# US 12,008,613 B2 · Method of optimizing patient-related outcomes

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- Number: US 12,008,613 B2
- Document ID: US12008613B2
- Status: Granted
- Kind: B2
- Application number: 18/344,704
- Filed: 2023-06-29
- Published: 2023-11-23
- Granted: 2024-06-11
- Earliest claimed: 2010-09-01
- Assignees: Apixio, Inc.
- Applicants: Apixio, LLC
- Inventors: Shahram Shawn Dastmalchi; Vishnuvyas Sethumadhavan; Mary Ellen Campana; Robert Derward Rogers; Imran N. Chaudhri
- Web page: https://patentgrep.com/patent/US12008613B2

## CPC

- G06Q 10/10: Office automation; Time management
- G06Q 30/0283: Price estimation or determination
- G06Q 40/08: Insurance
- G16H 10/20: for electronic clinical trials or questionnaires
- G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof
- G16H 20/00: ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G16H 40/67: for remote operation
- G16H 50/50: for simulation or modelling of medical disorders
- G16H 70/00: ICT specially adapted for the handling or processing of medical references
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A medical information navigation engine (“MINE”) is provided. In some embodiments, the system computes a current patient encounter vector for a current patient encounter, and then an optimal patient encounter vector is computed by assuming a best case patient encounter in accordance with the organizational objectives. The system is then able to compute the difference between the best case encounter and the current patient encounter. This difference is used to compute a corresponding payoff using an intelligent matrix.

## Claims

### Claim 1

A Medical Information Navigation Engine (“MINE”) including at least one hardware processor configured to:
convert medical information formatted in various formats into a format to facilitate search speed for data queried from the medical information, the medical information associated with a plurality of patients;
generate, using the converted medical information, a plurality of patient state timelines, wherein a subset of the plurality of patient state timelines includes at least one state of interest;
generate a plurality of impact measures for each of the plurality of patient state timelines, wherein each impact measure is a cost of services provided at a given time to transition from one state to another;
generate a probability distribution of future impacts by summing all impact measures after the at least one state of interest occurs for each of the subset of the plurality of patient state timelines;
generate a suggestion model by analyzing the probability distribution of future impacts; and
apply the suggestion model to one patient state timeline to generate recommendations for the corresponding patient.

### Claim 2 (depends on 1)

The MINE of claim 1, wherein each patient state timeline corresponds to a particular patient of the plurality of patients.

### Claim 3 (depends on 1)

The MINE of claim 1, wherein each patient state timeline is an ordering of individual states in a time order.

### Claim 4 (depends on 1)

The MINE of claim 1, wherein the least one hardware processor is further configured to select, based on analyzing the probability distribution of future impacts by the at least one hardware processor, actions that change a likelihood of a future outcome that maximizes at least one organizational objective.

### Claim 5 (depends on 1)

The MINE of claim 1, wherein the least one hardware processor is further configured to receive, from a plurality of medical data sources, the medical information for the plurality of patients.

### Claim 6 (depends on 1)

The MINE of claim 1, wherein the least one hardware processor is further configured to apply the suggestion model to each of the subset of the plurality of patient state timelines to generate a set of actions for a cohort.

### Claim 7

A computer-implemented method implemented by a Medical Information Navigation Engine (“MINE”) including at least one processor, the method comprising:
converting, by the at least one processor, medical information formatted in various formats into a format to facilitate search speed for data queried from the medical information, the medical information associated with a plurality of patients;
generating, by the at least one processor and using the converted medical information, a plurality of patient state timelines, wherein a subset of the plurality of patient state timelines includes at least one state of interest;
generating, by the at least one processor, a plurality of impact measures for each of the plurality of patient state timelines, wherein each impact measure is a cost of services provided at a (liven time to transition from one state to another;
generating, by the at least one processor, a probability distribution of future impacts by summing all impact measures after the at least one state of interest occurs for each of the subset of the plurality of patient state timelines;
generating, by the at least one processor, a suggestion model by analyzing the probability distribution of future impacts; and
applying, by the at least one processor, the suggestion model to one patient state timeline to generate recommendations for the corresponding patient.

### Claim 8 (depends on 7)

The computer-implemented method of claim 7, wherein each patient state timeline corresponds to a particular patient of the plurality of patients.

### Claim 9 (depends on 7)

The computer-implemented method of claim 7, wherein each patient state timeline is an ordering of individual states in a time order.

### Claim 10 (depends on 7)

The computer-implemented method of claim 7 further comprising selecting, based on analyzing the probability distribution of future impacts by the at least one processor, actions that change a likelihood of a future outcome that maximizes at least one organizational objective.

### Claim 11 (depends on 7)

The computer-implemented method of claim 7 further comprising receiving, by the at least one processor from a plurality of medical data sources, the medical information for the plurality of patients.

### Claim 12 (depends on 7)

The computer-implemented method of claim 7 further comprising applying, by the at least one processor, the suggestion model to each of the subset of the plurality of patient state timelines to generate a set of actions for a cohort.

### Claim 13

At least one non-transitory computer-readable medium including computer-executable instructions that when executed by at least one processor of a Medical Information Navigation Engine (“MINE”) cause the at least one processor to:
convert medical information formatted in various formats into a format to facilitate search speed for data queried from the medical information, the medical information associated with a plurality of patients;
generate, using the converted medical information, a plurality of patient state timelines, wherein a subset of the plurality of patient state timelines includes at least one state of interest;
generate a plurality of impact measures for each of the plurality of patient state timelines, wherein each impact measure is a cost of services provided at a (liven time to transition from one state to another;
generate a probability distribution of future impacts by summing all impact measures after the at least one state of interest occurs for each of the subset of the plurality of patient state timelines;
generate a suggestion model by analyzing the probability distribution of future impacts; and
apply the suggestion model to one patient state timeline to generate recommendations for the corresponding patient.

### Claim 14 (depends on 13)

The at least one non-transitory computer-readable medium of claim 13, wherein each patient state timeline corresponds to a particular patient of the plurality of patients.

### Claim 15 (depends on 13)

The at least one non-transitory computer-readable medium of claim 13, wherein each patient state timeline is an ordering of individual states in a time order.

### Claim 16 (depends on 13)

The at least one non-transitory computer-readable medium of claim 13, wherein the computer-executable instructions further cause the at least one processor to select, based on analyzing the probability distribution of future impacts by the at least one processor, actions that change a likelihood of a future outcome that maximizes at least one organizational objective.

### Claim 17 (depends on 13)

The at least one non-transitory computer-readable medium of claim 13, wherein the computer-executable instructions further cause the at least one processor to receive, from a plurality of medical data sources, the medical information for the plurality of patients.