A Semantic Search Engine for Helping Patients Find Doctors and Locations in a Large Healthcare Organization

Mayank Kejriwal, Hamid Haidarian, Min-Hsueh Chiu, Andy Xiang, Deep Shrestha, Faizan Javed

Abstract: Efficiently finding doctors and locations (FDL) is an important search problem for patients in the healthcare domain, for which traditional information retrieval (IR) methods tend to be sub-optimal. This paper introduces and defines FDL as an important healthcare industry-specific problem in IR. We then propose a semantic search engine as a robust solution to FDL in Kaiser Permanente (KP), a large healthcare organization with 12 million members. Our solution meets practical needs of data security and privacy, scalability, cost-effectiveness, backward compatibility with existing indexes and search infrastructure, and interpretability of outputs for patients. It uses a concept-rich ontology to model raw data from multiple sources as entities, relations, and attributes in a knowledge graph that is stored and indexed in an industry-scale graph database. We evaluate the solution on a real patient-query log and demonstrate its practical utility. The system has been implemented and deployed live to KP customers.