IP Library › Granted Patent US 12,237,057
Granted Patent B1
US 12,237,057 · App. 17/498,527 · Granted Feb 25, 2025

Discovering context-specific complexity and utilization trajectories

Inventor: Douglas S. McNair (Seattle, WA)
Assignee: Cerner Innovation, Inc.
G16H10/60G16H20/10G16H50/20
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Quick Facts
Patent No.
US 12,237,057
App. No.
17/498,527
Granted
Feb 25, 2025
Kind
B1
Abstract

Systems, methods, and computer-readable media are provided for patient case and care complexity characterization, and detecting matches of an individual patient's record with collections of other patients' records, based on serial, longitudinal patterns, for facilitating efficient health services utilization, implementing programs to reduce complexity, preventive medicine, and risk management in health care. In an embodiment, time series are formed by electronically representing information pertaining to successive longitudinal episodes of health services utilization and the circumstances in which the episodes were incurred; calculating time-series K-nearest-neighbor clusters and distances for each combination; determining the cluster to which a given candidate patient complexity record is nearest, and prescribing one or more interventions specific to hazards that are characteristic of trajectories that are members of that cluster, or that are deemed to be relevant to mitigating those hazards, thereby preventing the adverse outcomes and subsequent excess utilization that are prevalent in that cluster.

Claims (43)

1. A system having one or more hardware processors configured to facilitate a plurality of operations, the operations comprising:

receiving target information associated with a target population of patients from a first set of records of an electronic health records system;

determining, via the one or more hardware processors, a set of timeseries based on the target information, wherein each timeseries comprises condition complexity information associated with a medication complexity and a care complexity;

generating one or more timeseries trajectory clusters based at least in part on the condition complexity information, wherein each timeseries trajectory cluster has an associated centroid, wherein generating the one or more timeseries trajectory clusters comprises performing, via the one or more hardware processors, a single database scan that uses a vertical identification-list database format and a sparse-matrix approach to decompose an original search lattice into a plurality of smaller sub-lattices;

processing each of the plurality of smaller sub-lattices independently in distributed memory to generate the one or more timeseries trajectory clusters;

for a first health record, determining at least a first health record timeseries cluster distance based on a centroid associated with one of the one or more timeseries trajectory clusters;

determining at least one timeseries trajectory cluster match for the first health record based on the first health record timeseries cluster distance; and

generating, via the one or more hardware processors, at least one preventative intervention, for a patient associated with the first health record, based on the at least one timeseries trajectory cluster match.

2. The system of claim 1 , comprising an entropy index component configured to determine an entropy index for each timeseries trajectory cluster.

3. The system of claim 1 , comprising a display component configured to display one or more of the timeseries trajectory clusters to a caregiver.

4. The system of claim 2 , comprising a display component configured to display the entropy index to a caregiver.

5. The system of claim 1 , comprising an estimation component configured to estimate a future healthcare services cost for the patient associated with the first health record.

6. The system of claim 1 , wherein the operations further comprise, based on the at least one timeseries trajectory cluster match, generating a care regimen for the patient associated with the first health record.

7. The system of claim 1 , wherein the operations further comprise, based on the at least one timeseries trajectory cluster match, calculating a likelihood of risk of a future acute-care episode for the patient associated with the first health record.

8. The system of claim 1 , wherein the operations further comprise, based on the at least one timeseries trajectory cluster match, predicting a utilization level of health care resources for the patient associated with the first health record.

9. One or more non-transitory computer-readable media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations, the operations comprising:

receiving target information associated with a target population of patients from a first set of records of an electronic health records system;

determining, via the one or more hardware processors, a set of timeseries based on the target information, wherein each timeseries comprises condition complexity information associated with a medication complexity and a care complexity;

generating one or more timeseries trajectory clusters based at least in part on the condition complexity information, wherein each timeseries trajectory cluster has an associated centroid, wherein generating the one or more timeseries trajectory clusters comprises performing, via the one or more hardware processors, a single database scan that uses a vertical identification-list database format and a sparse-matrix approach to decompose an original search lattice into a plurality of smaller sub-lattices;

processing each of the plurality of smaller sub-lattices independently in distributed memory to generate the one or more timeseries trajectory clusters;

for a first health record, determining at least a first health record timeseries cluster distance based on a centroid associated with one of the one or more timeseries trajectory clusters;

determining at least one timeseries trajectory cluster match for the first health record based on the first health record timeseries cluster distance; and

generating, via the one or more hardware processors, at least one preventative intervention, for a patient associated with the first health record, based on the at least one timeseries trajectory cluster match.

10. The one or more non-transitory computer-readable media of claim 9 , wherein the at least one timeseries trajectory cluster match is determined based further on a pre determined threshold implemented as a heuristic threshold to facilitate evaluating trajectory distances and entropies associated with one or more timeseries.

11. The one or more non-transitory computer-readable media of claim 9 , wherein the operations further comprise linking the first health record to the one or more timeseries trajectory clusters by ranking each of the one or more timeseries trajectory clusters based on the first health record timeseries cluster distance.

12. A method, comprising:

receiving target information associated with a target population of patients from a first set of records of an electronic health records system;

determining, via one or more hardware processors, a set of timeseries based on the target information, wherein each timeseries comprises condition complexity information associated with a medication complexity and a care complexity;

generating one or more timeseries trajectory clusters based at least in part on the condition complexity information, wherein each timeseries trajectory cluster has an associated centroid, wherein generating the one or more timeseries trajectory clusters comprises performing, via the one or more hardware processors, a single database scan that uses a vertical identification-list database format and a sparse-matrix approach to decompose an original search lattice into a plurality of smaller sub-lattices;

processing each of the plurality of smaller sub-lattices independently in distributed memory to generate the one or more timeseries trajectory clusters;

for a first health record, determining at least a first health record timeseries cluster distance based on a centroid associated with one of the one or more timeseries trajectory clusters;

determining at least one timeseries trajectory cluster match for the first health record based on the first health record timeseries cluster distance; and

generating, via the one or more hardware processors, at least one preventative intervention, for a patient associated with the first health record, based on the at least one timeseries trajectory cluster match.

13. The method of claim 12 , wherein the at least one timeseries trajectory cluster match is determined based further on a predetermined threshold implemented as a heuristic threshold to facilitate in evaluating trajectory distances and entropies associated with one or more timeseries.

14. The method of claim 12 , further comprising linking the first health record to the one or more timeseries trajectory clusters by ranking each of the one or more timeseries trajectory clusters based on the first health record timeseries cluster distance.

15. The one or more non-transitory computer-readable media of claim 9 , wherein the operations further comprise binding, via an attribute information component, one or more attributes to the target information, and wherein determining the set of timeseries is based further on the one or more attributes.

16. The one or more non-transitory computer-readable media of claim 9 , wherein the operations further comprise determining, via an entropy index component, an entropy index for each timeseries trajectory cluster.

17. The one or more non-transitory computer-readable media of claim 9 , wherein the operations further comprise determining whether the first health record timeseries cluster distance exceeds a predetermined threshold, and wherein the determining of the at least one timeseries trajectory cluster match is based further on the first health record timeseries cluster distance exceeding the predetermined threshold.

18. The one or more non-transitory computer-readable media of claim 17 , wherein the predetermined threshold is implemented as a heuristic threshold to facilitate evaluating trajectory distances and entropies associated with one or more timeseries.

19. The one or more non-transitory computer-readable media of claim 9 , wherein generating the one or more timeseries trajectory clusters comprises: applying, via the one or more hardware processors, a multi-cluster model and extracting, via the one or more hardware processors, cluster information from model results for each of one or more entities of interest.

20. The one or more non-transitory computer-readable media of claim 19 , wherein the cluster information is associated with one or more cluster assignments.

21. The one or more non-transitory computer-readable media of claim 19 , wherein the one or more entities of interest are associated with the target information.

22. The one or more non-transitory computer-readable media of claim 19 , wherein the one or more entities of interest comprise one or more attributes associated with the target information.

Assignments (2)
CORRECTIVE ASSIGNMENT TO CORRECT THE CONVEYING PARTY NAME PREVIOUSLY RECORDED AT REEL: 057764 FRAME: 0047. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Nov 11, 2021
From: MCNAIR, DOUGLAS S.
To: CERNER INNOVATION, INC.
Reel/Frame 058861/0910 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 12, 2021
From: MCNAIR, -DOUGLAS S.
To: CERNER INNOVATION, INC.
Reel/Frame 057764/0047 →
Continuity (7)
Continuation 15855720 · Dec 27, 2017
Division 14281593 · May 19, 2014
Continuation In Part 14209568 · Mar 13, 2014
Continuation In Part 14175750 · Feb 7, 2014
Provisional Application 61824377 · May 17, 2013
Provisional Application 61798123 · Mar 15, 2013
Provisional Application 61762178 · Feb 7, 2013
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