ABSTRACT
Objectives: Introduce the CDS-Sandbox, a cloud-based virtual machine created to facilitate Clinical Decision Support (CDS) developers and implementers in the use of FHIR- and CQL-based open-source tools and technologies for building and testing CDS artifacts. Materials and Methods: The CDS-Sandbox includes components that enable workflows for authoring and testing CDS artifacts. Two workshops at the 2020 and 2021 AMIA Annual Symposia were conducted to demonstrate the use of the open-source CDS tools. Results: The CDS-Sandbox successfully integrated the use of open-source CDS tools. Both workshops were well attended. Participants demonstrated use and understanding of the workshop materials and provided positive feedback after the workshops. Discussion: The CDS-Sandbox and publicly available tutorial materials facilitated an understanding of the leading-edge open-source CDS infrastructure components. Conclusion: The CDS-Sandbox supports integrated use of the key CDS open-source tools that may be used to introduce CDS concepts and practice to the clinical informatics community.
ABSTRACT
Objective: To provide a method for evaluating the interoperability of Fast Healthcare Interoperability Resources (FHIR®) clients and servers supporting different FHIR resources, profiles, and versions, and to determine the feasibility of FHIR servers supporting multiple FHIR Implementation Guides (IGs). Materials and Methods: A method of analysis, the FHIR Interoperability Table (FHIT), is proposed. The FHIT involves the concept of a "catchment," the type or category of data that a profile is intended to represent. The solution first aligns sender and/or receiver profiles according to their catchments, then determines the relationship between the admittances of those profiles, and finally interprets the relationship in terms of the feasibility of data exchange. Results: The FHIT method is demonstrated by analyzing the FHIR-based exchange between the US Core IG and the International Patient Summary IG. Discussion: The last few years have witnessed a significant growth in Fast Healthcare Interoperability Resources (FHIR), resulting in several major versions of FHIR, hundreds of IGs, and thousands of FHIR profiles. Previous work and available tools have not fully addressed the problem of interoperability between clients and servers that support different FHIR resources, profiles, and versions. Conclusion: Application of the proposed methodology allows interoperability problems in FHIR networks to be identified. In some cases, new profiles that resolve those conflicts can be derived, using intersections of the original profiles. There is a need for additional tools that implement the proposed method, as well as structured methods for expressing catchments in FHIR profiles.
ABSTRACT
OBJECTIVE: Our objective is to create a source of synthetic electronic health records that is readily available; suited to industrial, innovation, research, and educational uses; and free of legal, privacy, security, and intellectual property restrictions. MATERIALS AND METHODS: We developed Synthea, an open-source software package that simulates the lifespans of synthetic patients, modeling the 10 most frequent reasons for primary care encounters and the 10 chronic conditions with the highest morbidity in the United States. RESULTS: Synthea adheres to a previously developed conceptual framework, scales via open-source deployment on the Internet, and may be extended with additional disease and treatment modules developed by its user community. One million synthetic patient records are now freely available online, encoded in standard formats (eg, Health Level-7 [HL7] Fast Healthcare Interoperability Resources [FHIR] and Consolidated-Clinical Document Architecture), and accessible through an HL7 FHIR application program interface. DISCUSSION: Health care lags other industries in information technology, data exchange, and interoperability. The lack of freely distributable health records has long hindered innovation in health care. Approaches and tools are available to inexpensively generate synthetic health records at scale without accidental disclosure risk, lowering current barriers to entry for promising early-stage developments. By engaging a growing community of users, the synthetic data generated will become increasingly comprehensive, detailed, and realistic over time. CONCLUSION: Synthetic patients can be simulated with models of disease progression and corresponding standards of care to produce risk-free realistic synthetic health care records at scale.
