ABSTRACT
Data collection, analysis, and data driven action cycles have been viewed as vital components of healthcare for decades. Throughout the COVID-19 pandemic, case incidence and mortality data have consistently been used by various levels of governments and health institutions to inform pandemic strategies and service distribution. However, these responses are often inequitable, underscoring pre-existing healthcare disparities faced by marginalized populations. This has prompted governments to finally face these disparities and find ways to quickly deliver more equitable pandemic support. These rapid data informed supports proved that learning health systems (LHS) could be quickly mobilized and effectively used to develop healthcare actions that delivered healthcare interventions that matched diverse populations' needs in equitable and affordable ways. Within LHS, data are viewed as a starting point researchers can use to inform practice and subsequent research. Despite this innovative approach, the quality and depth of data collection and robust analyses varies throughout healthcare, with data lacking across the quadruple aims. Often, large data gaps pertaining to community socio-demographics, patient perceptions of healthcare quality and the social determinants of health exist. This prevents a robust understanding of the healthcare landscape, leaving marginalized populations uncounted and at the sidelines of improvement efforts. These gaps are often viewed by researchers as indication that more data is needed rather than an opportunity to critically analyze and iteratively learn from multiple sources of pre-existing data. This continued cycle of data collection and analysis leaves one to wonder if healthcare has a data problem or a learning problem. In this commentary, we discuss ways healthcare data are often used and how LHS disrupts this cycle, turning data into learning opportunities that inform healthcare practice and future research in real time. We conclude by proposing several ways to make learning from data just as important as the data itself. © The Authors.
ABSTRACT
Background: The perceived risk of coronavirus disease 2019 (COVID-19) infection for healthcare workers (HCWs) is high. Although testing has focused on symptomatic HCWs, asymptomatic testing is considered by some to be an important strategy to limit occupational spread. Evidence on the results of large asymptomatic testing strategies in healthcare is, however, limited. This study examines the uptake and positivity of COVID-19 testing in a voluntary asymptomatic testing campaign at a large Canadian hospital. Method: In addition to testing HCWs with symptoms, all asymptomatic staff were offered a COVID-19 test at Trillium Health Partners, a large Ontario hospital, from May 27 to June 15, 2020. Testing was offered in four waves, corresponding to the likelihood of exposure to COVID-19-positive patients. The mass asymptomatic testing campaign was offered when the hospital's community test positivity rate had declined to 5%. Results: Since March 16, the hospital has tested 51.3% of its 10,143-person workforce at least once. In the asymptomatic testing campaign for HCWs between May 27 and June 15, 27% of clinical and non-clinical staff received testing. No large differences were found in the proportions of clinical HCWs tested by their exposure to COVID-19-positive patients. In this campaign, 0.2% of asymptomatic HCWs tested positive. However, these individuals either had mild symptoms at testing and did not self-identify or became symptomatic after testing. Conclusions: At this large hospital with declining community prevalence, a mass asymptomatic testing campaign of HCWs found they had a very low likelihood of testing positive for COVID-19.