US 12,451,237 B2
Integrated patient acquisition for virtual care
Serrah Lynn Linares, Gig Harbor, WA (US); and Gautam Shah, San Francisco, CA (US)
Assigned to Change Healthcare Holdings, LLC, Nashville, TN (US)
Filed by Change Healthcare Holdings, LLC, Nashville, TN (US)
Filed on Jun. 22, 2021, as Appl. No. 17/354,309.
Prior Publication US 2022/0406441 A1, Dec. 22, 2022
Int. Cl. G16H 40/20 (2018.01); G06N 20/00 (2019.01); G16H 10/60 (2018.01); G16H 40/67 (2018.01); G06Q 10/1093 (2023.01); G16H 10/20 (2018.01)
CPC G16H 40/20 (2018.01) [G06N 20/00 (2019.01); G16H 10/60 (2018.01); G16H 40/67 (2018.01); G06Q 10/1095 (2013.01); G16H 10/20 (2018.01)] 17 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
parsing, by one or more processors and via a chatbot front-end of a virtual triage system that interfaces with a user, a natural language input using a first machine learning model that is trained with transcripts from historical interactions between agents and users and with medical knowledge bases to derive a parsed natural language input;
determining, by the one or more processors and via a chatbot back-end of the virtual triage system that (1) is communicatively coupled to the chatbot front-end and (2) receives the parsed natural language input, that the user is authenticated for access to the virtual triage system based on the parsed natural language input and at least one of an Internet Protocol (IP) address, a phone number, or a media access control (MAC) address associated with a computing device used by the user to access the virtual triage system;
accessing, by the one or more processors and via the chatbot back-end and in response to determining that the user is authenticated, historical health data associated with the user, wherein the historical health data includes a medical history of the user and identifiers of medications taken by the user;
generating, by the one or more processors and via the chatbot back-end based on inputting the parsed natural language input and the historical health data into a second machine learning model that is trained with the medical knowledge bases, a recommendation for the user;
controlling, by the one or more processors and via the chatbot back-end, a type of additional information to be collected from the user by the chatbot front-end based on a type of the recommendation; and
providing, by the one or more processors and based on the recommendation, a programmatic entry directly to a scheduling service of a healthcare provider,
wherein the first machine learning model and the second machine learning model are trained by a common service of the virtual triage system.