U.S. patent application number 14/225549 was filed with the patent office on 2021-02-25 for healthcare information technology system for predicting or preventing readmissions.
The applicant listed for this patent is Cerner Health Services, Inc.. Invention is credited to Faisal Farooq, Balaji Krishnapuram, Bharat R. Rao, Romer E. Rosales, Shipeng Yu.
Application Number | 20210056176 14/225549 |
Document ID | / |
Family ID | 1000005381814 |
Filed Date | 2021-02-25 |
United States Patent
Application |
20210056176 |
Kind Code |
A9 |
Farooq; Faisal ; et
al. |
February 25, 2021 |
Healthcare Information Technology System for Predicting or
Preventing Readmissions
Abstract
Hospital readmissions may be prevented. Readmission is prevented
by predicting the probability of a given patient to be readmitted.
The probability alone may prevent readmission by educating the
patient or medical professional. The probability may be predicted
during a patient stay and used to generate a workflow action item
to reduce the probability, to warn, to output appropriate
instructions, and/or assist in avoiding readmission. The
probability may be specific to a hospital, physician group, or
other entity, allowing prevention to focus on past readmission
causes for the given entity.
Inventors: |
Farooq; Faisal; (Norristown,
PA) ; Krishnapuram; Balaji; (King of Prussia, PA)
; Rao; Bharat R.; (Berwyn, PA) ; Rosales; Romer
E.; (Downingtown, PA) ; Yu; Shipeng; (Exton,
PA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Cerner Health Services, Inc. |
Wilmington |
DE |
US |
|
|
Prior
Publication: |
|
Document Identifier |
Publication Date |
|
US 20140207492 A1 |
July 24, 2014 |
|
|
Family ID: |
1000005381814 |
Appl. No.: |
14/225549 |
Filed: |
March 26, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13153551 |
Jun 6, 2011 |
8949082 |
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14225549 |
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61352509 |
Jun 8, 2010 |
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61352515 |
Jun 8, 2010 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 10/60 20180101 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Claims
1. A method for predicting or preventing hospital readmission, the
method comprising: receiving an indication of an event for a
patient from a hospital; triggering application of a predictor of
readmission in response to the receiving of the indication;
applying, by a processor, the predictor of the readmission to an
electronic medical record of the patient in response to the
triggering, the predictor being based on readmission data of the
given hospital at which the patient is being treated; predicting,
by the processor, a probability of readmission of the patient based
on the applying of the predictor to the electronic medical record
of the patient; and outputting as a function of the
probability.
2. The method of claim 1 further comprising: mining the electronic
medical record of the patient; and populating a feature vector used
for predicting the probability from the mining; wherein applying
the predictor comprises applying the predictor to the feature
vector.
3. The method of claim 2 wherein mining comprises mining from a
first data source of the electronic medical record and mining from
a second data source of the electronic medical record, the first
data source comprising structured data and the second data source
comprising unstructured data, the mining outputting values for the
feature vector in a structured format from the first and second
data sources.
4. The method of claim 2 wherein mining comprises inferring a value
for each of a plurality of variables, each value inferred by
probabilistic combination of probabilities associated with
different possible values from different sources, the inferred
values for the variables comprising the feature vector.
5. The method of claim 2 where mining comprises mining as a
function of existing knowledge, guidelines, best practices, or
about specific institutions regarding readmissions.
6. The method of claim 1 wherein outputting comprises generating a
cell phone alert, a bedside monitor alert, an alert associated with
prevention of data entry, or combinations thereof.
7. The method of claim 1 further comprising: automatically
scheduling a job entry in a workflow of a case manager, the job
entry being for examination to avoid readmission.
8. The method of claim 1 wherein applying the predictor comprises
applying a machine-learnt classifier, and wherein predicting
comprises obtaining an output of the machine-learnt classifier, the
machine-learnt classifier comprising a statistical model.
9. The method of claim 1 wherein outputting comprises outputting at
least one variable having a value for the patient associated with a
strongest link to the probability indicating a risk of readmission,
the strongest link being relative to links for other values of
other variables to the risk.
10. The method of claim 1 wherein outputting comprises outputting a
mitigation plan associated with the predicting.
11. The method of claim 1 wherein outputting comprises outputting
based on a criteria set for the hospital.
12. The method of claim 1 wherein outputting comprises outputting
instructions based on the probability.
13. The method of claim 12 further comprising: applying a predictor
of compliance by the patient with the instructions to the
electronic medical record of the patient; and predicting a
probability of compliance of the patient based on the applying of
the predictor of compliance.
14. A system for predicting or preventing hospital readmission, the
system comprising: at least one memory operable to store data for a
plurality of readmitted patients of a first hospital; and a first
processor configured to: identify variables contributing to
readmission specific to the first hospital based on the data for
the plurality of the readmitted patients of the first hospital; and
incorporate the variables into a predictor of readmission for a
future patient of the first hospital.
15. The system of claim 14 wherein the processor is configured to
identify and incorporate by machine learning a statistical model
from the data, the predictor comprising a matrix of the statistical
model.
16. The system of claim 14 wherein the processor is configured to
mine the data including mining unstructured information, the mining
providing values for the variables, the values inferred from
different possible values in the data and probabilities assigned to
the possible values.
17. The system of claim 14 wherein the processor is configured to
associate different workflows with different possible predictions
of the predictor.
18. In a non-transitory computer readable storage medium having
stored therein data representing instructions executable by a
programmed processor for predicting or preventing hospital
readmission, the storage medium comprising instructions for:
predicting a probability of readmission of a patient, the
predicting occurring during a patient stay and prior to discharge;
comparing the probability to a threshold; and generating an alert
based on the comparing, the generating occurring during the patient
stay.
19. The non-transitory computer readable storage medium of claim 18
wherein generating the alert comprises displaying the alert on a
display while preventing entry of information.
20. The non-transitory computer readable storage medium of claim 18
wherein generating the alert comprises transmitting a message to a
cellular phone.
21. The non-transitory computer readable storage medium of claim 18
wherein generating the alert comprises displaying the alert on a
bedside monitor of the patient.
22. The non-transitory computer readable storage medium of claim 18
wherein generating the alert comprises alerting a person with a
notice indicating the patient and an indication of risk of the
readmission.
Description
RELATED APPLICATIONS
[0001] The present patent application is a continuation patent
application of U.S. patent application Ser. No. 13/153,551 filed
Jun. 6, 2011. The '551 Application and its related applications are
incorporated by reference herein in their entirety.
BACKGROUND
[0002] The present embodiments relate to predicting risk of
hospital readmission and/or providing valuable information to
potentially prevent readmission. Preventing readmission may reduce
medical costs and benefit the patient and hospital.
[0003] In the United States, about 20% of all Medicare
beneficiaries are readmitted, out of which 75% of the readmissions
are potentially preventable. Examples of this include admission for
angina following discharge for percutaneous transluminal coronary
angioplasty (PTCA) or admission for trauma following discharge for
Acute Myocardial Infarction (AMI). The government and other private
payers are focusing on controlling the costs associated with
readmission. Preventable readmission costs may amount to nearly $12
billion annually. The Center for Medicare and Medicaid Services
(CMS) currently mandates public reporting of readmission rates and
payers may institute financial penalties for poor performance
and/or rewards for low readmissions.
[0004] With the recent stimulus and inevitable paradigm shift
towards accountable care, organizations are focusing on cost
reduction, standardized care, and quality improvement. There is a
large, growing need to help hospitals reduce preventable rate of
readmissions to improve quality of care and avoid financial and
legal implications. Many of these preventable readmissions are
caused by discrepancies in personal health records that have not
been updated with previous or current admissions, medications (pre
and post admission) not reconciled at the time of discharge, and no
proper follow up with physicians or nurses.
SUMMARY
[0005] In various embodiments, systems, methods and computer
readable media are provided for predicting hospital readmission.
Readmission is prevented by predicting the probability of a given
patient to be readmitted. The probability alone may prevent
readmission by educating the patient or medical professional. The
probability may be predicted at the time of discharge and used to
generate a workflow action item to reduce the probability, to warn,
to output appropriate discharge instructions, and/or assist in
avoiding readmission. The probability may be specific to a
hospital, physician group, or other entity, allowing prevention to
focus on past readmission causes for the given entity.
[0006] In a first aspect, a method is provided for predicting
hospital readmission. An indication of discharge of a patient from
a medical entity is received. Application of a predictor of
readmission is triggered in response to the receiving of the
indication. The predictor of the readmission is applied to an
electronic medical record of the patient in response to the
triggering. The predictor is based on readmission data of the
medical entity. A probability of readmission of the patient is
predicted based on applying the predictor to the electronic medical
record of the patient at discharge. An output is provided as a
function of the probability.
[0007] In a second aspect, a system is provided for predicting
hospital readmission. At least one memory is operable to store data
for a plurality of readmitted patients of a first medical entity. A
first processor is configured to: identify variables contributing
to readmission for the first medical entity based on the data for
the plurality of the readmitted patients of the first medical
entity, and incorporate the variables into a predictor of
readmission for a future patient of the first medical entity.
[0008] In a third aspect, a non-transitory computer readable
storage medium has stored therein data representing instructions
executable by a programmed processor for predicting hospital
readmission. The storage medium includes instructions for
predicting a probability of readmission of a patient, the
predicting occurring at a time of discharge, comparing the
probability to a threshold, and generating an alert based on the
comparing, the generating occurring at the time of discharge.
[0009] Any one or more of the aspects described above may be used
alone or in combination. These and other aspects, features and
advantages will become apparent from the following detailed
description of preferred embodiments, which is to be read in
connection with the accompanying drawings. The present invention is
defined by the following claims, and nothing in this section should
be taken as a limitation on those claims. Further aspects and
advantages of the invention are discussed below in conjunction with
the preferred embodiments and may be later claimed independently or
in combination.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 is a flow chart diagram of one embodiment of a method
for predicting readmission;
[0011] FIG. 2 is a block diagram of one embodiment of a computer
processing system for mining patient data and/or using resulting
mined data;
[0012] FIG. 3 shows an exemplary data mining framework for mining
clinical information; and
[0013] FIG. 4 shows an exemplary computerized patient record
(CPR).
DESCRIPTION OF PREFERRED EMBODIMENTS
[0014] A majority of readmission cases may be prevented if the risk
of the patient to be readmitted is established as early as
possible. The risk of readmission is calculated from the patient
records (e.g., clinical, financial and demographic). For medical
entity specific readmission, the risk is calculated by a classifier
based on past patient data for the medical institution. For a
current patient, the system identifies whether the patient is at
risk for readmission. The risk is automatically calculated using a
predictive model. The possible reasons for risk of a particular
patient may be identified, and a plan for mitigating the risk may
be presented.
[0015] For generating the predictor from data of previous patients
and for applying the predictor for a current patient, patient data
is obtained from Electronic Medical Records (EMRs), such as patient
information databases, Radiology Information Systems (RIS),
Pharmacological Records, or other form of medical data storage or
representation. In an EMR or RIS, various data elements are
normally associated to a patient or patient visit, such as
diagnosis codes, lab results, pharmacy, insurance, doctor notes,
images, and genotypic information. Using the mined data, a computer
system predicts the risk of readmissions of a patient upon
discharge and suggests optimal plans to mitigate this risk.
[0016] The tasks for predicting the risk of readmission of a
patient are automatically performed using this combination.
Deviations and discrepancies may be identified, and mitigations to
possibly prevent the readmission may be output.
[0017] The risk of readmission may be specific to a given medical
entity. Any medical entity, such as a hospital, group of hospitals,
group of physicians, region group (e.g., hospitals in a city,
county, or state), office, insurance group, or other collection of
medical professionals associated with patients, may contribute data
to mitigation of risk of readmission. By using data associated with
a specific medical entity, risk mitigation more focused on that
entity rather than hospitals in general may be provided.
[0018] For example, a hospital may have a greater risk of
readmission for infection than hospitals in a peer group. The data
for patients previously admitted to the hospital is used to train a
predictor. A machine learns the factors at that hospital
contributing to the risk of readmission. Manually input factors may
be included as well. The factors used, the relationship between
factors, or relative weighting of the factors is specific to the
hospital. Upon discharge of a later patient of the hospital, the
risk of readmission may be predicted. Given the hospital specific
predictor, appropriate mitigation may be provided in response to
the prediction. Alerts and associated workflow actions may be
output to reduce the risk of readmission for the patient.
[0019] FIG. 1 shows a method for preventing hospital readmission.
The method is implemented by or on a computer, server, processor,
or other device. The method is provided in the order shown, but
other orders may be provided. Additional, different or fewer acts
may be provided. For example, acts 402, 404, 406, 408, 412, 414,
416, or combinations thereof are not provided. As another example,
the mining for data of act 406 is not performed as another source
of information for prediction is provided. In another example, act
416 is not provided.
[0020] Continuous (real time) or periodic prediction of the risk of
readmission is performed. Throughout the hospital stay, the care
provider may tune their care based on the most recent prediction.
Given the rise in accountable care where the care provider shares
the financial risk, prediction before scheduling discharge allows
alteration of the care of the patient in such a way that the risk
of readmission is kept low as the patient progresses on the floor.
The risk may be predicted right at the time the patient is admitted
and/or other times. As the time passes and as more data (e.g., new
labs results, new medications, new procedures, existing history
etc.) is gathered, the risk may be updated continuously for the
care provider to monitor.
[0021] In act 402, an indication of discharge of, admission of, or
new data for a patient from a medical entity is received. The
receipt is by a computer or processor. For example, a nurse or
administrator enters data for the medical record of a patient
indicating discharge. The entry may be doctor instructions to
discharge, may be that the patient is being discharged, may be
scheduling of a discharge, or may be another discharge related
entry. As another example, a new data entry is provided in the
electronic medical record of the patient. In another example, an
assistant enters data showing admission or other key trigger event
(e.g., completion of surgery, assignment of the patient to another
care group, or a change in patient status). In alternative
embodiments, the indication is not received.
[0022] In act 404, application of a predictor of readmission is
triggered. The trigger is in response to the receiving of the
indication. An automated workflow is started in response to
receiving the indication. The entry of discharge, admission, or
other information causes a processor to run a prediction
process.
[0023] The workflow determines whether there is an avoidable chance
of readmission or a probability of readmission above a norm for a
patient. This workflow occurs in response to discharge or
scheduling discharge, admission, or other event of the patient. The
triggered workflow begins prior to, during or after patient
discharge. In one embodiment, the trigger occurs, at least in part,
in real-time with patient discharge scheduling. While the patient
is in the hospital and after determining that the patient is ready
for discharge, the workflow for readmission prediction is started.
The workflow may be performed in real-time during the actual
patient stay in other embodiments, such as at admission,
periodically during a patient stay, or in response to other events
or data entry. The prediction may be performed after actual
discharge.
[0024] In other embodiments, the prediction of risk for readmission
is triggered based on an event other than discharge. For example,
one or more events identified as associated with readmission are
used as a trigger. A clinical action, entry of medication or
prescription, completion of surgery, or other entry or action
triggers the application of the predictor for the patient. Where a
given medical entity has a particular concern for readmission, such
as caused by failure to reconcile prescriptions, activity related
to that concern may trigger application (e.g., triggering when an
indication that a medication has been prescribed). The triggering
event may be different for different medical entities.
[0025] In act 406, the electronic medical record of the patient is
mined. To predict the risk of readmission, information is gathered.
The classifier for prediction has an input feature vector or group
of variables used for prediction. The values for the variables for
a particular patient are obtained by mining the electronic medical
record for the patient.
[0026] The electronic medical record for the patient is a single
database or a collection of databases. The record may include data
at or from different medical entities, such as data from a database
for a hospital and data from a database for a primary care
physician whether affiliated or not with the hospital. Data for a
patient may be mined from different hospitals. Different databases
at a same medical entity may be mined, such as mining a main
patient data system, a separate radiology system (e.g., picture
archiving and communication system), a separate pharmacy system, a
separate physician notes system, and/or a separate billing system.
Different data sources for the same and/or different medical
entities are mined.
[0027] The different data sources have a same or different format.
The mining is configured for the formats. For example, one, more,
or all of the data sources are of structured data. The data is
stored as fields with defined lengths, text limitations, or other
characteristics. Each field is for a particular variable. The
mining searches for and obtains the values from the desired fields.
As another example, one, more, or all of the data sources are of
unstructured data. Images, documents (e.g., free text), or other
collections of information without defined fields for variables is
unstructured. Physician notes may be grammatically correct, but the
punctuation does not define values for specific variables. The
mining may identify a value for one or more variables by searching
for specific criteria in the unstructured data.
[0028] Any now known or later developed mining may be used. For
example, the mining is of structured information. A specific data
source or field is searched for a value for a specific variable. As
another example, the values for variables are inferred. The values
for different variables are inferred by probabilistic combination
of probabilities associated with different possible values from
different sources. Each possible value identified in one or more
sources are assigned a probability based on knowledge
(statistically determined probabilities or professionally assigned
probabilities). The possible value to use as the actual value is
determined by probabilistic combination. The possible value with
the highest probability is selected. The selected values are
inferred values for the variables of the feature vector of the
predictor of readmission.
[0029] U.S. Pat. No. 7,617,078, the disclosure of which is
incorporated herein by reference, shows a patient data mining
method for combining electronic medical records for drawing
conclusions. This system includes extraction, combination and
inference components. The data to be extracted is present in the
hospital electronic medical records in the form of clinical notes,
procedural information, history and physical documents, demographic
information, medication records or other information. The system
combines local and global (possibly conflicting) evidences from
medical records with medical knowledge and guidelines to make
inferences over time.
[0030] U.S. Published Application No. 2003/0120458, the disclosure
of which is incorporated herein by reference, discloses mining
unstructured and structured information to extract structured
clinical data. Missing, inconsistent or possibly incorrect
information is dealt with through assignment of probability or
inference. These mining techniques are used for quality adherence
(U.S. Published Application No. 2003/0125985), compliance (U.S.
Published Application No. 2003/0125984), clinical trial
qualification (U.S. Published Application No. 2003/0130871), and
billing (U.S. Published Application No. 2004/0172297). The
disclosures of the published applications referenced in the above
paragraph are incorporated herein by reference. Other patent data
mining for mining approaches may be used, such as mining from only
structured information, mining without assignment of probability,
or mining without inferring for inconsistent, missing or incorrect
information. In alternative embodiments, values are input by a user
for applying the predictor without mining.
[0031] In act 408, a feature vector used for predicting the
probability is populated. By mining, the values for variables are
obtained. The feature vector is a list or group of variables used
to predict the likelihood of readmission. The mining outputs values
for the feature vector. The output is in a structured format. The
data from one or more data sources, such as an unstructured data
source, is mined to determine values for specific variables. The
values are in a structured format--values for defined fields are
obtained.
[0032] The mining may provide all of the values, such as resolving
any discrepancies based on probability. Any missing values may be
replaced with an average or predetermined value. The user may be
requested to enter a missing value or resolve a choice between
possible values for a variable. Alternatively, missing values are
not replaced where the predictor may operate with one or more of
the values missing.
[0033] The feature vector is populated by assigning values to
variables in a separate data storage device or location. A table
formatted for use by the predictor is stored. Alternatively, the
values are stored in the data sources from which they are mined and
pointers indicate the location for application of the
predictor.
[0034] In act 410, the probability of readmission is predicted by
applying the predictor. The predictor is a classifier or model. In
one embodiment, the predictor is a machine-trained classifier. Any
machine training may be used, such as training a statistical model
(e.g., Bayesian network). The machine-trained classifier is any one
or more classifiers. A single class or binary classifier,
collection of different classifiers, cascaded classifiers,
hierarchal classifier, multi-class classifier, model-based
classifier, classifier based on machine learning, or combinations
thereof may be used. Multi-class classifiers include CART,
K-nearest neighbors, neural network (e.g., multi-layer perceptron),
mixture models, or others. A probabilistic boosting tree may be
used. Error-correcting output code (ECOC) may be used. In one
embodiment, the machine-trained classifier is a probabilistic
boosting tree classifier. The detector is a tree-based structure
with which the posterior probabilities of readmission are
calculated from given values of variables. The nodes in the tree
are constructed by a nonlinear combination of simple classifiers
using boosting techniques. The probabilistic boosting tree (PBT)
unifies classification, recognition, and clustering into one
treatment. Alternatively, a programmed, knowledge based, or other
classifier without machine learning is used.
[0035] For learning-based approaches, the classifier is taught to
distinguish based on features. For example, a probability model
algorithm selectively combines features into a strong committee of
weak learners based on values for available variables. As part of
the machine learning, some variables are selected and others are
not selected. Those variables with the strongest or sufficient
correlation or causal relationship to readmission are selected and
variables with little or no correlation or causal relationship are
not selected. Features that are relevant to readmission are
extracted and learned in a machine algorithm based on the ground
truth of the training data, resulting in a probabilistic model. Any
size pool of features may be extracted, such as tens, hundreds, or
thousands of variables. The pool is determined by a programmer
and/or may include features systematically determined by the
machine. The training determines the most determinative features
for a given classification and discards lesser or non-determinative
features.
[0036] The classifier is trained from a training data set using a
computer. To prepare the set of training samples, actual
readmission is determined for each sample (e.g., for each patient
represented in the training data set, whether or after how long
readmission occurred is determined). Any number of medical records
for past patients is used. By using example or training data for
tens, hundreds, or thousands of examples with known readmission
status, a processor may determine the interrelationships of
different variables to the outcome of readmission. The training
data is manually acquired or mining is used to determine the values
of variables in the training data. The training may be based on
various criteria, such as readmission within a time period.
[0037] The training data is for the medical entity for which the
predictor will be applied. By using data for past patients of the
same medical entity, the variables or feature vector most relevant
to readmission for that entity are determined. Different variables
may be used by a machine-trained classifier for one medical entity
than for another medical entity. Some of the training data may be
from patients of other entities, such as using half or more of the
examples from other entities with similar readmission concerns,
sizes, or patient populations. The training data from the specific
institution may skew or still result in a different machine-learnt
classifier for the entity than using fewer examples from the
specific institution. In alternative embodiments, all of the
training data is from other medical entities, or the predictor is
trained in common for a plurality of different medical
entities.
[0038] The classifier may be trained to predict based on different
time periods, such as readmission within 30 days or after 1 year.
In alternative embodiments, the predictor is programmed, such as
using physician knowledge or the results of studies.
[0039] The classifier is trained to predict readmission in general.
Alternatively, separate classifiers are trained for different
reasons for readmission, such as training a classifier for
readmission for trauma following discharge for acute myocardial
infarction and another classifier for readmission for angina
following discharge for precutaneous transluminal coronary
angioplasty.
[0040] The learnt predictor is a matrix. The matrix provides
weights for different variables of the feature vectors. The values
for the feature vector are weighted and combined based on the
matrix. The predictor is applied by inputting the feature vector to
the matrix. Other representations than a matrix may be used.
[0041] For application, the predictor is applied to the electronic
medical record of a patient. In response to the triggering, the
values of the variables used by the learned classifier are
obtained. The values are input to the predictor as the feature
vector. The predictor outputs a probability of readmission of the
patient based on the patient's current electronic medical
record.
[0042] The probability of readmission is determined automatically.
The user may input one or more values of variables into the
electronic medical record, but the prediction is performed without
entry of values after the trigger and while applying the predictor.
Alternatively, one or more inputs are provided, such as resolving
ambiguities in values or to select an appropriate classifier (e.g.,
select a predictor of readmission for infection as opposed to
readmission for trauma).
[0043] By applying the predictor to mined information for a
patient, a probability of readmission is predicted for that
patient. The machine-learnt or other classifier outputs a
statistical probability of readmission based on the values of the
variables for the patient. Where the prediction occurs in response
to an event, such as triggering at the request of a medical
professional or administrator, the probability is predicted from
that time.
[0044] The classifier may indicate one or more values contributing
to the probability. For example, the failure to prescribe aspirin
is identified as being the strongest link or contributor to a
probability of readmission for a given patient being beyond a
threshold. This variable and value are identified. The
machine-learnt classifier may include statistics or weights
indicating the importance of different variables to readmission
and/or the normal. In combination with the values, some weighted
values may more strongly determine an increased probability of
readmission. Any deviation from norm may be highlighted. For
example, a value or weighted value of a variable a threshold amount
different from the norm or mean is identified. The difference alone
or in combination with the strength of contribution to the
probability is considered in selecting one or more values as more
significant. The more significant value or values may be
identified.
[0045] The prediction is made during the patient stay. The
prediction may be repeated at different times during the patient
stay. The prediction may be made at the time of discharge, such as
the day of discharge. The prediction may be updated, such as made
before discharge and updated after discharge based on any data
entered after the original prediction.
[0046] The probability of readmission is compared to one or more
thresholds to establish risk. The thresholds may be any probability
based on national standards, local standards, or other criteria.
The medical entity may set the thresholds to customize their
definition of low, medium or high risk patients. For example, the
medical entity sets a threshold to distinguish a probability of
readmission that is unusually high for that medical entity, for a
similar class of medical entities, for entities in a region, for a
rate important to reimbursement, or other grouping or
consideration.
[0047] The comparison may be used to identify a patient for which
further action may help reduce the probability of readmission. The
comparison may be used to place the patient in a range. The output
probability value may be used to classify the patient into
different subgroups, such as high, medium, or low risk of
readmission.
[0048] In addition, appropriate quantification of severity (Low,
Medium and High) may be used to reflect the stratification of risk.
A different classifier or the same classifier weights the
probability by the type of readmission. For more serious
complications or reasons for readmission, a lesser probability may
still be quantified as higher severity.
[0049] In alternative embodiments of creating and applying the
predictor, the prediction of readmission is integrated as a
variable to be mined. The inference component determines the
probability based on combination of probabilistic factoids or
elements. The probability of readmission is treated as part of the
patient state to be mined. Domain knowledge determines the
variables used for combining to output the probability of
readmission.
[0050] An output is provided in act 412. The output is a function
of the probability. The probability is used in a further workflow
or output. For example, the probability causes a job or action item
in a workflow in an effort to reduce the probability. As another
example, the probability with or without identification of the most
significantly contributing value or values and/or type of
readmission predicted is used to recommend the type of follow-up,
discharge instructions, or other clinical action.
[0051] This analysis may be performed in real time. If performed in
real time, suggestions and/or corrections are output based on the
probability. The suggestions and/or corrections may reduce the risk
in a timely manner. Retrospective analysis may establish the top
readmission reasons for the patients at a particular institution
medical entity and possibly suggest alternative workflows based on
best clinical practices.
[0052] In one embodiment, an alert is generated based on the
comparing of the probability to the threshold or thresholds. The
alert is generated during the patient stay, at the time of
discharge (e.g., when a medical professional is preparing discharge
papers), or other times.
[0053] The alert is sent via text, email, voice mail, voice
response, or network notification. The alert indicates the level of
risk of readmission, allowing mitigation when desired or
appropriate. The alert is sent to the patient, family member,
treating physician, nurse, primary care physician, and/or other
medical professional. The alert may be transmitted to a computer,
cellular phone, tablet, bedside monitor of the patient, or other
device. The recipient of the alert may examine why the probability
is beyond the threshold, determine changes in workflow to reduce
the risk of readmission for other patients, and/or take actions to
reduce the risk for the patient for which the alert was
generated.
[0054] The alert indicates the patient and a risk of readmission.
Other information may be provided alternatively or additionally,
such as identification of one or more values and corresponding
variables correlating with the severity or risk level.
[0055] In one embodiment, the alert is generated as a displayed
warning while preventing entry of discharge or other information.
The user is prevented from scheduling discharge or entering other
data where the probability of readmission and/or severity of
predicated readmission are sufficiently high. In response to the
user attempting to schedule discharge or enter information
associated with the patient, the alert is generated and the user is
prevented from entering or saving the information. The prevention
is temporary (e.g., seconds or minutes), may remain until the
probability has been reduced or require an over-ride from an
authorized personnel (e.g. a case manager or an attending
physician). The prevention may be for one type of data entry (e.g.,
discharge scheduling) but allow another type (e.g., medication
reconciliation) to reduce the risk of readmission.
[0056] A user may be requested to enter additional information to
help improve readmissions rates in general, such as the user
reconciling different prescriptions, scheduling a follow-up,
resolving discrepancies in the electronic medical record, resolving
a lack of adherence to a guideline, completing documentation in the
electronic medical record, or arranging for a clinical action. The
system may output a list of variables that can be considered to
reduce the risk of readmission. At least one variable having a
value for the patient associated with a strong, stronger, or
strongest link to the probability is output. For example, a patient
has an unusually high measured blood characteristic, indicating a
possible infection. This high value may be the most significant
reason for a probability of readmission above a threshold. Most
significant or significant may be based on the weight for the
variable and the value in determining the probability or be based
on a combination of factors (e.g., the relative strength or weight
and the amount of deviance from a threshold). The strength of the
link may be relative to links for other values of other variables
to the risk of readmission. The reasons for the risk of readmission
are identified.
[0057] Recommendations may be made based on the identified
variable, variables, or combination of variables. For example,
based on the past and current medical records of a patient, it can
be determined whether the personal health record of the patient has
been updated or not with the current admission. Where the
probability of readmission is based, at least in part, on old
information, a recommendation to document or update the record is
provided. Similarly, it can be highlighted whether the medications
have been reconciled or not.
[0058] The recommendation is textual, such as providing
instructions. Other recommendations may be visual. A visual
representation of the relationship of the probability to the
patient record may assist user understanding. The visual
representation is output on a display or printed. The visual
representation of the relationship links elements or factoids
(variables) to the resulting risk of readmission. The values for
the variables from a specific patient record are inserted. A
pictorial representation of the contribution of different
variables, based on the values, to the risk may assist the user in
general understanding of how any conclusions are supported by
inputs.
[0059] The visual representation shows the dependencies between the
data and conclusions. The dependencies may be actual or imaginary.
For example, a machine learning technique may be used. The
relationship of a given input to the actual output may be unknown,
but a statistical correlation may be identified by machine
learning. To assist in user understanding, a relationship may be
graphically represented without actual dependency, such as
probability or relative weighting, being known.
[0060] The visual representation may have any number of inputs,
outputs, nodes or links. The types of data are shown. The relative
contribution of an input to a given output may be shown, such as
colors, bold, or breadth of a link indicating a weight. The data
source or sources used to determine the values of the variables may
be shown (e.g., billing record, prescription database or
others).
[0061] The probability of readmission and/or variables associated
with the probability of readmission for a particular patient may be
used to determine a mitigation plan. The mitigation plan includes
instructions, prescriptions, education materials, schedules,
clinical actions, or other information that may reduce the risk of
readmission. The next recommended clinical actions or reminders for
the next recommended clinical actions may be output so that health
care personnel are better able to follow the recommendations.
[0062] A library of mitigation plans is provided. Separate plans
may be provided for different reasons for possible readmission,
different variables causing a higher risk of readmission, and/or
different combinations of both. The plan or plans appropriate for a
given patient are obtained and output.
[0063] The output may be based on a criteria set for the medical
entity. For example, the medical entity may set the threshold for
comparison to be more or less inclusive of different levels of
risk. As another example, the medical entity may select a
combination of factors to trigger an alert, such as probability
level and types of variables contributing to the probability level.
If one variable causes the predictor to regularly and inaccurately
predict a risk higher than the threshold amount, then patients with
higher probability based just or mostly on that variable may not
have an alert output or a different alert may be output.
[0064] The output may be discharge instructions for the patient
and/or medical professional (e.g., treating and/or primary care
physician). The discharge instructions may include the mitigation
plan. Alternatively or additionally, the discharge instructions
include the predicted probability. Patients or physicians may be
more likely to take corrective or preventative actions where the
probability of readmission is known. The instruction may indicate
the difference in probability if a value is changed and by how
much, showing benefit to change in behavior or performance of
clinical or medical action. Recommendations may be made to mitigate
the risks. Semi-automated discharge instructions based on the
longitudinal clinical record are created. In other embodiments, the
output is a mitigation plan to be performed during the patient's
stay.
[0065] An optimal follow-up strategy (e.g., phone call, in-home
follow-up, or visit to a doctor) may be provided in the
instructions. The follow-up strategy may be selected or determined
based on the probability of readmission and/or the variables
contributing to the probability of readmission being beyond the
threshold. For example, an in-home follow-up is scheduled for a
probability further beyond the threshold (e.g., beyond another
threshold in a stratification of risk), and a phone call is
scheduled for a probability closer to the threshold (e.g., for a
lower risk). As another example, the severity of the type of
readmission predicted is considered. The probability may be
utilized to manage the care and suggest possible and alternative
care plans for optimal patient outcomes.
[0066] In another embodiment, a job entry in a workflow is
automatically scheduled as a function of the probability. A
computerized workflow system includes action items to be performed
by different individuals. The action items are communicated to the
individual in a user interface for the workflow, by email, by text
message, by placement in a calendar, or by other mechanism.
[0067] The workflow job is generated for a case manager. The job
entry may be made to avoid readmission. The job entry may be to
update patient data, arrange for clinical action, update a
prescription, arrange for a prescription, review test results,
arrange for testing, schedule a follow-up, review the probability,
review patient data, or other action to reduce the probability of
readmission. For example, where a follow-up is not scheduled during
discharge and is not automatically arranged, arranging for the
follow-up may be placed as an action item in an administrator's,
assistant's, nurse's, or other case manager's workflow. As another
example, review of test results is placed in a physician's workflow
so that appropriate action may be taken before or after discharge.
A blood sample or other test may be performed at discharge, but the
results not available before discharge. This may occur, for
example, where the predictor identifies a probability of
readmission beyond the threshold due to missing information. The
test is ordered to provide the missing information. A workflow
action is automatically scheduled to examine the test results and
take appropriate action to avoid readmission. Similarly, a workflow
action may be scheduled during the patient's stay to avoid a higher
risk of readmission.
[0068] The workflow action item may be generated to review reasons
for readmission after readmission. Where a patient is readmitted, a
retrospective analysis may be performed in an effort to identify
what could or should have been done differently. A case manager,
such as an administrator of a hospital, may predict the probability
of readmission based on the data at the time of the previous
discharge or review the saved probability. The discharge
instructions, workflow action items, or other use of the
probability may be examined to determine if other action was
warranted. Future workflow action items, discharge instructions,
physician education, or other actions may be performed to avoid
similar reasons for readmission in other patients. A correlation
study of readmitted patients may indicate common problems or
trends.
[0069] The workflow is a separate application that queries the
results of the mining and/or prediction of probability of
readmission. The workflow uses the results or is included as part
of the predictor application. Any now known or later developed
software or system providing a workflow engine may be configured to
initiate a workflow based on data.
[0070] The workflow system may be configured to monitor adherence
to the action items. Reminders may be automatically generated where
an action item is due or past due so that health care providers are
better able to follow the recommendations.
[0071] Other predictors or statistical classifiers may be provided.
One example predictor is for compliance by the patient with the
discharge or other instructions. A level of risk (i.e., risk
stratification) and/or reasons for risk are predicted. The ground
truth for compliance may rely on patient surveys or questionnaires.
The predictor for whether a patient will comply is trained from the
training data. Different predictors may be generated for different
groups, such as by type of condition. The variables used for
training may be the same or different than for training the
predictor of readmission. The trained predictor of compliance may
have a different or same feature vector as the predictor of
readmission. Mining is performed to determine the values for
training and/or the values for application.
[0072] The predictor for compliance is triggered for application at
the time of discharge or when other instructions are given to the
patient, but may be performed at other times. The values of
variables in the feature vector of the predictor of compliance are
input to the predictor. The application of the predictor to the
electronic medical record of the patient results in an output
probability of compliance by the patient. The reasons for the
probability being beyond a threshold or thresholds may also be
output, such as a lack of insurance or high medication cost
contributing as a strong or stronger link to the probability being
beyond the threshold. For example, a patient may be discharged to
an unknown location (no home or hospice listed in the discharge
location variable). An unknown location may occur for homeless
patients whom are less able to adhere to a care plan. The discharge
location being unknown may be output so that a care provider may
make subsequent care arrangements before discharge or assign a case
worker to assist with adherence.
[0073] The probability of compliance may be used to modify the
discharge or other instructions and/or workflow action items. For
example, the type of follow-up may be more intensive or thorough
where the probability of compliance is low. As another example, a
workflow action may be generated to identify alternative medicines
where the cost of medication is high. A consultation with a social
worker may be arranged and/or the discharge instructions based on
lower cost alternatives may be provided where the patient does not
have insurance.
[0074] FIG. 2 is a block diagram of an example computer processing
system 100 for implementing the embodiments described herein, such
as preventing hospital or medical entity readmission. The systems,
methods and/or computer readable media may be implemented in
various forms of hardware, software, firmware, special purpose
processors, or a combination thereof. Some embodiments are
implemented in software as a program tangibly embodied on a program
storage device. By implementing with a system or program,
completely or semi-automated workflows, predictions, classifying,
and/or data mining are provided to assist a person or medical
professional.
[0075] The system 100 is for generating a predictor, such as
implementing machine learning to train a statistical classifier.
Alternatively or additionally, the system 100 is for applying the
predictor. The system 100 may also implement associated
workflows.
[0076] The system 100 is a computer, personal computer, server,
PACs workstation, imaging system, medical system, network
processor, or other now know or later developed processing system.
The system 100 includes at least one processor (hereinafter
processor) 102 operatively coupled to other components via a system
bus 104. The program may be uploaded to, and executed by, a
processor 102 comprising any suitable architecture. Likewise,
processing strategies may include multiprocessing, multitasking,
parallel processing and the like. The processor 102 is implemented
on a computer platform having hardware such as one or more central
processing units (CPU), a random access memory (RAM), and
input/output (I/O) interface(s). The computer platform also
includes an operating system and microinstruction code. The various
processes and functions described herein may be either part of the
microinstruction code or part of the program (or combination
thereof) which is executed via the operating system. Alternatively,
the processor 102 is one or more processors in a network and/or on
an imaging system.
[0077] The processor 102 is configured to learn a classifier, such
as creating a predictor of readmission from training data, to mine
the electronic medical record of the patient or patients, and/or to
apply a machine-learnt classifier to predict the probability of
readmission. Training and application of a trained classifier are
first discussed below. Example embodiments for mining follow.
[0078] For training, the processor 102 determines the relative or
statistical contribution of different variables to the outcome,
readmission. A programmer may select variables to be considered.
The programmer may influence the training, such as assigning
limitations on the number of variables and/or requiring inclusion
of one or more variables to be used as the input feature vector of
the final classifier. By training, the classifier identifies
variables contributing to readmission. Where the training data is
for patients from a given medical entity, the learning identifies
the variables most appropriate or determinative for readmission
based on discharge from that medical entity. The training
incorporates the variables into a predictor of readmission for a
future patient of the medical entity.
[0079] For application, the processor 102 applies the resulting
(machine-learned) statistical model to the data for a patient. For
each patient or for each patient in a category of patients (e.g.,
patients treated for a specific condition or by a specific group
within a medical entity), the predictor is applied to the data for
the patient. The values for the identified and incorporated
variables of the machine-learnt statistical model are input as a
feature vector. A matrix of weights and combinations of weighted
values calculates a probability of readmission.
[0080] The predictor of readmission may have any accuracy. For
example, the receiver operating characteristic (ROC) curve may show
an area of about 81% and a standard of deviation of about 1.7%
where, at about 0% false alarms, about 70% of the eventual
readmissions are predicted. Other performance may be provided.
[0081] The processor 102 associates different workflows with
different possible predictions of the predictor. The probability of
readmission, the probability of compliance, severity, and/or most
determinative values may be different for different patients. One
or a combination of these factors is used to select an appropriate
workflow or action. Different predictions or probabilities of
readmission may result in different jobs to be performed and/or
different instructions.
[0082] The processor 102 is operable to assign actions or to
perform workflow actions. For example, the processor 102 initiates
contact for follow-up by electronically notifying a patient in
response to identifying a probability of readmission. As another
example, the processor 102 requests documentation to resolve
ambiguities in a medical record. In another example, the processor
102 generates a request for clinical action likely to decrease a
probability of readmission. Clinical actions may include a test
order, recommended action, request for patient information, other
source of obtaining clinical information, or combinations thereof.
To decrease a probability of readmission, the processor 102 may
generate a prescription form, clinical order (e.g., test order), or
other workflow action.
[0083] In a real-time usage, the processor 102 receives currently
available medical information for a patient. Based on the currently
available information and mining the patient record, the processor
102 may indicate how to mitigate risk of readmission. The actions
may then be performed during the treatment or before discharge.
[0084] The processor 102 implements the operations as part of the
system 100 or a plurality of systems. A read-only memory (ROM) 106,
a random access memory (RAM) 108, an I/O interface 110, a network
interface 112, and external storage 114 are operatively coupled to
the system bus 104 with the processor 102. Various peripheral
devices such as, for example, a display device, a disk storage
device (e.g., a magnetic or optical disk storage device), a
keyboard, printing device, and a mouse, may be operatively coupled
to the system bus 104 by the I/O interface 110 or the network
interface 112.
[0085] The computer system 100 may be a standalone system or be
linked to a network via the network interface 112. The network
interface 112 may be a hard-wired interface. However, in various
exemplary embodiments, the network interface 112 may include any
device suitable to transmit information to and from another device,
such as a universal asynchronous receiver/transmitter (UART), a
parallel digital interface, a software interface or any combination
of known or later developed software and hardware. The network
interface may be linked to various types of networks, including a
local area network (LAN), a wide area network (WAN), an intranet, a
virtual private network (VPN), and the Internet.
[0086] The instructions and/or patient record are stored in a
non-transitory computer readable memory, such as the external
storage 114. The same or different computer readable media may be
used for the instructions and the patient record data. The external
storage 114 may be implemented using a database management system
(DBMS) managed by the processor 102 and residing on a memory such
as a hard disk, RAM, or removable media. Alternatively, the storage
114 is internal to the processor 102 (e.g. cache). The external
storage 114 may be implemented on one or more additional computer
systems. For example, the external storage 114 may include a data
warehouse system residing on a separate computer system, a PACS
system, or any other now known or later developed hospital, medical
institution, medical office, testing facility, pharmacy or other
medical patient record storage system. The external storage 114, an
internal storage, other computer readable media, or combinations
thereof store data for at least one patient record for a patient.
The patient record data may be distributed among multiple storage
devices or in one location.
[0087] The patient data for training a machine learning classifier
is stored. The training data includes data for patients that have
been readmitted and data for patients that have not been readmitted
after a selected time. The patients are for a same medical entity,
group of medical entities, region, or other collection.
[0088] Alternatively or additionally, the data for applying a
machine-learnt classifier is stored. The data is for a patient
being treated or ready for discharge. The memory stores the
electronic medical record of one or more patients. Links to
different data sources may be provided or the memory is made up of
the different data sources. Alternatively, the memory stores
extracted values for specific variables.
[0089] The instructions for implementing the processes, methods
and/or techniques discussed herein are provided on
computer-readable storage media or memories, such as a cache,
buffer, RAM, removable media, hard drive or other computer readable
storage media. Computer readable storage media include various
types of volatile and nonvolatile storage media. The functions,
acts or tasks illustrated in the figures or described herein are
executed in response to one or more sets of instructions stored in
or on computer readable storage media. The functions, acts or tasks
are independent of the particular type of instructions set, storage
media, processor or processing strategy and may be performed by
software, hardware, integrated circuits, firmware, micro code and
the like, operating alone or in combination. In one embodiment, the
instructions are stored on a removable media device for reading by
local or remote systems. In other embodiments, the instructions are
stored in a remote location for transfer through a computer network
or over telephone lines. In yet other embodiments, the instructions
are stored within a given computer, CPU, GPU or system. Because
some of the constituent system components and method steps depicted
in the accompanying figures are preferably implemented in software,
the actual connections between the system components (or the
process steps) may differ depending upon the manner in which the
present embodiments are programmed.
[0090] Health care providers may employ automated techniques for
information storage and retrieval. The use of a computerized
patient record (CPR) (e.g., an electronic medical record) to
maintain patient information is one such example. As shown in FIG.
4, an exemplary CPR 200 includes information collected over the
course of a patient's treatment or use of an institution. This
information may include, for example, computed tomography (CT)
images, X-ray images, laboratory test results, doctor progress
notes, details about medical procedures, prescription drug
information, radiological reports, other specialist reports,
demographic information, family history, patient information, and
billing (financial) information.
[0091] A CPR may include a plurality of data sources, each of which
typically reflects a different aspect of a patient's care.
Alternatively, the CPR is integrated into one data source.
Structured data sources, such as financial, laboratory, and
pharmacy databases, generally maintain patient information in
database tables. Information may also be stored in unstructured
data sources, such as, for example, free text, images, and
waveforms. Often, key clinical findings are only stored within
unstructured physician reports, annotations on images or other
unstructured data source.
[0092] Referring to FIG. 2, the processor 102 executes the
instructions stored in the computer readable media, such as the
storage 114. The instructions are for mining patient records (e.g.,
the CPR), predicting readmission, assigning workflow jobs, other
functions, or combinations thereof. For training and/or application
of the predictor of readmission, values of variables are used. The
values for particular patients are mined from the CPR. The
processor 102 mines the data to provide values for the
variables.
[0093] Any technique may be used for mining the patient record,
such as structured data based searching. In one embodiment, the
methods, systems and/or instructions disclosed in U.S. Published
Application No. 2003/0120458 are used, such as for mining from
structured and unstructured patient records. FIG. 3 illustrates an
exemplary data mining system implemented by the processor 102 for
mining a patient record to create high-quality structured clinical
information. The processing components of the data mining system
are software, firmware, microcode, hardware, combinations thereof,
or other processor based objects. The data mining system includes a
data miner 350 that mines information from a CPR 310 using
domain-specific knowledge contained in a knowledge base 330. The
data miner 350 includes components for extracting information from
the CPR 352, combining all available evidence in a principled
fashion over time 354, and drawing inferences from this combination
process 356. The mined information may be stored in a structured
CPR 380. The architecture depicted in FIG. 4 supports plug-in
modules wherein the system may be easily expanded for new data
sources, diseases, and hospitals. New element extraction
algorithms, element combining algorithms, and inference algorithms
can be used to augment or replace existing algorithms.
[0094] The mining is performed as a function of domain knowledge.
The domain knowledge provides an indication of reliability of a
value based on the source or context. For example, a note
indicating the patient is a smoker may be accurate 90% of the time,
so a 90% probability is assigned. A blood test showing nicotine may
indicate that the patient is a smoker with 60% accuracy.
[0095] Detailed knowledge regarding the domain of interest, such
as, for example, a disease of interest, guides the process to
identify relevant information. This domain knowledge base 330 can
come in two forms. It can be encoded as an input to the system, or
as programs that produce information that can be understood by the
system. For example, a study determines factors contributing to
readmission. These factors and their relationships may be used to
mine for values. The study is used as domain knowledge for the
mining. Additionally or alternatively, the domain knowledge base
330 may be learned from test data.
[0096] The domain-specific knowledge may also include
disease-specific domain knowledge. For example, the
disease-specific domain knowledge may include various factors that
influence risk of a disease, disease progression information,
complications information, outcomes, and variables related to a
disease, measurements related to a disease, and policies and
guidelines established by medical bodies.
[0097] The information identified as relevant by the study,
guidelines for treatment, medical ontologies, or other sources
provides an indication of probability that a factor or item of
information indicates or does not indicate a particular value of a
variable. The relevance may be estimated in general, such as
providing a relevance for any item of information more likely to
indicate a value as 75% or other probability above 50%. The
relevance may be more specific, such as assigning a probability of
the item of information indicating a particular diagnosis based on
clinical experience, tests, studies or machine learning. Based on
the domain-knowledge, the mining is performed as a function of
existing knowledge, guidelines, or best practices regarding
readmissions. The domain knowledge indicates elements with a
probability greater than a threshold value of indicating the
patient state (i.e., collection of values). Other probabilities may
be associated with combinations of information.
[0098] Domain-specific knowledge for mining the data sources may
include institution-specific domain knowledge. For example,
information about the data available at a particular hospital,
document structures at a hospital, policies of a hospital,
guidelines of a hospital, and any variations of a hospital. The
domain knowledge guides the mining, but may guide without
indicating a particular item of information from a patient
record.
[0099] The extraction component 352 deals with gleaning small
pieces of information from each data source regarding a patient or
plurality of patients. The pieces of information or elements are
represented as probabilistic assertions about the patient at a
particular time. Alternatively, the elements are not associated
with any probability. The extraction component 352 takes
information from the CPR 310 to produce probabilistic assertions
(elements) about the patient that are relevant to an instant in
time or period. This process is carried out with the guidance of
the domain knowledge that is contained in the domain knowledge base
330. The domain knowledge for extraction is generally specific to
each source, but may be generalized.
[0100] The data sources include structured and/or unstructured
information. Structured information may be converted into
standardized units, where appropriate. Unstructured information may
include ASCII text strings, image information in DICOM (Digital
Imaging and Communication in Medicine) format, and text documents
partitioned based on domain knowledge. Information that is likely
to be incorrect or missing may be noted, so that action may be
taken. For example, the mined information may include corrected
information, including corrected ICD-9 diagnosis codes.
[0101] Extraction from a database source may be carried out by
querying a table in the source, in which case, the domain knowledge
encodes what information is present in which fields in the
database. On the other hand, the extraction process may involve
computing a complicated function of the information contained in
the database, in which case, the domain knowledge may be provided
in the form of a program that performs this computation whose
output may be fed to the rest of the system.
[0102] Extraction from images, waveforms, etc., may be carried out
by image processing or feature extraction programs that are
provided to the system.
[0103] Extraction from a text source may be carried out by phrase
spotting, which requires a list of rules that specify the phrases
of interest and the inferences that can be drawn there from. For
example, if there is a statement in a doctor's note with the words
"There is evidence of metastatic cancer in the liver," then, in
order to infer from this sentence that the patient has cancer, a
rule is needed that directs the system to look for the phrase
"metastatic cancer," and, if it is found, to assert that the
patient has cancer with a high degree of confidence (which, in the
present embodiment, translates to generate an element with name
"Cancer", value "True" and confidence 0.9).
[0104] The combination component 354 combines all the elements that
refer to the same variable at the same time period to form one
unified probabilistic assertion regarding that variable.
Combination includes the process of producing a unified view of
each variable at a given point in time from potentially conflicting
assertions from the same/different sources. These unified
probabilistic assertions are called factoids. The factoid is
inferred from one or more elements. Where the different elements
indicate different factoids or values for a factoid, the factoid
with a sufficient (thresholded) or highest probability from the
probabilistic assertions is selected. The domain knowledge base may
indicate the particular elements used. Alternatively, only elements
with sufficient determinative probability are used. The elements
with a probability greater than a threshold of indicating a patient
state (e.g., directly or indirectly as a factoid), are selected. In
various embodiments, the combination is performed using domain
knowledge regarding the statistics of the variables represented by
the elements ("prior probabilities").
[0105] The patient state is an individual model of the state of a
patient. The patient state is a collection of variables that one
may care about relating to the patient, such as established by the
domain knowledgebase. The information of interest may include a
state sequence, i.e., the value of the patient state at different
points in time during the patient's treatment.
[0106] The inference component 356 deals with the combination of
these factoids, at the same point in time and/or at different
points in time, to produce a coherent and concise picture of the
progression of the patient's state over time. This progression of
the patient's state is called a state sequence. The patient state
is inferred from the factoids or elements. The patient state or
states with a sufficient (thresholded), high probability or highest
probability is selected as an inferred patient state or
differential states.
[0107] Inference is the process of taking all the factoids and/or
elements that are available about a patient and producing a
composite view of the patient's progress through disease states,
treatment protocols, laboratory tests, clinical action or
combinations thereof. Essentially, a patient's current state can be
influenced by a previous state and any new composite observations.
The risk for readmission may be considered as a patient state so
that the mining determines the risk without a further application
of a separate model.
[0108] The domain knowledge required for this process may be a
statistical model that describes the general pattern of the
evolution of the disease of interest across the entire patient
population and the relationships between the patient's disease and
the variables that may be observed (lab test results, doctor's
notes, or other information). A summary of the patient may be
produced that is believed to be the most consistent with the
information contained in the factoids, and the domain
knowledge.
[0109] For instance, if observations seem to state that a cancer
patient is receiving chemotherapy while he or she does not have
cancerous growth, whereas the domain knowledge states that
chemotherapy is given only when the patient has cancer, then the
system may decide either: (1) the patient does not have cancer and
is not receiving chemotherapy (that is, the observation is probably
incorrect), or (2) the patient has cancer and is receiving
chemotherapy (the initial inference--that the patient does not have
cancer--is incorrect); depending on which of these propositions is
more likely given all the other information. Actually, both (1) and
(2) may be concluded, but with different probabilities.
[0110] As another example, consider the situation where a statement
such as "The patient has metastatic cancer" is found in a doctor's
note, and it is concluded from that statement that <cancer=True
(probability=0.9)>. (Note that this is equivalent to asserting
that <cancer=True (probability=0.9), cancer=unknown
(probability=0.1)>).
[0111] Now, further assume that there is a base probability of
cancer <cancer=True (probability=0.35), cancer=False
(probability=0.65)> (e.g., 35% of patients have cancer). Then,
this assertion is combined with the base probability of cancer to
obtain, for example, the assertion <cancer=True
(probability=0.93), cancer=False (probability=0.07)>.
[0112] Similarly, assume conflicting evidence indicated the
following:
[0113] 1. <cancer=True (probability=0.9), cancer=unknown
probability=0.1)>
[0114] 2. <cancer=False (probability=0.7), cancer=unknown
(probability=0.3)>
[0115] 3. <cancer=True (probability=0.1), cancer=unknown
(probability=0.9)> and
[0116] 4. <cancer=False (probability=0.4), cancer=unknown
(probability=0.6)>.
[0117] In this case, we might combine these elements with the base
probability of cancer <cancer=True (probability=0.35),
cancer=False (probability=0.65)> to conclude, for example, that
<cancer=True (prob=0.67), cancer=False (prob=0.33)>.
[0118] Numerous data sources may be assessed to gather the
elements, and deal with missing, incorrect, and/or inconsistent
information. As an example, consider that, in determining whether a
patient has diabetes, the following information might be
extracted:
[0119] (a) ICD-9 billing codes for secondary diagnoses associated
with diabetes;
[0120] (b) drugs administered to the patient that are associated
with the treatment of diabetes (e.g., insulin);
[0121] (c) patient's lab values that are diagnostic of diabetes
(e.g., two successive blood sugar readings over 250 mg/d);
[0122] (d) doctor mentions that the patient is a diabetic in the
H&P (history & physical) or discharge note (free text);
and
[0123] (e) patient procedures (e.g., foot exam) associated with
being a diabetic.
As can be seen, there are multiple independent sources of
information, observations from which can support (with varying
degrees of certainty) that the patient is diabetic (or more
generally has some disease/condition). Not all of them may be
present, and in fact, in some cases, they may contradict each
other. Probabilistic observations can be derived, with varying
degrees of confidence. Then these observations (e.g., about the
billing codes, the drugs, the lab tests, etc.) may be
probabilistically combined to come up with a final probability of
diabetes. Note that there may be information in the patient record
that contradicts diabetes. For instance, the patient has some
stressful episode (e.g., an operation) and his blood sugar does not
go up.
[0124] The above examples are presented for illustrative purposes
only and are not meant to be limiting. The actual manner in which
elements are combined depends on the particular domain under
consideration as well as the needs of the users of the system.
Further, while the above discussion refers to a patient-centered
approach, actual implementations may be extended to handle multiple
patients simultaneously. Additionally, a learning process may be
incorporated into the domain knowledge base 330 for any or all of
the stages (i.e., extraction, combination, inference).
[0125] The system may be run at arbitrary intervals, periodic
intervals, or in online mode. When run at intervals, the data
sources are mined when the system is run. In online mode, the data
sources may be continuously mined. The data miner may be run using
the Internet. The created structured clinical information may also
be accessed using the Internet. Additionally, the data miner may be
run as a service. For example, several hospitals may participate in
the service to have their patient information mined, and this
information may be stored in a data warehouse owned by the service
provider. The service may be performed by a third party service
provider (i.e., an entity not associated with the hospitals).
[0126] Once the structured CPR 380 is populated with patient
information, it will be in a form where it is conducive for
answering questions regarding individual patients, and about
different cross-sections of patients. The values are available for
use in predicting readmission.
[0127] The domain knowledgebase, extractions, combinations and/or
inference may be responsive or performed as a function of one or
more variables. For example, the probabilistic assertions may
ordinarily be associated with an average or mean value. However,
some medical practitioners or institutions may desire that a
particular element be more or less indicative of a patient state. A
different probability may be associated with an element. As another
example, the group of elements included in the domain knowledge
base for a predictor of readmission may be different for different
medical entities. The threshold for sufficiency of probability or
other thresholds may be different for different people or
situations.
[0128] Other variables may be use or institution specific. For
example, different definitions of a primary care physician may be
provided. A number of visits threshold may be used, such as
visiting the same doctor 5 times indicating a primary care
physician. A proximity to a patient's residence may be used.
Combinations of factors may be used.
[0129] The user may select different settings. Different users in a
same institution or different institutions may use different
settings. The same software or program operates differently based
on receiving user input. The input may be a selection of a specific
setting or may be selection of a category associated with a group
of settings.
[0130] The mining, such as the extraction, and/or the inferring,
such as the combination, are performed as a function of the
selected threshold. By using a different upper limit of normal for
the patient state, a different definition of information used in
the domain knowledge or other threshold selection, the patient
state or associated probability may be different. User's with
different goals or standards may use the same program, but with the
versatility to more likely fulfill the goals or standards.
[0131] Various improvements described herein may be used together
or separately. Although illustrative embodiments of the present
invention have been described 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 other
changes and modifications may be affected therein by one skilled in
the art without departing from the scope or spirit of the
invention.
* * * * *