UC Davis

During the Coronavirus Disease 2019 (COVID-19) pandemic, a public health emergency (PHE) was declared by the United States (U.S.) government, reducing the number of in-person clinic visits and increasing telemedicine utilization.1–12 Healthcare reimbursement guidelines evolved on an ongoing basis and a lack of standardization in procedure coding for telemedicine visits created confusion amongst providers.13–17 This thesis focuses on a standardized, multi-site data repository, the University of California (UC) COVID-19 Research Dataset (UC CORDS) and uses it as an example to review the downstream consequences of ad-hoc data mapping of new services such as telemedicine visits to formalized coding systems during the COVID-19 pandemic. The findings are then translated to recommendations for creating best practices to combat challenges associated with building computable phenotypes for complex multi-site data in emergent health scenarios. Included patients had a COVID-19 test result mapping to the designated LOINC codes between Feb 2020 to Feb 2021. My study results reflect the lack of standardization in standard vocabulary naming conventions and concept mapping for telehealth. This makes it difficult for researchers to find telehealth-specific data from CDM datasets like UC CORDS, which only capture data mapped to standard vocabularies. My journey through this master’s thesis also highlights the multiple data access, data fluency, and data management challenges that clinical researchers face with complex healthcare datasets such as UC CORDS. In conclusion, although telemedicine has been considered beneficial for several years, the COVID-19 pandemic offered the best opportunity to improve telemedicine services and fully integrate them into healthcare reimbursement workflows and healthcare information systems. Based on the outcomes of this study, there is still room for process improvement in regard to handling the needs of data capture for new services in emergency scenarios, and healthcare institutions should involve multiple key stakeholders at an earlier stage when developing and implementing a digital infrastructure.