ABSTRACT
Background. Uncertainty and continuously changing guidance for COVID-19 response has taken a toll on the mental health of all individuals. Growing uncertainty within a healthcare system can create stress and confusion, ultimately affecting patient care. We designed an evidence-based, data-driven, decision matrix for healthcare operations that adapts COVID-19 mitigation measures in a predictable fashion according to the CDC community risk level and wastewater positivity data. Methods. A multidisciplinary team reviewed mitigation measures, published and internal data on the value and yield of these measures, and different operational aspects of a major healthcare system that are affected by COVID-19. The team also reviewed publicly available community risk metrics to then a create a decision matrix that adjusts mitigation measures and operational aspects in a predictable fashion according to COVID-19 risk in the community. Results. When COVID-19 rates were low during the pandemic the yield of temperature and symptom screening was 0.19% people screening out and the yield of preprocedure testing was 0.17% patients testing positive. Taking that into account, as well as published literature and public health guidance, we built the COVID-19 decision matrix shown in the figure, with adaptations for in-patient, out-patient, long-term care sites. The decision matrix was then posted online with a qualification that the enterprise COVID-19 status would only change with sustained changes ( > 2 weeks) in CDC Community risk level or wastewater positivity. The enterprise COVID-19 status is inserted in all enterprise communications for operational awareness. The response to the COVID-19 decision matrix has been overwhelmingly positive by staff and visitors, with no safety or operational problems in the first month of implementation. Conclusion. An evidence-based, data-driven, decision matrix provides predictable mitigation measures and operational responses to changes in the COVID-19 community risk levels and alleviates workforce uncertainty.