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:
Jul. 23, 2024

Filed:

Jun. 24, 2022
Applicants:

Daniel Kogan, Brooklyn, NY (US);

Rada Sumareva, New York, NY (US);

Gennady Ukrainksy, New York, NY (US);

Inventors:

Daniel Kogan, Brooklyn, NY (US);

Rada Sumareva, New York, NY (US);

Gennady Ukrainksy, New York, NY (US);

Assignee:

ZIPHYCARE INC, New York, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 40/20 (2018.01); G01C 21/36 (2006.01); G16H 10/60 (2018.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
G16H 40/20 (2018.01); G01C 21/3617 (2013.01); G01C 21/3691 (2013.01); G01C 21/3697 (2013.01); G16H 10/60 (2018.01); G16H 50/20 (2018.01);
Abstract

A system and a computer-implemented method employ an appointment optimization and route planning system (AORPS) for optimizing home-visit appointments and related travel for delivering patient care. The AORPS receives registration and patient data from patients and client input including information about healthcare providers, onsite care coordinators, health plans, appointment types, and success rates from a client. The AORPS collates the patient data and generates an input matrix from the client input and the collated patient data. The AORPS generates a predictive model for appointments, capitation, and return on investment for delivering patient care based on appointment and patient history, feedback, and healthcare data. The AORPS generates an appointment schedule with travel routes dynamically based on optimization factors derived from the client input, the collated patient data, the input matrix, the healthcare data, and the predictive model, incorporating real-time changes in patient data, the client input, the optimization factors, and appointments.


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