The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 18, 2022

Filed:

Dec. 11, 2020
Applicant:

Flatiron Health, Inc., New York, NY (US);

Inventors:

Pooja Shaw, New York, NY (US);

Abigail Orlando, New York, NY (US);

Alexander Rich, New York, NY (US);

Rebecca Miksad, Newton, MA (US);

Dominique Connolly, Lincroft, NJ (US);

Blythe Adamson, New York, NY (US);

Jessie Tseng, New York, NY (US);

Maya Najarian, Denver, CO (US);

Shreyas Lakhtakia, New York, NY (US);

Joshua Kraut, Seattle, WA (US);

Jesse Lee, New York, NY (US);

Assignee:

FLATIRON HEALTH, INC., New York, NY (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G16H 50/30 (2018.01); G16H 10/60 (2018.01); G16H 50/70 (2018.01); G06N 20/00 (2019.01); G16H 70/00 (2018.01); G16H 10/40 (2018.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
G16H 50/30 (2018.01); G06N 20/00 (2019.01); G16H 10/40 (2018.01); G16H 10/60 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G16H 70/00 (2018.01);
Abstract

A model-assisted system and method for predicting health care services. In one implementation, a model-assisted system may comprise a least one processor programmed to access a database storing a medical record associated with a patient and analyze the medical record to identify a characteristic of the patient. The processor may determine a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period; compare the patient risk level to a predetermined risk threshold; and generate a report indicating a recommended intervention for the patient. The processor may further determine a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of patients; and determine, based on the calibration factor, a bias relative to a second group of patients.


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