Data integration of health through connected services can save lives in the event of a medical emergency or to provide efficient and effective interventions for the benefit of the patients through the integration of bedside and bench side clinical research. Such integration will support all wind of change in healthcare for being predictive, pre-emptive, personalized, problem-oriented and participatory. Prototyping a healthcare system that enables data integration is a big challenge for healthcare for a long time. However, an innovative solution started to emerge by focusing on problem lists where everything can connect the problem list forming a growing graph. This notion has been introduced by Dr. Lawrence Weed in early 70’s but the enabling technologies weren’t mature enough to provide a successful implementation prototype. In this article we are describing our efforts in prototyping Dr. Lawrence Weed problem-oriented medical record (POMR) and his patient case schema (SOAP) to shape a prototype for connected health. For this we are using the TypeGraphQL API and our enterprise-based QL4POMR to describe a Web-Based gateway for healthcare services connectivity. Our prototype has reported success in connecting to the HL7 FHIR medical record and the OpenTarget biomedical repositories.

Physicians and Clerkships follow the SOAP note in describing and presenting patient cases. The SOAP note was originated from the problem-oriented medical record (POMR) developed nearly 60 years ago by Lawrence Weed, MD. However, the POMR/SOAP is not commonly found in electronic medical records (EMR) used today due to the flexible nature of building patient cases that requires complex harmonization with variety of patient case schemas. In this article we attempted to use the GraphQL API for harmonizing patient case data communicated with different care interface and providing the query on the data. This harmonization interface is called QL4POMR as a GraphQL implementation to the POMR SOAP note. Physicians can used this interface to describe and present any patient case for the purpose of diagnosis and prognosis with a varying backend. The QL4POMR implemented a mapping module to map graphs from POMR to HL7 FHIR and vise versa.