Alexander Rich Phones & Addresses
-
New Gloucester, ME
-
Harpswell, ME
-
64 N Shore Dr, Owls Head, ME 04854 (207) 420-0648
-
Chicago, IL
-
Madison, NJ
-
Gorham, ME
-
Falmouth, ME
Work
Company:
Kumar moolayil md
Address:
6450 N Hamlin Ave, Chicago, IL 60712
Phones:
(847) 673-0556
Position:
Psychologist
Industries:
Offices and Clinics of Health Practitioners
Business Records
Name / Title
Company / Classification
Phones & Addresses
Psychologist
Kumar Moolayil Md
Offices and Clinics of Health Practitioners
Offices and Clinics of Health Practitioners
6450 N Hamlin Ave, Chicago, IL 60712
Psychologist
Kumar Moolayil Md
Offices and Clinics of Health Practitioners
Offices and Clinics of Health Practitioners
6450 N Hamlin Ave, Chicago, IL 60712
Publications
Us Patents
Clinical Risk Model
View pageUS Patent:
20230097114, Mar 30, 2023
Filed:
Oct 5, 2022
Appl. No.:
17/960406
Inventors:
- 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
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
International Classification:
G16H 50/30
G16H 70/00
G06N 20/00
G16H 50/20
G16H 10/40
G16H 50/70
G16H 10/60
G16H 70/00
G06N 20/00
G16H 50/20
G16H 10/40
G16H 50/70
G16H 10/60
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.
Clinical Risk Model
View pageUS Patent:
20210296000, Sep 23, 2021
Filed:
Dec 11, 2020
Appl. No.:
17/119390
Inventors:
- 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
Shreyes Lakhtakia - New York NY, US
Joshua Kraut - Seattle WA, US
Jesse Lee - 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
Shreyes Lakhtakia - New York NY, US
Joshua Kraut - Seattle WA, US
Jesse Lee - New York NY, US
Assignee:
Flatiron Health, Inc. - New York NY
International Classification:
G16H 50/30
G16H 10/60
G16H 10/40
G16H 50/20
G16H 70/00
G06N 20/00
G16H 10/60
G16H 10/40
G16H 50/20
G16H 70/00
G06N 20/00
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.