# US 2021/0241856 A1 · Systems and methods for determining a genomic testing status

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- Number: US 2021/0241856 A1
- Document ID: US20210241856A1
- Status: Published application
- Kind: A1
- Application number: 17/239,058
- Filed: 2021-04-23
- Published: 2021-08-05
- Earliest claimed: 2019-10-21
- Assignees: Flatiron Health, Inc.
- Applicants: Flatiron Health, Inc.
- Inventors: Addison Shelley; Alexander Padmos; Angel Leung; Chun-Che Wang; Dominic Green; Edward Liu; Janet Donegan; Lauren Sutton; Lucy He; Sharang Phadke
- Web page: https://patentgrep.com/patent/US20210241856A1

## CPC

- G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16B 45/00: ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
- G16B 50/00: ICT programming tools or database systems specially adapted for bioinformatics
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 10/20: for electronic clinical trials or questionnaires
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A computer-implemented system for determining a genomic testing status of a patient may include at least one processor programmed receive, from a source, unstructured information from a plurality of patient records associated with a patient; determine, using a first machine learning model, a primary patient record from among the plurality of patient records, wherein at least a portion of information represented in the primary patient record correlates to genomic testing; determine, using a second machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; and display a user interface comprising an indicator of the genomic testing status of the patient and a link to the primary patient record.

## Claims

### Claim 1

A system for determining a genomic testing status of a patient, the system comprising:
at least one processor programmed to:
receive, from a source, unstructured information from a plurality of patient records associated with a patient;
determine, using a first machine learning model, a primary patient record from among the plurality of patient records, wherein at least a portion of information represented in the primary patient record correlates to genomic testing;
determine, using a second machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient;
determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; and
display a user interface comprising an indicator of the genomic testing status of the patient and a link to the primary patient record.

### Claim 2 (depends on 1)

The system of claim 1, wherein the at least one processor is further programmed to determine the primary patient record based on a comparison of the correlation to a threshold.

### Claim 3 (depends on 2)

The system of claim 2, wherein the at least one processor is further programmed to receive the threshold from an entity associated with a client device configured to display the user interface.

### Claim 4 (depends on 1)

The system of claim 1, wherein the at least one processor is further programmed to:
categorize the plurality of patient records into record categories;
determine a priority order of the plurality of patient records based on the record categories; and
determine the likelihood of the occurrence of genomic testing of the patient according to the priority order of the plurality of patient records.

### Claim 5 (depends on 4)

The system of claim 4, wherein determining the priority order includes ordering the plurality of patient records based on dates associated with the plurality of patient records.

### Claim 6 (depends on 4)

The system of claim 4, wherein the at least one processor is further programmed to:
determine that the primary patient record is from a particular record category; and
determine the primary patient record, using the first machine learning model, based on the determination that the primary patient record is from a particular record category.

### Claim 7 (depends on 1)

The system of claim 1, wherein the user interface further includes an indicator of a biomarker associated with the patient.

### Claim 8 (depends on 7)

The system of claim 7, wherein the at least one processor is further programmed to determine the biomarker based on a user input received via the user interface.

### Claim 9 (depends on 7)

The system of claim 7, wherein the at least one processor is further programmed to determine the biomarker using a third machine learning model.

### Claim 10 (depends on 1)

The system of claim 1, wherein the at least one processor is further programmed to:
associate the indicator of the genomic testing status of the patient with a patient identifier of the patient;
store the association among a plurality of other associations in a database; and
retrieve, in response to a search request received from a remote device, the indicator of the genomic testing status of the patient and the patient identifier, wherein the search request comprises a parameter associated with the genomic testing status of the patient.

### Claim 11 (depends on 1)

The system of claim 1, wherein the at least one processor is further programmed to:
access testing criteria for the patient; and
based on the testing criteria and the genomic testing status of the patient, transmit a notification to a remote device.

### Claim 12 (depends on 11)

The system of claim 11, wherein the notification is transmitted to an application at the remote device configured to display the user interface and the primary patient record.

### Claim 13 (depends on 1)

The system of claim 1, wherein the at least one processor is further programmed to determine the likelihood of the occurrence of genomic testing of the patient periodically.

### Claim 14 (depends on 13)

The system of claim 13, wherein the occurrence of genomic testing of the patient is determined periodically using at least one of a time period measured in hours, a time period measured in days, or a time period determined based on a frequency of receiving new patient records.

### Claim 15 (depends on 13)

The system of claim 13, wherein the at least one processor is further programmed to update the likelihood of the occurrence of genomic testing of the patient after receiving additional patient records.

### Claim 16 (depends on 1)

The system of claim 1, wherein the at least one processor is further programmed to receive an input comprising a verified genomic testing status of the patient.

### Claim 17 (depends on 16)

The system of claim 16, wherein the at least one processor is further programmed to transmit the verified genomic testing status to a remote device.

### Claim 18 (depends on 16)

The system of claim 16, wherein the at least one processor is further programmed to change at least one parameter of the first machine learning model based on the verified genomic testing status of the patient.

### Claim 19 (depends on 1)

The system of claim 1, wherein the plurality of patient records include at least one electronic record generated by scanning a physical document with a scanning device.

### Claim 20

A method for determining a genomic testing status of a patient, comprising:
receiving, from a source, a plurality of patient records associated with a patient;
determining, using a first machine learning model, a primary patient record from among the plurality of patient records, wherein at least a portion of information represented in the primary patient record correlates to genomic testing;
determining, using a second machine learning model and based on at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient;
determining a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; and
displaying a user interface comprising an indicator of the genomic testing status of the patient and a link to the primary patient record.

### Claim 21

(canceled)