. 24, the logic sequence to generate a high-risk rule for
patients
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taking medication that may adversely impact the liver is illustrated. The FDA
now
recommends that liver enzyme testing be performed on patients taking specific
medications. The user selects a medication category, is then prompted to
determine if it
is a new or existing medication, and is then prompted to determine whether the
medication is associated with an adverse reaction. If the medication is
associated with a
liver reaction, a high-risk rule is generated to require testing for liver
enzymes. If the
enzyme is present, the action sequence is initiated (FIGS. 11A-11C).
In a further embodiment of the present invention, the chronic disease monitor
10
1 o performs real-time monitoring, analysis, and reporting of patient
outcomes, illustrating
and substantiating the clinical effectiveness of medications, devices, and
other chronic
disease clinical and educational interventions. The automated process analyzes
changes
over time in software defined and stored clinical indicators. Outcomes are
monitored,
analyzed, and reported for single patients or patient populations. Patient
data 16 can be
15 aggregated from multiple locations, and standard or custom (both tabular
and graphical)
reports can be generated. Aggregated data is stripped of patient identifiers
to protect
confidentiality.
Moreover, the chronic disease monitor 10 enables tracking of educational
2o intervention outcomes (for example, see FIG. 31). By tracking changes in
patient clinical
measures, the effectiveness of the educational intervention can be measured
empirically.
The chronic disease monitor 10 also automates the development and updating of
individualized assessment and educational plans for each patient.
25 The chronic disease monitor 10 integrates comprehensive clinical management
with the delivery and assessment of patient education, consistent with the
National
Standards for Diabetes Self Management Education and other national standards.
The chronic disease monitor 10 produces automated, context-driven patient
30 communications and education. It will produce multi-language correspondence
that
reminds patients about planned care events and offers results-based feedback
about the
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most recent changes in their glycemic control. Digitally stored education
materials are
produced and scheduled for patient delivery based on a care plan customized
for each
patient.
5 In particular, the invention provides a chronic disease monitor 10 that
incorporates the process steps of best practices, prevention and wellness
programs,
patient education, professional education, therapy goals and selection,
integrated real-
time quality assessment and reporting, utilization and compliance monitoring,
and
outcomes measurement and management. All of this information may be entered
through
the user interface 13 into the database 12 and stored therein according to an
algorithm.
Moreover, the chronic disease monitor 10 is compatible with various standards
bodies. For example, in a diabetes implementation, the invention is compatible
with the
American Diabetes Association (ADA), the ADA Standards of Care, and the
National
15 Standards for Diabetes Self Management Education.
The chronic disease monitor 10, termed a "diabetes registry" in one possible
implementation, supports physicians and other members of a diabetes care team
by fully
automating the planning, delivery, management, and ongoing quality assessment
of
20 patient care. The chronic disease monitor 10 provides software that
conducts real-time
monitoring of each patient's care, integrates and measures the results of
patient
education, and provides an ongoing systematic assessment of clinical outcomes.
The
chronic disease monitor 10 has the potential to advance the quality of
diabetes care by
giving physicians and other diabetes care team members the critical
information and tools
25 they need more effectively to help patients.
The chronic disease monitor 10 greatly assists a user in improving the quality
of
care by providing a program that gives up-to-date and complete information
needed to
help a patient manage his or her diabetes, and by producing an outcomes
analysis that
3o substantiates the value of interventions.
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The chronic disease monitor 10 provides software algorithms that automate the
creation and implementation of standards-based guidelines, and enable a
diabetes clinical
team to select the measures, parameters and thresholds that are used to
collect, monitor
and report the clinical status of all patients. The program assesses the
status, measures
the progress, and reports the results for all patients under care. The program
assures the
delivery of standards-based, cost-efficient, quality diabetes care - delivered
to the
appropriate patient at the appropriate time.
Furthermore, the chronic disease monitor 10 analyzes clinical results on an
ongoing basis, producing alerts for results above established thresholds,
reminders for
scheduled tests and visits, and context-sensitive patient communication and
education.
The software includes a high-risk monitor that is active in the background of
the program
- building an ongoing high-risk patient assessment based on parameters set by
the
clinical team. This ongoing high-risk assessment creates an opportunity for
earlier
identification and intervention before the occurrence of high-expense care
episodes.
FIG. 25 is a block diagram representing the logic sequence for a patient
outcome
analysis performed by the system 10. A user, such as a physician, logs into
the system 10
and, using an appropriate graphical window, discussed below, selects the
desired patient
2o population. The user then selects the medication, device or educational
intervention
whose effects are desired to be analyzed, and the clinical measure desired.
The system
10 then displays the selected outcome on the screen of the terminal 18, either
as a graph
or as a chart, or on the printer 28. Program flow then terminates.
FIG. 26 is a block diagram representing the logic sequence for patient
communications performed by the system 10. A user, such as a physician, logs
into the
system 10 and, using an appropriate graphical window, discussed below, selects
the
desired time period. The user then selects the desired clinical condition,
which may be,
for example, elevated HbAlc. The user then selects whether to employ a
standard
3o template or a custom template for the patient communication. The system 10
then
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generates the desired patient communication, which may be an e-mail 27 or a
conventionally mailed letter printed on the printer 28. Program flow then
terminates.
FIG. 26 is a block diagram representing the logic sequence for patient
education
performed by the system 10. A user logs into the system 10 and, using an
appropriate
graphical window, discussed below, selects a patient record. The user then
records the
patient assessment, and schedules an appropriate educational intervention. The
patient's
educational goals and objectives are then evaluated, and the system 10 then
correlates the
clinical outcomes to the educational intervention. Program flow then
terminates.
FIGS. 28 - 50 illustrate sample screen printouts that may be used to implement
the invention in one possible further embodiment. It will be appreciated that,
in FIGS. 28
50, the notation "LastNamexx, FirstNamexx" denotes a patient identifier.
FIG. 28 illustrates an "outcomes" screen that provides an overview of the
medications and educational treatment plans for the patient. The screen of
FIG. 28
provides, at the top, an indication of the medications that the particular
patient has been
taking. The name of the medication, dosage, frequency, start, and stop dates
are
indicated. These fields can be entered by the user using an appropriate
interface device
2o such as the terminal 18. Generally, the user of the chronic disease monitor
10 will be a
doctor, doctor's office administrator, managed care administrator, or health
care educator.
The screen of FIG. 28 also indicates the names of classes that the patient is
scheduled to
attend. These educational courses are an integral part of the patient's
treatment, and may
be offered at any location, such as a hospital, doctor's office or school, or
even via video
tape, Internet and so forth. The patient may learn various skills in a class,
such as how to
take his or her medication, the important of proper nutrition and exercise,
and so forth.
The class name is indicated as well as the goals of the classes.
FIG. 29 illustrates how the user may select clinical measure criteria for the
patient. The values for the particular measures may be input over time by the
administrator based on test results. As an example, assume the desired measure
to view
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is HbAlc. This measure is seen plotted versus time in FIG. 30. A benchmark is
also
shown that indicates when the patient attended a class (i.e., "educational
intervention").
This allows the user to determine the trend of the measure, as well as the
effects of the
educational intervention. In the illustration of FIG. 30, it is seen that the
educational
intervention has a positive effect. This provides an important confirmation of
the value
of the education. Additional threshold and goal levels may be plotted as
shown.
FIG. 31 shows an example of a medication outcome for HbAlc for the medication
"GlucotrolXL~."
FIG. 32 shows an example of a medication outcome for LDL for the medication
"Lipitor~".
FIG. 33 shows an example of an outcome for patient weight, plotted versus
time.
I s Additionally, the educational intervention is shown.
FIG. 34 illustrates a patient information screen. Here, the user may scroll
down to
view specific information, such as alerts and reminders, for the various
factors shown.
For example, the factor "Lipid Profile" is selected, as indicated by the
highlight. A
2o corresponding alert is that an HbAlc measure is to be taken on a certain
date. A
corresponding reminder is that the patient's next doctor's visit is scheduled
for a certain
date. Additionally, a history and results of the various factors are shown, as
well as a
color-coded highlight for results that are out of range.
25 FIG. 35 illustrates an education content summary - medical history screen.
Note
that various assessment factors may be selected by the user, such as medical
history,
diabetes history, social history, educational intervention, and evaluation of
progress. For
example, with medical history selected, information is entered regarding the
patient's
vision, hearing, and so forth. Moreover, the system documents who made the
entry and
3o when, and any other relevant notes. FIG. 35 illustrates a specific
application of an
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education content summary, i.e., for a patient's diabetes history screen.
Here, information
such as the patient's diabetes type, year of diagnosis and so forth are
entered by the user.
FIG. 37 illustrates an education content summary - social history screen.
Here,
information such as the patient's date of birth, marital status, and so forth
are entered.
FIG. 38 illustrates an education content summary- educational intervention.
Here, information is entered regarding educational courses that the patient
has taken, or is
assigned to take.
to
FIG. 39 illustrates an enter intervention screen. This interface allows the
user to
view details of a particular class. For example, for the class "Meal Planning"
(see also
FIG. 38), detailed information is provided at FIG. 39 regarding the objectives
of the class.
This allows the user to determine if the class is appropriate for the patient,
and provides
administrative information such as the location of the class and the
instructor's name.
FIG. 40 illustrates an education content summary - evaluation of progress
screen.
Here, the user can enter information regarding how many of the class
objectives have
been met by the patient.
FIG. 41 illustrates further details of the evaluation of progress function,
e.g., to
allow the user to view each objective and indicate whether it has been met.
FIG. 42 illustrates a "diabetes registry" that provides a variety of
functions. For
example, one function regarding education allows the user to define a class,
schedule a
class, or indicate a class instructor.
FIG. 43 illustrates an education class definition summary that provides
functions
to allow the user to enter and view information. A class function indicates,
e.g., the name
of the class, its description, and its identifier number.
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FIG. 44 illustrates an education class definition screen that allows the user
to enter
information regarding a class, such as its name and description. Moreover, the
user
denotes whether the class provides information that is related to various
standardized
content areas.
5
FIG. 45 illustrates an education class summary schedule class screen that
allows
the user to review class-scheduling information.
FIG. 46 illustrates an education class summary - instructor screen that
provides
10 summary information regarding the various class instructors.
FIG. 47 provides a data entry screen for use in connection with the screen of
FIG.
44. It should be appreciated that documentation of the credentials of the
instructors may
be important for compliance with some standards groups.
FIG. 48 provides a select patient attendance screen that allows a user, such
as a
class instructor, to enter attendance information for patients.
FIG. 49 illustrates a patient communication screen that allows a user to
initiate
2o communications, such as printed letters or e-mails 27, to patients based on
selected
criteria. The communications may be reminders for upcoming appointments, or
notifications that recent test results are out of range, or are on an
increasing or decreasing
trend. This is a great time saver, as the user can contact selected patients
from a large
population to receive a communication. These ongoing communications are
important
for managing chronic diseases such as diabetes to keep the patient informed of
his or her
medical condition, and provide a convenience in reminding him or her of
important
upcoming events.
FIG. 50 illustrates a select patient communications screen that lists selected
patients based on the selection criteria input by the user via the screen of
FIG. 49. This
allows the user to identify the patients that fall under the criteria and
individually to select
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or de-select them from receiving a communication. Additionally, the particular
type of
communication is indicated. Accordingly, it can be seen that the chronic
disease monitor
provides a chronic disease monitoring, education and patient communication
system
that provides a variety of features, including setting patient outcomes,
providing
education, and automating patient communications.
The chronic disease monitor 10 allows the user to monitor and manage patients,
as well as generating a care plan. The chronic disease monitor 10 provides
conformance
with industry and governmental guidelines for patient education. The chronic
disease
10 monitor 10 allows users to use their professional judgment and assessment
results such as
test results to create educational programs tailored to the patient, including
the creation of
new types of classes with specific objectives. The chronic disease monitor 10
facilitates
the monitoring of a large number of patients, and allows aggregation of data.
This is
particularly important, e.g., to pharmaceutical companies that wish to obtain
concrete
evidence of the effectiveness of their medications. The chronic disease
monitor 10 also
provides proof of effectiveness for the program educators, care providers and
other.
Although preferred embodiments of the present invention have been described in
detail herein with reference to the accompanying drawings, it is to be
understood that the
invention is not limited to those precise embodiments, and that various
changes and
modifications may be effected therein by one skilled in the art without
departing from the
spirit and scope of the invention as defined in the appended claims.
