Digital Health Surveillance Strategies for Management of Coronavirus Disease 2019

Abstract

Objective: To describe the design, implementation, and utilization of electronic health record (EHR)–based digital health surveillance strategies used to manage the coronavirus disease 2019 (COVID-19) pandemic and to ensure delivery of high-quality clinical care, such as case identification, remote monitoring, telemedicine services, and recruitment to clinical trials at Mayo Clinic. Methods: The design and implementation work described in this report was performed at Mayo Clinic, a large multistate integrated health care system with more than 1.5 million annual patient visits that uses the Epic EHR system. Rule-based live registries were designed in the EHR system to classify patients who currently test positive for COVID-19, patients who test positive but have recovered from COVID-19, patients who are thought to have COVID-19 but do not yet meet clinical diagnostic criteria, patients who test negative for COVID-19, and patients who exceed a risk score for serious complications from COVID-19. Results: By use of registries, custom dashboards and operational reports were developed to provide a daily high-level summary for clinical practice use and up-to-date information to manage individual patients affected by COVID-19, including support of case identification, contact isolation, and other care management tasks. Conclusion: We developed and implemented a systematic approach to the use of EHR patient registries to manage the COVID-19 pandemic that proved feasible and useful in a large multistate group clinical practice. The key to harnessing the potential of digital surveillance tools to promote patient-centered care during the COVID-19 pandemic was to use the registry data, reports, and dashboards as informatics tools to inform decision-making.