Abstract

Background: Rapid treatment is critical to improve survival from out-of-hospital cardiac arrest (OHCA) and is directly affected by bystanders’ abilities to immediately recognize cardiac arrest, call 911, and provide early CPR and/or Automated External Defibrillation (AED). Given the critical element of early bystander interventions, we chose to use a learning health system (LHS) approach to reduce time-to-first-treatment for OHCA. In a LHS, a community of diverse stakeholders embark on continuous, data-driven learning to improve best practices and health outcomes. Methods: We established an OHCA learning community (LC) comprised of multidisciplinary representatives of the chain of survival in Michigan’s Washtenaw-Livingston county medical control authority region (a two-county entity, population 555,278, that coordinates emergency medical services). The LC aimed to accelerate the shared identification of priority areas for investigation and to facilitate more rapid implementation of innovative interventions to reduce OHCA response time. Results: We held two LC meetings. Using an approach that included facilitated discussion, 65 diverse stakeholders (OHCA survivors, 911 dispatchers, first responders, physicians) identified the following 4 themes as critical to improving time-to-first-treatment: 1) Improve bystander response; 2) Increase OHCA public awareness; 3) Reduce time-to-treatment and improve data collection for key process measures (time of cardiac arrest, time to first chest compression, time to AED); and 4) Improve post-cardiac arrest systems of care. The LC was supported by examination of standardized processes and infrastructure in a learning cycle (see graphic). These LC findings are being deployed in the Washtenaw-Livingston region to enhance OHCA data collection and increase bystander engagement; e.g. by establishing a system to review 911 calls to accurately assess times and identify systematic barriers to rapid treatment. Conclusions: The LHS approach and multi-stakeholder LC has led to the identification of community-owned key priority areas within a local context for OHCA. We believe this approach will accelerate the implementation and adoption of new data collection methods and innovative practices that will facilitate continuous learning and improvement.

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