Case management system using a medical event forecasting engine

• US 20160328526A1
• Filed: 04/07/2016
• Published: 11/10/2016
• Est. Priority Date: 04/07/2015
• Status: Abandoned Application

• Current Assignee: Accordion Health, Inc.
• Original Assignee: Accordion Health, Inc.
• Inventors: Park, Yubin, Ho, Joyce, Vishwanath, Sriram
• Application Number:
  US15/092,738
• Publication Number:
  US 20160328526A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A case management tool uses a novel event forecast engine. The engine leverages a flexible and extensible data structure that combines diverse formats of claims (e.g., both medical and pharmacy) and that highlights “episodes” rather than items. The engine also makes predictions with respect to “cohorts” groups, where cohorts are defined using both static and dynamic features, the latter being features that change their values based on observation periods. Multiple definitions of cohorts are implemented, and optimal cohort definitions are estimated. The engine uses rolling time window processing to extract dynamic features in the data sets, and then one or more machine learning algorithms are applied to the extracted data. Cohort-wise machine learning models preferably learn on dynamic features, which are then put together for final predictions. A validation step is applied when outcomes are later observed. Validation results update the cohort definitions as well as the model parameters.

Claims

1. A method of case management, comprising:

predicting one or more future events and their anticipated timing for patients by;

receiving and merging disparate health episode data sets into patient-specific data objects, wherein a patient-specific data object is a collection of episodes associated with the patient;

from the patient-specific data objects, processing a patient population into one or more cohorts, wherein a cohort is associated with one or more dynamic features whose values depend on one or more observation periods, each observation period defined by a rolling window;

applying one or more rolling windows to the one or more cohorts to extract one or more of the dynamic features; and

applying machine learning using the one or more dynamic features extracted to generate predictions for the one or more future events and their anticipated timing; and

providing the predictions to drive a case management operation.

2. The method as described in claim 1 wherein the case management operation generates a chase list of patients to be contacted by a case manager.

3. The method as described in claim 1 wherein the case management operation outputs a timeline indicating the one or more future events and their anticipated timing.

4. The method as described in claim 1 wherein the case management operation outputs potential outcomes for different care path scenarios for a patient.

5. The method as described in claim 1 wherein the case management operation outputs a tree diagram to visualize forecasted medical pathways based on one or more input constraints associated with a patient.

6. The method as described in claim 1 wherein, in addition to predicted values for future events, the machine learning also generates additional information.

7. The method as described in claim 6 wherein the additional information includes one or more machine learning model parameters, and other data that is one of:

a transition probability between future events, drift momentum data, and a latent representation of a time series associated with a rolling window.

8. The method as described in claim 1 wherein predicting the one or more future events further includes blending predictions generated from at least first and second machine learning algorithms to reduce bias.

9. The method as described in claim 8 wherein predicting the one or more future events further includes validating the blended predictions.

10. The method as described in claim 9 wherein predicting the one or more future events further includes adjusting a cohort definition based on a result of a validation of the blended predictions.

11. The method as described in claim 9 wherein predicting the one or more future events further includes adjusting a machine learning model parameter based on a result of a validation of the blended predictions.

12. The method as described in claim 1 further including augmenting a patient-specific data object to include a cohort of which the patient has been determined to be associated.

13. Apparatus for case management, comprising:

a case management system comprising one or more computing machines, and a web-based user interface for display of case management-specific information; and

a forecast engine executing as software in one or more hardware processors, the forecast engine operative to;

receive and merge disparate health episode data sets into patient-specific data objects, wherein a patient-specific data object is a collection of episodes associated with the patient;

from the patient-specific data objects, process a patient population into one or more cohorts, wherein a cohort is associated with one or more dynamic features whose values depend on one or more observation periods, each observation period defined by a rolling window;

apply one or more rolling windows to the one or more cohorts to extract one or more of the dynamic features; and

apply machine learning using the one or more dynamic features extracted to generate predictions for the one or more future events and their anticipated timing; and

outputting information on the web-based user interface based on the predictions to provide an improved case management operation.

14. The apparatus as described in claim 13 further include a data store to store the patient-specific data objects.

15. The apparatus as described in claim 14 wherein the data store is non-relational document data store that stores the patient-specific data objects in JSON format.

16. The apparatus as described in claim 13 wherein the disparate health episode data sets are merged in the patient-specific data objects by a data transformation.

17. A product, comprising:

a non-transitory computer readable storage device; and

computer readable instructions stored by the storage device;

wherein the computer readable instructions include instruction sets respectively written to cause a computer to perform the following operations in association with a case management tool;

(a) receive and merge disparate health episode data sets into patient-specific data objects, wherein a patient-specific data object is a collection of episodes associated with the patient marking dynamic output from a first web application;

(b) from the patient-specific data objects, process a patient population into one or more cohorts, wherein a cohort is associated with one or more dynamic features whose values depend on one or more observation periods, each observation period defined by a rolling window;

(c) apply one or more rolling windows to the one or more cohorts to extract one or more of the dynamic features; and

(d) apply machine learning using the one or more dynamic features extracted to generate predictions for the one or more future events and their anticipated timing.

18. The product of claim 17 further comprising a set of computer(s), wherein the set of computer(s) is operatively connected to the storage device so that the set of computer(s) can perform the operations respectively associated with instruction sets (a), (b), (c) and (d).

19. A method for forecasting in association with case management, comprising:

receiving and merging disparate health episode data sets into patient-specific data objects, wherein a patient-specific data object is a collection of episodes associated with the patient;

from the patient-specific data objects, processing a patient population into one or more cohorts, wherein a cohort is associated with one or more dynamic features whose values depend on one or more observation periods, each observation period defined by a rolling window;

applying one or more rolling windows to the one or more cohorts to extract one or more of the dynamic features;

applying machine learning using the one or more dynamic features extracted to generate predictions for the one or more future events and their anticipated timing;

validating the predictions; and

based on results of validating the predictions, taking an action that is one of;

adjusting a definition of a cohort, and adjusting a machine learning model.

20. The method as described in claim 19 further including blending predictions generated from two or more machine learning models prior to validation.