Granted Patent B2

US 12,620,485 · App. 17/978,677 · Granted May 5, 2026

Inventors: Yongjia Song (Louisville, KY); Peyman Yousefian (Frederick, MD); Rajiv Kumar Gumpina (Louisville, KY); Sravya Etlapur (Rochester, MN); Nataley Savanah Kennedy (Lexington, MA); Brandi Sambola (Gonzales, LA); Rohan Vohra (Louisville, KY)

Assignee: Humana Inc.

G16H50/20

Abstract

Claims (25)

1 . A system for predicting and recommending a particular healthcare facility or provider for those members or patients needing a particular medical procedure or other health intervention, the system comprising:

a database for storing historical claims data;

a computer processor;

a graphical user interface comprised of a first region for entering a zip code, and a second region for listing recommended healthcare facilities or providers for performing the particular medical procedure or other health intervention based on the entered zip code;

a non-transitory computer-readable medium storing instructions that when executed by the computer processor cause the computing device to perform the steps of:

a. extracting a heterogeneous graph from the historical claims data, the graph comprised of at least two types of nodes selected from the group comprising a member node, a healthcare facility node, and a provider node, the heterogeneous graph further comprising a plurality of edges each representing a positive class, and a plurality of edges each representing a negative class, wherein each edge representing a positive class connects one member node to one healthcare facility node or provider node that the one member has a previous connection or visit with and wherein each edge representing a negative class connects one member node to one healthcare facility node or provider node that the one member does not have a previous connection or visit with;

b. embedding the heterogeneous graph to generate a member node vector for each member node and a healthcare facility node vector for each healthcare facility node or a provider node vector for each provider node and sampling a neighborhood for each member node and healthcare facility node or provider node and aggregating node feature information from the sampled neighborhood to update each member node vector and each healthcare facility node vector or provider node vector, concatenating a plurality of vectors, each concatenated vector comprised of a first member node vector and at least one healthcare facility node vector or at least one provider node vector, wherein each of the plurality of concatenated vectors represents either a member-healthcare facility pair or member-provider pair;

c. applying a deep learning model to the plurality of concatenated vectors to determine the probability that each member-healthcare facility pair or member-provider pair will be selected for the particular medical procedure or other health intervention, the deep learning model having been trained using labeled member-healthcare-facility pairs or member-provider pairs derived from historical claims data, assigning a positive label where a member previously visited the facility or provider and a negative label where the member did not, and updating model weights by minimizing a loss function; and

populating the second region with the recommended healthcare facilities or providers with the highest probabilities for performing the particular medical procedure or other health intervention based on an entered zip code for the patient or member.

2 . The system according to claim 1 , wherein the heterogeneous graph is further comprised of:

member node features including age, gender and medical conditions; and

healthcare facility node features including facility performance and quality.

3 . The system according to claim 1 , further comprising:

a first model used to score the likelihood that each member would receive the particular medical procedure or other health intervention at the particular type of healthcare facility or with one of the providers before determining the probability that each member-healthcare facility pair or member-provider pair will be selected for the particular medical procedure or other health intervention.

4 . The system according to claim 1 , wherein the non-transitory computer-readable medium stores instructions that when executed by the computer processor cause the computing device to perform, prior to extracting the graph, a step of generating the plurality of edges where each edge represents a negative class, this step further comprising the steps of:

assigning all available healthcare facilities or providers to each member;

determining a distance between each member location and each healthcare facility location or provider location;

grouping each distance between each member location and each healthcare facility location or provider location into a predetermined number of categories based on the determined distance;

matching each edge representing a positive class with one randomly selected edge representing a negative class from each of the categories; and

preparing the graph.

5 . The system according to claim 1 , wherein the particular medical procedure needed is Esophagogastroduodenoscopy (EGD) and the particular healthcare facility is an Ambulatory Surgery Center (ASC).

6 . The system according to claim 1 , wherein a weight matrix is applied to the plurality of concatenated vectors to produce final probability scores.

7 . The system according to claim 1 , wherein the heterogeneous graph is comprised of a training set comprised of a predetermined number of randomly selected member nodes and a testing set comprised of the rest of the member nodes that were not randomly selected for the training set.

8 . The system according to claim 1 , wherein the graphical user interface is further comprised of a third region for entering in member identification information and a fourth region for entering in a minimum threshold distance that the member-healthcare facility pair or member-provider must fall below.

9 . The system according to claim 1 , wherein the non-transitory computer-readable medium stores instructions that when executed by the computer processor cause the computing device to generate a neighborhood embedding and concatenate the neighborhood embedding with a respective node vector to update each member node vector and each healthcare facility node vector or provider node vector.

Assignments (1)

Continuity (1)

References Cited (10)