Dashboarding to Monitor Machine Learning-based Clinical Decision Support Interventions

Abstract Background Existing monitoring of ML-CDS is focused predominantly on the ML outputs and accuracy thereof. Improving patient care requires not only accurate algorithms but systems of care that enable the output of these algorithms to drive specific actions by care teams, necessitating expanding their monitoring. Objectives In this case report we describe the creation of a dashboard which allows the intervention development team and operational stakeholders to govern and identify potential issues which may require corrective action by bridging the monitoring gap between model outputs and patient outcomes. Methods We used an iterative development process to build a dashboard to monitor the performance of our intervention in the broader context of the care system. Results Our investigation of best practices elsewhere, iterative design, and expert consultation led us to anchor our dashboard on alluvial charts and control charts. Both the development process and the dashboard itself illuminated areas to improve the broader intervention. Conclusion We propose that monitoring ML-CDS algorithms with regular dashboards that allow both a context-level view of the system and a drilled down view of specific components is critical part of implementing these algorithms to ensure that these tools function appropriately within the broader care system.