Enhancing narrative clinical guidance with computer-readable artifacts: Authoring FHIR implementation guides based on WHO recommendations.

INTRODUCTION: Narrative clinical guidelines often contain assumptions, knowledge gaps, and ambiguities that make translation into an electronic computable format difficult. This can lead to divergence in electronic implementations, reducing the usefulness of collected data outside of that implementation setting. This work set out to evolve guidelines-based data dictionaries by mapping to HL7 Fast Health Interoperability Resources (FHIR) and semantic terminology, thus progressing toward machine-readable guidelines that define the minimum data set required to support family planning and sexually transmitted infections. MATERIAL AND METHODS: The data dictionaries were first structured to facilitate mapping to FHIR and semantic terminologies, including ICD-10, SNOMED-CT, LOINC, and RxNorm. FHIR resources and codes were assigned to data dictionary terms. The data dictionary and mappings were used as inputs for a newly developed tool to generate FHIR implementation guides. RESULTS: Implementation guides for core data requirements for family planning and sexually transmitted infections were created. These implementation guides display data dictionary content as FHIR resources and semantic terminology codes. Challenges included the use of a two-dimensional spreadsheet to facilitate mapping, the need to create FHIR profiles and resource extensions, and applying FHIR to a data dictionary that was created with a user interface in mind. CONCLUSIONS: Authoring FHIR implementation guides is a complex and evolving practice, and there are limited examples for this groundbreaking work. Moving toward machine-readable guidelines by mapping to FHIR and semantic terminologies requires a thorough understanding of the context and use of terminology, an applied information model, and other strategies for optimizing the creation and long-term management of implementation guides. Next steps for this work include validation and, eventually, real-world application. The process for creating the data dictionary and for generating implementation guides should also be improved to prepare for this expanding work. FUNDING: This work was supported by the World Health Organization, which also worked as a collaborative partner throughout the study.

OpenMRS as an emergency EMR-How we used a global good to create an emergency EMR in a week.

BACKGROUND: As the coronavirus pandemic progressed through the United States, Indianapolis Emergency Medical Services (IEMS) identified a gap between the health system capacity and the projected need to support an overwhelmed health care system. In addressing emergencies or special cases, each medical institution in a metropolitan area typically has a siloed process for capturing emergency patient records. These approaches vary in technical capabilities and may include use of an electronic medical record system (EMR) or a hybrid paper/EMR process. Given the projected volume of patients for the COVID-19 pandemic and the proposed multi-institutional team approach needed in case of significant provider illness, IEMS sought a simple, efficient, consolidated EMR solution to support planning for the potential capacity gap. IEMS approached Regenstrief Institute (RI), an established partner with experience in supporting OpenMRS, a global good EMR platform that had been deployed in multiple settings globally. OBJECTIVE: The purpose of this project was to determine if OpenMRS, a global good, could be used to quickly stand up a system that would meet the needs for health emergency data collection and reporting. DESIGN AND IMPLEMENTATION METHODS: The team used an "all hands on deck" approach, bringing together technical and subject matter experts, and a human-centered and iterative process to ensure the system met the key needs of IEMS. The OpenMRS Reference Application was adapted to the specific need and deployed as Docker containers to servers within the Indiana Health Information Exchange. PROJECT OUTCOMES AND LESSONS LEARNED: In less than two weeks, the Regenstrief team was able to install, configure and set up a working version of OpenMRS to support the desired electronic record requirements for the IEMS disaster field clinics. Using a human-centered approach, the RI team developed, tested, and released a user-friendly, installation-ready solution complete with an end user manual and a base support plan. IEMS and RI are sharing this approach to demonstrate how a global good can quickly generate a solution for COVID-19 and other disaster responses. CONCLUSIONS: Open source global goods can rapidly be adapted to meet local needs in an emergency. OpenMRS can be adapted to meet the needs of basic emergency medical services registration, triage, and basic data collection.

Giant cell tumor of the thoracic spine in a 30-year-old woman.

Giant cell tumor (GCT) of the spine is a rare, benign tumor. Patients typically present with pain and also may experience neurologic deficits from spinal cord and/or nerve root compression. This article describes a patient who presented with acute mid-back pain, was diagnosed with spinal GCT through biopsy, and was treated successfully with surgical resection and instrumentation.