Note: Descriptions are shown in the official language in which they were submitted.
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CLINICAL DASHBOARD USER INTERFACE SYSTEM AND METHOD
FIELD
[0001] The present disclosure relates to a clinical dashboard user interface
system and
method, and in particular in the field of disease identification and
monitoring.
BACKGROUND
[0002] One of the challenges facing hospitals today is identifying a patient's
primary
illness as early as possible, so that appropriate interventions can be
deployed immediately.
Some illnesses, such as Acute Myocardial Infarction (AMI) and pneumonia,
require an
immediate standard action or pathway within 24 hours of the diagnosis. Other
illnesses are less
acute but still require careful adherence to medium and long-term treatment
plans over
multiple care settings.
[0003] The Joint Commission, the hospital accreditation agency approved by the
Centers for Medicare and Medicaid Services (CMS), has developed Core Measures
that have
clearly articulated process measures. These measures are tied to standards
that could result in
CMS penalties for poor performance. For example, the measures set forth for
Acute
Myocardial Infarction include:
Set Measure ID # Measure Short Name
AM I-1 Aspirin at Arrival
AMI-2 Aspirin Prescribed at Discharge
AMI-3 ACEI or ARE for LVSD
AMI-4 Adult Smoking Cessation Advice/Counseling
AM1-5 Beta-Blocker Prescribed at Discharge
AMI-7 Median Time to Fibrionolysis
AMI-7a Fibrinolytic Therapy Received within 30 minutes of Hospital
Arrival
AMI-8 Median Time to Primary PCI
AMI-8a Primary PCI Received within 90 minutes of Hospital Arrival
AMI-9 , Inpatient Mortality (retired effective 12/31/2010)
AMI-10 Statin Prescribed at Discharge
2
[00041 To date, most reporting and monitoring of accountable measure
activities are done
after the patient has been discharged from the healthcare facility. The delay
in identifying and
learning about a particular intervention often makes it impossible to rectify
any situation. It is also
difficult for a hospital administrator to determine how well the hospital is
meeting core measures on
a daily basis. Clinicians need a real-time or near real-time view of patient
progress and care
throughout the hospital stay, including clinician notes, that will infonn
actions (pathways and
monitoring) on the part of care management teams and physicians toward meeting
these core
measures.
[0005] Case management teams have difficulty following patents' real-time
disease status.
The ability to do this with a clear picture of clinicians' notes as they
change in real-time as new
information comes in during a patient's hospital stay would increase the
teams' ability to apply
focused interventions as early as possible and follow or change those pathways
as needed throughout
a patient's hospital stay, increasing quality and safety of care, decreasing
unplanned readmissions
and adverse events, and improving patient outcomes. This disclosure describes
software developed
to identify and risk stratify patients at highest risk for hospital
readmissions and other adverse
clinical events, and a dashboard user interface that presents data to the
users in a clear and easy-to-
understand manner.
[0005A] In a broad aspect, the present invention pertains to a clinical
predictive and
monitoring system comprising a data store configured to receive data by wore
or wireless
communication from a plurality of source entities associates with a plurality
of patients, The
plurality of source entities include hospital and health care entities, non-
health care entities, health
information exchange entities, social-to-health information entities and
social service entities, the
data from these entities being stored including clinical and non-clinical
data. There is at least one
predictive model including a plurality of weighted risk variables and risk
thresholds in consideration
of the clinical and non-clinical data to identify at least one high-risk
patient as having a high risk of
Date Recue/Date Received 2022-06-16
2a
developing at least one specified medical condition. A risk logic module is
configured to apply the
at east one predictive model to the patient clinical and non-clinical data of
the plurality of patients
received from the plurality of source entities, to determine at least one risk
score associated with the
at least one specified medical condition for each patient, and identify at
least one high-risk patient
from among the plurality of patients as having a high risk of developing the
at least one specified
medical condition indicated by the risk scores. A data presentation module is
configured to present
notification and information to an intervention coordination team abut the
identified at least one
high-risk patient, the data presentation module further configured to generate
and transmit
notification and infomiation in a form selected from at least one member of
the group consisting of
text message, multimedia message, instant message, voice message, e-mail
message, web page,
web-based message, and text files.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 is a simplified block diagram of an exemplary embodiment of a
clinical
predictive and monitoring system and method according to the present
disclosure;
[0007] FIG. 2 is a timeline diagram of an exemplary embodiment of a clinical
predictive
and monitoring system and method according to the present disclosure;
[0008] FIG. 3 is a simplified logical block diagram of an exemplary embodiment
of a
clinical predictive and monitoring system and method according to the present
disclosure;
Date Recue/Date Received 2022-06-16
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[0009] FIG. 4 is a simplified flowchart of an exemplary embodiment of a
clinical
predictive and monitoring method according to the present disclosure;
[0010] FIG. 5 is a simplified flowchart/block diagram of an exemplary
embodiment of
a clinical predictive and monitoring method according to the present
disclosure;
[0011] FIG. 6 is a simplified flowchart diagram of an exemplary embodiment of
a
dashboard user interface system and method according to the present
disclosure;
[0012] FIG. 7 is a simplified flowchart diagram of an exemplary embodiment of
a
typical user interaction with the dashboard user interface system and method
according to the
present disclosure;
[0013] FIG. 8 is an exemplary screen shot of a dashboard user interface system
and
method according to the present disclosure;
[0014] FIG. 9 is an exemplary screen shot of a dashboard user interface system
and
method showing a drop comment window according to the present disclosure; and
[0015] FIG. 10 is an exemplary screen shot of a dashboard user interface
system and
method showing a watch comment window according to the present disclosure.
DETAILED DESCRIPTION
[0016] FIG. 1 is a simplified block diagram of an exemplary embodiment of a
clinical
predictive and monitoring system 10 according to the present disclosure. The
clinical
predictive and monitoring system 10 includes a computer system 12 adapted to
receive a
variety of clinical and non-clinical data relating to patients or individuals
requiring care. The
variety of data include real-time data streams and historical or stored data
from hospitals and
healthcare entities 14, non-health care entities 15, health information
exchanges 16, and social-
to-health information exchanges and social services entities 17, for example.
These data are
used to determine a disease risk score for selected patients so that they may
receive more target
intervention, treatment, and care that are better tailored and customized to
their particular
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condition and needs. The system 10 is most suited for identifying particular
patients who
require intensive inpatient and/or outpatient care to avert serious
detrimental effects of certain
diseases and to reduce hospital readmission rates. It should be noted that the
computer system
12 may comprise one or more local or remote computer servers operable to
transmit data and
communicate via wired and wireless communication links and computer networks.
[0017] The data received by the clinical predictive and monitoring system 10
may
include electronic medical records (EMR) that include both clinical and non-
clinical data. The
EMR clinical data may be received from entities such as hospitals, clinics,
pharmacies,
laboratories, and health information exchanges, including: vital signs and
other physiological
data; data associated with comprehensive or focused history and physical exams
by a
physician, nurse, or allied health professional; medical history; prior
allergy and adverse
medical reactions; family medical history; prior surgical history; emergency
room records;
medication administration records; culture results; dictated clinical notes
and records;
gynecological and obstetric history; mental status examination; vaccination
records;
=
radiological imaging exams; invasive visualization procedures; psychiatric
treatment history;
prior histological specimens; laboratory data; genetic information;
physician's notes;
networked devices and monitors (such as blood pressure devices and glucose
meters);
pharmaceutical and supplement intake information; and focused genotype
testing.
[0018] The EMR non-clinical data may include, for example, social, behavioral,
lifestyle, and economic data; type and nature of employment; job history;
medical insurance
information; hospital utilization patterns; exercise information; addictive
substance use;
occupational chemical exposure; frequency of physician or health system
contact; location and
frequency of habitation changes; predictive screening health questionnaires
such as the patient
health questionnaire (PHQ); personality tests; census and demographic data;
neighborhood
environments; diet; gender; marital status; education; proximity and number of
family or care-
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giving assistants; address; housing status; social media data; and educational
level. The non-
clinical patient data may further include data entered by the patients, such
as data entered or
uploaded to a social media website.
[0019] Additional sources or devices of EMR data may provide, for example, lab
5 .. results, medication assignments and changes, EKG results, radiology
notes, daily weight
readings, and daily blood sugar testing results. These data sources may be
from different areas
of the hospital, clinics, patient care facilities, patient home monitoring
devices, among other
available clinical or healthcare sources.
[0020] As shown in FIG. 1, patient data sources may include non-healthcare
entities
15. These are entities or organizations that are not thought of as traditional
healthcare
providers. These entities 15 may provide non-clinical data that include, for
example, gender;
marital status; education; community and religious organizational involvement;
proximity and
number of family or care-giving assistants; address; census tract location and
census reported
socioeconomic data for the tract; housing status; number of housing address
changes;
frequency of housing address changes; requirements for governmental living
assistance; ability
to make and keep medical appointments; independence on activities of daily
living; hours of
seeking medical assistance; location of seeking medical services; sensory
impairments;
cognitive impairments; mobility impairments; educational level; employment;
and economic
status in absolute and relative terms to the local and national distributions
of income; climate
data; and health registries. Such data sources may provide further insightful
information about
patient lifestyle, such as the number of family members, relationship status,
individuals who
might help care for a patient, and health and lifestyle preferences that could
influence health
outcomes.
[0021] The clinical predictive and monitoring system 10 may further receive
data from
health information exchanges (HIE) 16. HIEs are organizations that mobilize
healthcare
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information electronically across organizations within a region, community or
hospital system.
H1Es are increasingly developed to share clinical and non-clinical patient
data between
healthcare entities within cities, states, regions, or within umbrella health
systems. Data may
arise from numerous sources such as hospitals, clinics, consumers, payers,
physicians, labs,
outpatient pharmacies, ambulatory centers, nursing homes, and state or public
health agencies.
[0022] A subset of HIEs connect healthcare entities to community organizations
that do
not specifically provide health services, such as non-governmental charitable
organizations,
social service agencies, and city agencies. The clinical predictive and
monitoring system 10
may receive data from these social services organizations and social-to-health
information
exchanges 17, which may include, for example, information on daily living
skills, availability
of transportation to doctor appointments, employment assistance, training,
substance abuse
rehabilitation, counseling or detoxification, rent and utilities assistance,
homeless status and
receipt of services, medical follow-up, mental health services, meals and
nutrition, food pantry
services, housing assistance, temporary shelter, home health visits, domestic
violence,
appointment adherence, discharge instructions, prescriptions, medication
instructions,
neighborhood status, and ability to track referrals and appointments.
[0023] Another source of data include social media or social network services
18, such
as FACEBOOK and GOOGLE+ websites. Such sources can provide information such as
the
number of family members, relationship status, identify individuals who may
help care for a
patient, and health and lifestyle preferences that may influence health
outcomes. These social
media data may be received from the websites, with the individual's
permission, and some data
may come directly from a user's computing device as the user enters status
updates, for
example.
[0024] These non-clinical patient data provides a much more realistic and
accurate
depiction of the patient's overall holistic healthcare environment. Augmented
with such non-
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clinical patient data, the analysis and predictive modeling performed by the
present system to
identify patients at high-risk of readmission or disease recurrence become
much more robust
and accurate.
[0025] The system 10 is further adapted to receive user preference and system
configuration data from clinicians' computing devices (mobile devices, tablet
computers,
laptop computers, desktop computers, servers, etc.) 19 in a wired or wireless
manner. These
computing devices are equipped to display a system dashboard and/or another
graphical user
interface to present system data and reports. For example, a clinician
(healthcare personnel)
may immediately generate a list of patients that have the highest congestive
heart failure risk
scores, e.g., top n numbers or top x %. The graphical user interface are
further adapted to
receive the user's (healthcare personnel) input of preferences and
configurations, etc. The data
may be transmitted, presented, and displayed to the clinician/user in the form
of web pages,
web-based message, text files, video messages, multimedia messages, text
messages, e-mail
messages, and in a variety of suitable ways and formats.
[0026] As shown in FIG. 1, the clinical predictive and monitoring system 10
may
receive data streamed real-time, or from historic or batched data from various
data sources.
Further, the system 10 may store the received data in a data store 20 or
process the data
without storing it first. The real-time and stored data may be in a wide
variety of formats
according to a variety of protocols, including CCD, XDS, HL7, SSO, IMPS, ED!,
CSV, etc.
The data may be encrypted or otherwise secured in a suitable manner. The data
may be pulled
(polled) by the system 10 from the various data sources or the data may be
pushed to the
system 10 by the data sources. Alternatively or in addition, the data may be
received in batch
processing according to a predetermined schedule or on-demand. The data store
20 may
include one or more local servers, memory, drives, and other suitable storage
devices.
Alternatively or in addition, the data may be stored in a data center in the
cloud.
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[0027] The computer system 12 may comprise a number of computing devices,
including servers, that may be located locally or in a cloud computing farm.
The data paths
between the computer system 12 and the data store 20 may be encrypted or
otherwise protected
with security measures or transport protocols now known or later developed.
[0028] FIG. 2 is a timeline diagram of an exemplary embodiment of a clinical
predictive and monitoring system and method according to the present
disclosure. The timeline -
diagram is used to illustrate how the clinical predictive and monitoring
system and method 10
may be applied to reduce hospital readmission rate relating to congestive
heart failure as an
example. A majority of U.S. hospitals struggle to contain readmission rates
related to
congestive heart failure. Though numerous studies have found that some
combination of
careful discharge planning, care provider coordination, and intensive
counseling can prevent
subsequent re-hospitalizations, success is difficult to achieve and sustain at
the typical U.S.
hospital. Enrolling all heart failure patients into a uniform, high intensity
care transition
program requires a depth of case management resources that is out of reach for
many
institutions, particularly safety-net hospitals. The clinical predictive and
monitoring system and
method 10 is adapted to accurately stratify risk for certain diseases and
conditions such as 30-
day readmission among congestive heart failure patients.
[0029] Within 24 hours of a patient's admission to the hospital, stored
historical and
real-time data related to the patients are analyzed by the clinical predictive
and monitoring
system and method 10 to identify specific diseases and conditions related to
the patient, such
as congestive heart failure. Further, the system 10 calculates a risk score
for congestive heart
failure for this particular patient within 24 hours of admission. If this
particular patient's risk
score for congestive heart failure is above a certain risk threshold, then the
patient is identified
on a list of high-risk patients that is presented to an intervention
coordination team. The
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processes for disease identification and risk score calculation are described
in more detail
below.
[0030] The clinical predictive and monitoring system and method 10 are
operable to
display, transmit, and otherwise present the list of high risk patients to the
intervention
coordination team, which may include physicians, physician assistants, case
managers, patient
navigators, nurses, social workers, family members, and other personnel or
individuals
involved with the patient's care. The means of presentment may include e-mail,
text messages,
multimedia messages, voice messages, web pages, facsimile, audible or visual
alerts, etc.
delivered by a number of suitable electronic or portable computing devices.
The intervention
coordination team may then prioritize intervention for the highest risk
patients and provide
targeted inpatient care and treatment. The clinical predictive and monitoring
system and
method 10 may further automatically present a plan to include recommended
intervention and
treatment options. Some intervention plans may include detailed inpatient
clinical assessment
as well as patient nutrition, pharmacy, case manager, and heart failure
education consults
.. starting early in the patient's hospital stay. The intervention
coordination team may
immediately conduct the ordered inpatient clinical and social interventions.
Additionally, the
plan may include clinical and social outpatient interventions and developing a
post-discharge
plan of care and support.
[0031] High-risk patients are also assigned a set of high-intensity outpatient
interventions. Once a targeted patient is discharged, outpatient intervention
and care begin.
Such interventions may include a follow-up phone call within 48 hours from the
patient's case
manager, such as a nurse; doctors' appointment reminders and medication
updates; outpatient
case management for 30 days; a follow-up appointment in a clinic within 7 days
of discharge; a
subsequent cardiology appointment if needed; and a follow-up primary care
visit. Interventions
that have been found to be successful are based on well-known readmission
reduction
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programs and strategies designed to significantly reduce 30-day readmissions
associated with
congestive heart failure.
[0032] The clinical predictive and monitoring system and method 10 continue to
receive clinical and non-clinical data regarding the patient identified as
high risk during the
5 hospital stay and after the patient's discharge from the hospital to
further improve the diagnosis
and modify or augment the treatment and intervention plan, if necessary.
[0033] After the patient is discharged from the hospital, the clinical
predictive and
monitoring system and method 10 continue to monitor patient intervention
status according to
the electronic medical records, case management systems, social services
entities, and other
10 data sources as described above. The clinical predictive and monitoring
system and method 10
may also interact directly with caregivers, case managers, and patients to
obtain additional
information and to prompt action. For example, the clinical predictive and
monitoring system
and method 10 may notify a physician that one of his or her patients has
returned to the
hospital, the physician can then send a pre-formatted message to the system
directing it to
notify a specific case management team. In another example, the clinical
predictive and
monitoring system and method 10 may recognize that a patient missed a doctor's
appointment
and hasn't rescheduled. The system may send the patient a text message
reminding the patient
to reschedule the appointment.
[0034] FIG. 3 is a simplified logical block diagram of an exemplary embodiment
of a
clinical predictive and monitoring system and method 10 according to the
present disclosure.
Because the system 10 receives and extracts data from many disparate sources
in myriad
formats pursuant to different protocols, the incoming data must first undergo
a multi-step
process before they may be properly analyzed and utilized. The clinical
predictive and
monitoring system and method 10 includes a data integration logic module 22
that further
includes a data extraction process 24, a data cleansing process 26, and a data
manipulation
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process 28. It should be noted that although the data integration logic module
22 is shown to
have distinct processes 24-28, these are done for illustrative purposes only
and these processes
may be performed in parallel, iteratively, and interactively.
[0035] The data extraction process 24 extracts clinical and non-clinical data
from data
sources in real-time or in historical batch files either directly or through
the Internet, using
various technologies and protocols. Preferably in real-time, the data
cleansing process 26
"cleans" or pre-processes the data, putting structured data in a standardized
format and
preparing unstructured text for natural language processing (NLP) to be
performed in the
disease/risk logic module 30 described below. The system may also receive
"clean" data and
convert them into desired formats (e.g., text date field converted to numeric
for calculation
purposes).
[0036] The data manipulation process 28 may analyze the representation of a
particular
data feed against a meta-data dictionary and determine if a particular data
feed should be re-
configured or replaced by alternative data feeds. For example, a given
hospital EMR may store
the concept of "maximum creatinine" in different ways. The data manipulation
process 28 may
make inferences in order to determine which particular data feed from the EMR
would best
represent the concept of "creatinine" as defined in the meta-data dictionary
and whether a feed
would need particular re-configuration to arrive at the maximum value (e.g.,
select highest
value).
[0037] The data integration logic module 22 then passes the pre-processed data
to a
disease/risk logic module 30. The disease risk logic module 30 is operable to
calculate a risk
score associated with an identified disease or condition for each patient and
identifying those
patients who should receive targeted intervention and care. The disease/risk
logic module 30
includes a de-identification/re-identification process 32 that is adapted to
remove all protected
health information according to HIPAA standards before the data is transmitted
over the
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Internet. It is also adapted to re-identify the data. Protected health
information that may be
removed and added back may include, for example, name, phone number, facsimile
number,
email address, social security number, medical record number, health plan
beneficiary number,
account number, certificate or license number, vehicle number, device number,
URL, all
geographical subdivisions smaller than a State, including street address,
city, county, precinct,
zip code, and their equivalent geocodes (except for the initial three digits
of a zip code, if
according to the current publicly available data from the Bureau of the
Census), Internet
Protocol number, biometric data, and any other unique identifying number,
characteristic, or
code.
[0038] The disease/risk logic module 30 further includes a disease
identification
process 34. The disease identification process 34 is adapted to identify one
or more diseases or
conditions of interest for each patient. The disease identification process 34
considers data such
as lab orders, lab values, clinical text and narrative notes, and other
clinical and historical
information to determine the probability that a patient has a particular
disease. Additionally,
during disease identification, natural language processing is conducted on
unstructured clinical
and non-clinical data to determine the disease or diseases that the physician
believes are
prevalent. This process 34 may be performed iteratively over the course of
many days to
establish a higher confidence in the disease identification as the physician
becomes more
confident in the diagnosis. New or updated patient data may not support a
previously identified
disease, and the system would automatically remove the patient from that
disease list. The
natural language processing combines a rule-based model and a statistically-
based learning
model.
[0039] The disease identification process 34 utilizes a hybrid model of
natural language
processing, which combines a rule-based model and a statistically-based
learning model.
During natural language processing, raw unstructured data, for example,
physicians' notes and
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reports, first go through a process called tokenization. The tokeniz,ation
process divides the text
into basic units of information in the form of single words or short phrases
by using defined
separators such as punctuation marks, spaces, or capitalizations. Using the
rule-based model,
these basic units of information are identified in a meta-data dictionary and
assessed according
to predefined rules that determine meaning. Using the statistical-based
learning model, the
disease identification process 34 quantifies the relationship and frequency of
word and phrase
patterns and then processes them using statistical algorithms. Using machine
learning, the
statistical-based learning model develops inferences based on repeated
patterns and
relationships. The disease identification process 34 performs a number of
complex natural
language processing functions including text pre-processing, lexical analysis,
syntactic parsing,
semantic analysis, handling multi-word expression, word sense disambiguation,
and other
functions.
[0040] For example, if a physician's notes include the following: "55 yo m c
h/o dm,
cri. now with adib rvr, chfexac, and rle cellulitis going to 10W, tele." The
data integration logic
22 is operable to translate these notes as: "Fifty-five-year-old male with
history of diabetes
mellitus, chronic renal insufficiency now with atrial fibrillation with rapid
ventricular response,
congestive heart failure exacerbation and right lower extremity cellulitis
going to 10 West and
on continuous cardiac monitoring."
[0041] Continuing with the prior example, the disease identification process
34 is
adapted to further ascertain the following: 1) the patient is being admitted
specifically for atrial
fibrillation and congestive heart failure; 2) the atrial fibrillation is
severe because rapid
ventricular rate is present; 3) the cellulitis is on the right lower
extremity; 4) the patient is on
continuous cardiac monitoring or telemetry; and 5) the patient appears to have
diabetes and
chronic renal insufficiency.
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[0042] The disease/risk logic module 30 further comprises a predictive model
process
36 that is adapted to predict the risk of particular diseases or condition of
interest according to
one or more predictive models. For example, if the hospital desires to
determine the level of
risk for future readmission for all patients currently admitted with heart
failure, the heart
failure predictive model may be selected for processing patient data. However,
if the hospital
desires to determine the risk levels for all internal medicine patients for
any cause, an all-cause
readmissions predictive model may be used to process the patient data. As
another example, if
the hospital desires to identify those patients at risk for short-term and
long-term diabetic
complications, the diabetes predictive model may be used to target those
patients. Other
predictive models may include HIV readmission, diabetes identification, risk
for cardio-
pulmonary arrest, kidney disease progression, acute coronary syndrome,
pneumonia, cirrhosis,
all-cause disease-independent readmission, colon cancer pathway adherence, and
others.
[0043] Continuing to use the prior example, the predictive model for
congestive heart
failure may take into account a set of risk factors or variables, including
the worst values for
laboratory and vital sign variables such as: albumin, total bilirubin,
creatine kinase, creatinine,
sodium, blood urea nitrogen, partial pressure of carbon dioxide, white blood
cell count,
troponin-I, glucose, internationalized normalized ratio, brain natriuretic
peptide, pH,
temperature, pulse, diastolic blood pressure, and systolic blood pressure.
Further, non-clinical
factors are also considered, for example, the number of home address changes
in the prior year,
.. risky health behaviors (e.g., use of illicit drugs or substance), number of
emergency room visits
in the prior year, history of depression or anxiety, and other factors. The
predictive model
specifies how to categorize and weight each variable or risk factor, and the
method of
calculating the predicted probably of readmission or risk score. In this
manner, the clinical
predictive and monitoring system and method 10 is able to stratify, in real-
time, the risk of
each patient that arrives at a hospital or another healthcare facility.
Therefore, those patients at
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the highest risks are automatically identified so that targeted intervention
and care may be
instituted. One output from the disease/risk logic module 30 includes the risk
scores of all the
patients for particular disease or condition. In addition, the module 30 may
rank the patients
according to the risk scores, and provide the identities of those patients at
the top of the list.
5 .. For example, the hospital may desire to identify the top 20 patients most
at risk for congestive
heart failure readmission, and the top 5% of patients most at risk for cardio-
pulmonary arrest in
the next 24 hours. Other diseases and conditions that may be identified using
predictive
modeling include, for example, HIV readmission, diabetes identification,
kidney disease
progression, colorectal cancer continuum screening, meningitis management,
acid-base
10 management, anticoagulation management, etc.
[0044] The disease/risk logic module 30 may further include a natural language
generation module 38. The natural language generation module 38 is adapted to
receive the
output from the predictive model 36 such as the risk score and risk variables
for a patient, and
"translate" the data to present the evidence that the patient is at high-risk
for that disease or
15 condition. This module 30 thus provides the intervention coordination
team additional
information that supports why the patient has been identified as high-risk for
the particular
disease or condition. In this manner, the intervention coordination team may
better formulate
the targeted inpatient and outpatient intervention and treatment plan to
address the patient's
specific situation.
[0045] The disease/risk logic module 30 further includes an artificial
intelligence (Al)
model tuning process 40. The artificial intelligence model tuning process 38
utilizes adaptive
self-learning capabilities using machine learning technologies. The capacity
for self-
reconfiguration enables the system and method 10 to be sufficiently flexible
and adaptable to
detect and incorporate trends or differences in the underlying patient data or
population that
may affect the predictive accuracy of a given algorithm. The artificial
intelligence model
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tuning process 40 may periodically retrain a selected predictive model for
improved accurate
outcome to allow for selection of the most accurate statistical methodology,
variable count,
variable selection, interaction terms, weights, and intercept for a local
health system or clinic.
The artificial intelligence model tuning process 40 may automatically modify
or improve a
.. predictive model in three exemplary ways. First, it may adjust the
predictive weights of clinical
and non-clinical variables without human supervision. Second, it may adjust
the threshold
values of specific variables without human supervision. Third, the artificial
intelligence model
tuning process 40 may, without human supervision, evaluate new variables
present in the data
feed but not used in the predictive model, which may result in improved
accuracy. The
.. artificial intelligence model tuning process 40 may compare the actual
observed outcome of
the event to the predicted outcome then separately analyze the variables
within the model that
contributed to the incorrect outcome. It may then re-weigh the variables that
contributed to this
incorrect outcome, so that in the next reiteration those variables are less
likely to contribute to
a false prediction. In this manner, the artificial intelligence model tuning
process 40 is adapted
.. to reconfigure or adjust the predictive model based on the specific
clinical setting or population
in which it is applied. Further, no manual reconfiguration or modification of
the predictive
model is necessary. The artificial intelligence model tuning process 40 may
also be useful to
scale the predictive model to different health systems, populations, and
geographical areas in a
rapid timeframe.
[0046] As an example of how the artificial intelligence model tuning process
40
functions, the sodium variable coefficients may be periodically reassessed to
determine or
recognize that the relative weight of an abnormal sodium laboratory result on
a new population
should be changed from 0.1 to 0.12. Over time, the artificial intelligence
model tuning process
38 examines whether thresholds for sodium should be updated. It may determine
that in order
for the threshold level for an abnormal sodium laboratory result to be
predictive for
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readmission, it should be changed from, for example, 140 to 136 mg/dL.
Finally, the artificial
intelligence model tuning process 40 is adapted to examine whether the
predictor set (the list of
variables and variable interactions) should be updated to reflect a change in
patient population
and clinical practice. For example, the sodium variable may be replaced by the
NT-por-BNP
protein variable, which was not previously considered by the predictive model.
[0047] The results from the disease/risk logic module 30 are provided to the
hospital
personnel, such as the intervention coordination team, and other caretakers by
a data
presentation and system configuration logic module 42. The data presentation
logic module 42
includes a dashboard interface 44 that is adapted to provide information on
the performance of
the clinical predictive and monitoring system and method 10. A user (e.g.,
hospital personnel,
administrator, and intervention coordination team) is able to find specific
data they seek
through simple and clear visual navigation cues, icons, windows, and devices.
The interface
may further be responsive to audible commands, for example. Because the number
of patients
a hospital admits each day can be overwhelming, a simple graphical interface
that maximizes
efficiency and reduce user navigation time is desirable. The visual cues are
preferably
presented in the context of the problem being evaluated (e.g., readmissions,
out-of-ICU,
cardiac arrest, diabetic complications, among others).
[0048] The dashboard user interface 44 allows interactive requesting of a
variety of
views, reports and presentations of extracted data and risk score calculations
from an
operational database within the system. including, for example, summary views
of a list of
patient in a specific care location; detailed explanation of the components of
the various sub-
scores; graphical representations of the data for a patient or population over
time; comparison
of incidence rates of predicted events to the rates of prediction in a
specified time frame;
summary text clippings, lab trends and risk scores on a particular patient for
assistance in
dictation or preparation of history and physical reports, daily notes, sign-
off continuity of care
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notes, operative notes, discharge summaries, continuity of care documents to
outpatient
medical practitioners; order generation to automate the generation of orders
authorized by a
local care providers healthcare environment and state and national guidelines
to be returned to
the practitioner's office, outside healthcare provider networks or for return
to a hospital or
practices electronic medical record; aggregation of the data into frequently
used medical
formulas to assist in care provision including but not limited to: acid-base
calculation, MELD
score, Child-Pugh-Turcot score, TIMI risk score, CHADS score, estimated
creatinine
clearance, Body Surface area, Body Mass Index, adjuvant, neoadjuvant and
metastatic cancer
survival nomograms, MEWS score, APACHE score, SWIFT score, NM stroke scale,
PORT
score, AJCC staging; and publishing of elements of the data on scanned or
electronic versions
of forms to create automated data forms.
[0049] The data presentation and system configuration logic module 40 further
includes a messaging interface 46 that is adapted to generate output messaging
code in forms
such as HL7 messaging, text messaging, e-mail messaging, multimedia messaging,
web pages,
web portals, REST, XML, computer generated speech, constructed document forms
containing
graphical, numeric, and text summary of the risk assessment, reminders, and
recommended
actions. The interventions generated or recommended by the system and method
10 may
include: risk score report to the primary physician to highlight risk of
readmission for their
patients; score report via new data field input into the EMR for use by
population surveillance
of entire population in hospital, covered entity, accountable care population,
or other level of
organization within a healthcare providing network; comparison of aggregate
risk of
readmissions for a single hospital or among hospitals to allow risk-
standardized comparisons
of hospital readmission rates; automated incorporation of score into discharge
summary
template, continuity of care document (within providers in the inpatient
setting or to outside
physician consultants and primary care physicians), HL7 message to facility
communication of
19 '
readmission risk transition to nonhospital physicians; and communicate
subcomponents of the
aggregate social-environmental score, clinical score and global risk score.
These scores would
highlight potential strategies to reduce readmissions including: generating
optimized
medication lists; allowing pharmacies to identify those medication on
formulary to reduce out-
of-pocket cost and improve outpatient compliance with the pharmacy treatment
plan; flagging
nutritional education needs; identifying transportation needs; assessing
housing instability to
identify need for nursing home placement, transitional housing, Section 8 HHS
housing
assistance; identifying poor self regulatory behavior for additional follow-up
phone calls;
identifying poor social network scores leading to recommendation for
additional in home RN
assessment; flagging high substance abuse score for consultation of
rehabilitation counselling
for patients with substance abuse issues.
[0050] This output may be transmitted wirelessly or via LAN, WAN, the
Internet, and
delivered to healthcare facilities' electronic medical record stores, user
electronic devices (e.g.,
pager, text messaging program, mobile telephone, tablet computer, mobile
computer, laptop
computer, desktopcomputer, and server), health information exchanges, and
other data stores,
databases, devices, and users. The system and method 10 may automatically
generate, transmit,
and present information such as high-risk patient lists with risk scores,
natural language
generated text, reports, recommended actions, alerts, Continuity of Care
Documents, flags,
appointment reminders, and questionnaires.
[0051] The data presentation and system configuration logic module 42 further
includes a system configuration interface 48. Local clinical preferences,
knowledge, and
approaches may be directly provided as input to the predictive models through
the system
configuration interface 48 This system configuration interface 48' allows the
institution or
health system to set or reset variable thresholds, predictive weights, and
other parameters in the
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predictive model directly. The system configuration interface 48 preferably
includes a
graphical user interface designed to minimize user navigation time.
[0052] FIG. 4 is a simplified flowchart of an exemplary embodiment of a
clinical
predictive and monitoring method 50 according to the present disclosure. The
method 50
5 receives structured and unstructured clinical and non-clinical data
related to specific patients
from a variety of sources and in a number of different formats, as shown in
block 52. These
data may be encrypted or protected using data security methods now known or
later developed.
In block 54, the method 50 pre-processes the received data, such as data
extraction, data
cleansing, and data manipulation. Other data processing techniques now known
and later
10 developed may be utilized. In block 56, data processing methods such as
natural language
processing and other suitable techniques may be used to translate or otherwise
make sense of
the data. In block 58, by analyzing the pre-processed data, one or more
diseases or conditions
of interest as related to each patient are identified. In block 60, the method
50 applies one or
more predictive models to further analyze the data and calculate one or more
risk scores for
15 each patient as related to the identified diseases or conditions. In
blocks 62 and 64, one or more
lists showing those patients with the highest risks for each identified
disease or condition are
generated, transmitted, and otherwise presented to hospital personnel, such as
members of an
intervention coordination team. These lists may be generated on a daily basis
or according to
another desired schedule. The intervention coordination team may then
prescribe and follow
20 targeted intervention and treatment plans for inpatient and outpatient
care. In block 66, those
patients identified as high-risk are continually monitored while they are
undergoing inpatient
and outpatient care. The method 50 ends in block 68.
[0053] Not shown explicitly in FIG. 4 is the de-identification process, in
which the data
become disassociated with the patient's identity to comply with HIPAA
regulations. The data
.. can be de-coupled with the patient's identity whenever they are transmitted
over wired or
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wireless network links that may be compromised, and otherwise required by
H1PAA. The
method 50 is further adapted to reunite the patient data with the patient's
identity.
[0054] FIG. 5 is a simplified flowchart/block diagram of an exemplary
embodiment of
a clinical predictive and monitoring method 70 according to the present
disclosure. A variety
of data are received from a number of disparate data sources 72 related to
particular patients
admitted at a hospital or a healthcare facility. The incoming data may be
received in real-time
or the data may be stored as historical data received in batches or on-demand.
The incoming
data are stored in a data store 74. In block 76, the received data undergo a
data integration
process (data extraction, data cleansing, data manipulation), as described
above. The resultant
pre-processed data then undergoes the disease logic process 78 during which de-
identification,
disease identification, and predictive modeling are performed. The risk score
computed for
each patient for a disease of interest is compared to a disease risk threshold
in block 80. Each
disease is associated with its own risk threshold. If the risk score is less
than the risk threshold,
then the process returns to data integration and is repeated when new data
associated with a
patient become available. If the risk score is greater than or equal to the
risk threshold, then the
identified patient having the high risk scorc is included in a patient list in
block 82. In block
84, the patient list and other associated information may then be presented to
the intervention
coordination team in one or more possible ways, such as transmission to and
display on a
desktop or mobile device in the form of a text message, c-mail message, web
page, etc. In this
manner, an intervention coordination team is notified and activated to target
the patients
identified in the patient list for assessment, and inpatient and outpatient
treatment and care, as
shown in block 88. The process may thereafter provide feedback data to the
data sources 72
and/or return to data integration 76 that continues to monitor the patient
during his/her targeted
inpatient and outpatient intervention and treatment. Data related to the
patient generated during
the inpatient and outpatient care, such as prescribed medicines and further
laboratory results,
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radiological images, etc. is continually monitored according to pre-specified
algorithms which
define the patient's care plan.
[0055] FIG. 6 is a simplified flowchart diagram of an exemplary embodiment of
a
dashboard user interface system and method 90 according to the present
disclosure. The
patients' data are evaluated as described above, and those patients associated
with targeted
diseases and surveillance conditions are identified in block 92. The targeted
diseases are those
illnesses that the patient is at risk for readmission to the healthcare
facility. The monitored
conditions are those patient conditions, e.g., injury and harm, that are
indicative of occurrence
of adverse events in the healthcare facility. The patients' inclusion on a
particular disease or
surveillance condition list is further verified by comparison to a
predetermined probability
threshold, as shown in block 94. If the probability threshold is met, then the
patient is classified
or identified as belonging to a disease list or condition list. The display is
also updated so that
when a user selects a particular disease list for display, that patient is
shown in the list, as
shown in block 96. This may be seen in the exemplary screen in FIG. 8. In this
exemplary
screen, the list of patients that are at risk for 30-day readmission due to
congestive heart failure
(Cl-IF) are identified and listed in the active congestive heart failure list.
Details of the
exemplary screen are provided below.
[0056] The user may print, transmit, and otherwise use the displayed
information, and
generate standard or custom reports. The reports may be primarily textual in
nature, or include
graphical information. For example, a graphical report may chart the
comparison of expected
to observed readmission rates for any disease type, condition, or category for
patients enrolled
or not enrolled in an intensive intervention program, the readmission rates
for enrolled versus
dropped patients over a period of time for any disease type, condition, or
category. Patients
with greater than 95%, for example, of probability of having heart failure,
total versus enrolled
over a specified time period, and the number of patients not readmitted within
30 day discharge
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readmission window by number of days from discharge. Additional exemplary
standard
tracking reports that may further identify all enrolled patients for which:
post-discharge
appointments are scheduled, post-discharge phone consults are scheduled,
patient has attended
follow-up appointment, patient has received post-discharge phone consult,
patient has received
.. and filled medical prescriptions, and patient has received transportation
voucher. Further
sample reports may include a comparison of expected to observed readmission
rates for any
disease type, event, or category for enrolled and not enrolled patients,
readmission rates for
enrolled vs. dropped patients over a period of time for any disease type,
event, or category,
patients with greater than 95% probability of having heart failure: total vs.
enrolled over a
.. specific time period, and the number of patients not readmitted within 30
day discharge
readmission window by number of days from discharge. If the probability
threshold is not met
in block 94, then the patient's data are re-evaluated as new or updated data
become available.
[0057] Another type of reports available are outcome optimization reports.
These are
reports designed to help users (administrators) assess the efficacy of a
program, establish
benchmarks, and identify needs for change on a systematic and population
levels to improve
care outcomes. The report may include data that assist in assessing the
effectiveness of the
identifying high risk patients. Some of the data may demonstrate effort spent,
patients enrolled,
and how often those patents truly are afflicted with the identified diseases.
Reports may
include data that assist in assessing whether interventions are given to the
right patients, at the
.. right time, etc.
[0058] As new, updated, or additional patient data become available, as shown
in block
98, the data is evaluated to identify or verify disease/condition. The patient
may be reclassified
if the data now indicate the patient should be classified differently, for
example. A patient may
also be identified as an additional disease and be included in another list.
For example, in the
first 24 hours of admissions, the system identifies patient Jane Doe as having
CHF. Upon
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receiving more information, such as lab results and new physician notes, the
system identifies
Jane Doe as also having AML Jane Doe will then be placed in the AMI list, and
identified as
an AMI patient as soon as the new diagnosis is available. Additionally, Jane
Doe will remain
in the Cl-IF list, yet she will be identified as an AMI patient in that list.
[0059] If there is no new patient data, then there is no change to the patient
classification and the display reflects the current state of patient
classification, as shown in
block 99. Accordingly, as real-time or near real-time patient data become
available, the
patients' disease and condition classification is re-evaluated and updated as
necessary.
[0060] FIG. 7 is a simplified flowchart diagram of an exemplary embodiment of
a
typical user interaction process 100 with the dashboard user interface system
and method
according to the present disclosure. All users that are permitted to access
the system must have
log-in security information such as username and password on record. All
access to the system
requires logging in to the system by supplying the correct log-in information,
as shown in
block 102. The user may select a number of parameters such as the disease
type, event, risk
level, and eligibility for high-intensity intervention care program enrollment
to generate a
report, as shown in block 103. The user may make this selection at any time
after the login is
successful. For example, the user may select a particular patient and review
information
associated With that patient. The user may then review and evaluate the
displayed information,
including clipped clinician notes, as shown in 104. The user may also print,
transmit, or
otherwise use, in some form, the displayed information.
[0061] Targeted predictive readmission diseases may include: congestive heart
failure,
pneumonia, acute myocardial infraction, diabetes, cardiopulmonary arrest and
mortality,
cirrhosis readmission, HIV readmission, sepsis, and all causes. Targeted
disease identification
may include: chronic kidney disease, sepsis, surveillance, chronic kidney
disease in outpatient,
.. diabetes mellitus in outpatient, and sepsis. Targeted conditions due to
possible adverse event
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for surveillance may include: sepsis, post-operative pulmonary embolism (PE)
or deep vein
thrombosis (DVT), post-operative sepsis, post-operative shock, unplanned
return to surgery,
respiratory failure, hypertension, unexpected injury, inadequate
communication, omission or
errors in assessment, diagnosis, or monitoring, falls, hospital-acquired
infections, medication-
5 wrong patient, patent identification issues, out-of-ICU cardiopulmonary
arrest and mortality,
chronic kidney disease, shock, trigger for narcan, trigger for narcotic (over-
sedation), trigger
for hypoglycemia, and unexpected death.
[0062] The evaluation may include inputting comments about the patient, for
example.
As a part of the evaluation process, the user may confirm, deny, or express
uncertainty about a
10 .. patient's disease or condition identification or intervention program
enrollment eligibility. For
example, the user may review the notes and recommendations associated with a
particular
patient and confirm the inclusion of that patient in the congestive heart
failure list, as shown in
block 106. For example, the user reviews the clipped clinician's notes that
call attention to key
words and phrases, helping him or her find key information regarding disease
identification by
15 the system. Key terms such as "shortness of breath," "BNP was elevated,"
and "Lasix" help the
user validate the disease identification of Cl-IF for that patient. If the
patient's classification,
risk level, and eligibility level are confirmed, there is no change in the
patient's classification
and the data that are displayed (except to indicate this classification has
been confirmed), as
shown in block 107. The user may supply comments associated with the
confirmation. User
20 comments are stored and can be seen by other users in real-time or near
real-time, allowing
clear and timely communication between team members. The user may proceed to
select a
report or a display parameter in block 103, or review and evaluate patients in
block 104.
[0063] Alternatively, the user may disagree with the inclusion of the patient
in the
congestive heart failure list, or express uncertainty. The user may enter
comments explaining
25 his or her assessment of and disagreement with the patient's disease
identification. User
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comments are stored and can be seen by other users in real-time or near real-
time, allowing
clear and timely communication between team members.
[0064] If the user denies the classification, then the patient is removed from
the active
list of the target disease or condition, and placed on a drop list, as shown
in block 108. In
response to the user denying the classification, the system may additionally
display or flag
information about the patient that contributed to the inclusion of the patient
on a particular list.
For example, if the user denies the disease ID that John Smith has heart
failure, the system may
further display a query: "Mr. Smith likely has Cl-IF due to the following
factors: elevated BNP,
shortness of breath, admitted for decompensated CHF 6/9. Are you sure you want
to remove
this patient from the active CHF list?" The user is required to respond to the
query with yes or
no. The system may additionally request rationale from the user for wanting to
remove the
patient from the active list. The rationale supplied by the user may be stored
and displayed as
reviewer comments. The user may also indicate uncertainty, and the patient is
removed from
the active list and placed on a watch list for further evaluation, as shown in
block 109. The user
may then review and evaluate additional patients on the same target disease
list or review
patients included on other disease and condition lists. At any point, the user
may print,
transmit, and otherwise use, in some form, the displayed information, such as
generate
standard or custom reports.
[0065] As an example, a patient Kit Yong Chen was identified as a CHF patient
on
admission. After receiving more data (i.e., new lab results and new physician
notes) during her
hospital stay, the system has identified this patient as having AMI.
[0066] The clinician notes upon admission states: 52 yo female w pmh of CAD,
also
with HTN presents with progressively worsening SOB and edema 1 month. 1.
Dyspnea: likely
CHF with elevated BNP afterload reduction with aCEi and diuresis with Lasix.
02 stats stable
2) elevated troponin: EKG with strain pattern follow serial enzymes to ROMI
and cards
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consulted for possible Cath. The clinician notes thereafter states: 52 yo
female with pmh of
CAD, also with FITN presents with progressively worsening SOB and edema 1
month c CAD
with LHC with stent prox LAD. I. Elevated troponins -NSTEMI, despite pt
denying CP - pt
with known hx of CAD, mild troponin leak 0.13->0.15->0.09->0.1 ¨ on admission
pt given
325, Plavix load with 300 mg 1, and heparin gtt - Metop increased 50 mg q6,
possibly change
Coreg at later time - LHC today per Cardiology, with PCI. also discuss with EP
for possible
LCD placement 2. Heart failure, acute on chronic - severe diastolic
dysfunction be due HTN off
meds +/- CAD - proBNP elevated 3183 on admission - initially started on lasix
40 tid, edema
much improved, now on lasix 40 po bid - TTE completed showing: 4 chamber
dilatation,
RVH, nml LV thickness, severely depressed LVSF, LVEF 30%,mod MR, mild TR, AR
and
PR; severe diastolic dysfunction, RVSP 52 - continue on Lasix, Lisinopril,
Metop - discuss
AICD evaluation with EP vs initial medical management.
[0067] The reviewer may assess the admission notes with the Disease ID of Cl-
IF
compared with the second notes with PIECES Disease ID of AMI in an effort to
validate this
new real-time disease identification. The admission note indicated Cl-IF as
the primary disease.
Key highlighted terms indicating CHF to a user include "pmh of CAD" (past
medical history
of coronary artery disease, "SOB" (shortness of breath), "edema," "elevated
BNP." The second
note indicates to a user that while the patient has Cl-IF, CAD is the primary
cause of the Cl-IF.
Key highlighted terms such as "elevated troponins" and "NSTEMI" (Non ST
Segment
Myocardial Infarction: heart attack) give the user a snapshot view of the key
terms the system
used to identify AMI as the primary disease. These highlighted key terms give
the users the
tools to validate in real-time or near real-time the system's change in
disease identification.
The user then confirms, denies, or expresses uncertainty with the new disease
identification. In
this example, the reviewer would assess the notes with the highlighted terms
and validate the
change by accepting the change in disease identification. Because the
patient's main pathway
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of intervention would be for AMI, the patient, identified disease, and risk
level would appear in
the AMI list.
[0068] The dashboard user interface may also indicate a change in the level of
risk. For
example, upon return of lab results (slightly elevated creatinine and tox
screen positive for
cocaine) and other social factors that influence risk (noncompliance with
sodium restriction
due to homelessness) as well as medical pathway language queues, the system
may identify
this patient as high risk. A user can follow these changes in the real-time
and to validate the
change in risk level.
[0069] FIG. 8 is an exemplary screen shot 120 of a dashboard user interface
system and
method according to the present disclosure. The exemplary screen 120 shows a
number of
patients identified as having risk of readmission to the healthcare facility
due to congestive
heart failure. The exemplary screen shows the active congestive heart failure
list. On the left
hand side of the screen are target diseases and surveillance conditions that
the user may select
for review and evaluation. The target diseases are those diseases that the
patients have been
evaluated against that may put them at risk for readimission to the healthcare
facility.
Registries and surveillance conditions are those conditions that may be the
result of adverse
events occurring in the healthcare facility. Due to the space available to
demonstrate the
exemplary screen, only a select few diseases and conditions are shown, and it
should be
understood that the system and method is capable of evaluating and analyzing
patient data for
any number of target diseases and conditions. The dashboard user interface
system is operable
to organize and display patients belonging to a number of lists: active list
(identified disease or
condition), watch list (uncertainty), and drop list (denied). The user may
click on any tab to
view and print any list. A number of data items associated with each patient
in a list are
displayed, such as the admission or arrival date, patient name, identified
target disease or
condition, status (enrollment in intensive intervention program), whether the
identified disease
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is confirmed, and the risk of readmission (expressed as, e.g., high, medium,
or low). The type
of data displayed for each list of patients may vary. It should be noted that
other types of data
associated with each patient on the lists may be displayed as well such as the
bed number and
Medical record number (MRN) of each patient to transmit, and otherwise used to
identify the
.. patient. The exemplary screen 120 may also display the user's name and
position (physician,
case manager, RN, nurse practitioner, etc.) near the top of the screen. The
numbers of patients
at risk for heart failure that has been selected today, this week, and last
week with the number
of low, medium and high risk statistics are tabulated and further displayed on
the exemplary
screen.
[0070] The user may click on a particular patient displayed in a list, and
obtain
additional detailed information about that patient. For example, clipped
clinician notes
(patient's assessment and plan) are displayed near the bottom of the screen,
with key words
and phrases highlighted or otherwise emphasized to indicate those text that
especially
contributed to the inclusion of the patient in the identified target disease
list, condition, risk
.. level, and eligibility level. The user may scroll through all the clipped
clinician notes
associated with the patient, which are organized chronologically so that the
user may review
the progression of the disease, diagnosis, assessments, and pathways. Because
the user can
view the notes in real-time or near real-time, he or she is able to clinically
validate the system's
assessment of the unstructured text. The display further provide the
reviewer's comments that
are associated with the confirmation or denial of the disease or condition
identification.
[0071] Not explicitly shown in FIG. 8 are additional features, such as a
search bar to
enable the user to enter one or more search criteria to locate one or more
particular patients.
For example, the user may input a medical record number, name, admission date,
disease type,
risk level, event, enrolled program, etc. to identify a set of patients that
satisfy the search
30
"criteria. The search criteria may be based on other types of criteria, such
as those patients that
have missed their post-discharge appointments.
[0072] FIGS. 9 and 10 are exemplary screen shots 122 and 124 of a dashboard
user
interface system and method showing a drop comment window and a watch comment
window.
respectively. If a user clicks on "No" to indicate that a particular patient
should not be on the
disease list, a reason for drop window pops up to enable the user to select
clinical and non-
clinical criteria that support the decision to deny the patient's
classification on the AM! list.
The user may further input reasons not already displayed. Similarly, the user
may enter reasons
that express uncertainty about a particular patient's inclusion on the
pneumonia list, as the
example shown in FIG. 10.
[0073] The display may optionally further include recommendations and
reminders
generated by the system. These recommendations and reminders may suggest
evidence-based
intervention options that would provide the greatest health benefit to the
patieni The proposed
intervention may consider clinical and nonclinical patient variables. In
addition, previous
patient enrollment results are factored into the recommended intervention. The
orders for post-
discharge care, e.g., nutrition, pharmacy, etc., may be automatically placed
when a patient is
enrolled in the program.
[0074] The system as described herein is operable to harness, simplify, sort,
and
present patient information in real-time or near real-time, predict and
identify highest risk
patients, identify adverse events, coordinate and alert practitioners, and
monitor patient
outcomes across time and space. The present system improves healthcare
efficiency, assists
_
with resource allocation, and presents the crucial information that lead to
better patient
outcomes.
[0075] The features of the present invention which are believed to be novel
are set
forth below with particularity in the appended claims. However, modifications,
variations, and
Date Recue/Date Received 2021-09-03
CA 02889613 2015-03-11
WO 2014/042942
PCT/US2013/058159
31
changes to the exemplary embodiments described above will be apparent to those
skilled in the
art, and the system and method described herein thus encompasses such
modifications,
variations, and changes and are not limited to the specific embodiments
described herein.
