Background

The COVID-19 pandemic has had a major impact on how clinicians must schedule, triage, room, and treat a significant number of infected patients with limited medical resources and equipment. Clinicians need to make quick decisions based on objective and subjective observations to determine if a patient has signs and symptoms compatible with COVID-19 and whether the patient should be tested. Clinicians are encouraged to leverage technology, virtual visits, and other Health IT-generated clinical data to assist with decision making and information sharing. Unfortunately, electronic clinical data is plagued by data quality challenges, including variation in how data elements are encoded by terminology standards, and stored in clinical information models. These challenges can cause inefficiencies in how clinical data elements are identified, retrieved, analyzed, and operationalized into workflows at the point-of-care.  

In the current COVID-19 world, clinicians are encouraged to rely on electronic clinical data to determine which patients should be prioritized for being tested, hospitalized, and/or isolated. Failure to include or exclude patients in COVID-19 screening, testing, and treatment could lead to life-threatening situations for patients and could impact the overall trajectory of the outbreak at a population-level.

 Overall, the ability to measure and improve healthcare outcomes relies on consistent, high-quality electronic data that is aggregated from a variety of Health IT systems across numerous medical centers / healthcare facilities. Clinicians need to be able to easily access and fully trust the electronic data they are using to make determinations at the point-of-care. This ability aligns to criteria for High Reliability Organizations and Learning Health Systems.