Using a Learning Health System Framework to Examine COVID-19 Pandemic Planning and Response [Preprint]

Abstract

Background: The COVID-19 pandemic has presented a unique opportunity to explore how health systems adapt under rapid and constant change and develop a better understanding of health system transformation. Learning health systems (LHS) have been proposed as an ideal structure to inform a data-driven response to a public health emergency like COVID-19. The aim of this study was to use a LHS lens to identify assets and gaps in health system pandemic planning and response during wave one of the COVID-19 pandemic.

Methods: An integrated knowledge translation approach guided this concurrent triangulation mixed methods study. We examined relevant organizational documents and system performance data generated between January 1st, 2020 and August 31st, 2020 using directed content analysis and descriptive statistics. Additionally, we conducted qualitative semi-structured interviews with health care providers, patients and families, leadership and management teams, and health centre support staff. Lastly, we used a triangulation matrix to compare and contrast summaries of all quantitative and qualitative data and identify health-system receptors and research-system supports relevant to the seven characteristics of the LHS.

Results: We identified six key priorities relevant to the pandemic response during wave one, including access to health care, personal protective equipment, visitor restrictions, pandemic assessment centre (PAC), working from home, and food services. We identified several health system assets within the LHS characteristics, including appropriate decision supports and aligned governance. Opportunities for improvement were identified in the LHS characteristics of engaged patients and timely production and use of research evidence.

Conclusion: The LHS provided a useful framework to examine COVID-19 pandemic response measures. We highlighted opportunities to strengthen the LHS infrastructure for rapid integration of evidence and patient experience data into practice and policy for future pandemic planning and response.