Machine learning-based clinical decision support (ML-CDS) tools hold enormous promise to simultaneously improve clinical care and reduce clinician burden. ML-CDSs can streamline the process of identifying the right care for the right patient and presenting that information to providers at the right time. However, many challenges with ML-CDS still exist, and these tools require monitoring during and after implementation to ensure that they are functioning correctly. This toolkit contains a rough template for a dashboard to monitor ML-CDS tools during and after implementation.
This dashboard, created by authors in the UW-Madison BerbeeWalsh Department of Emergency Medicine including HIP Investigator Dr. Brian Patterson, was developed in the context of a ML-CDS intervention in the Emergency Department (ED) to identify patients at high risk for an outpatient fall and prompt providers to refer qualifying patients to a Mobility and Falls Clinic in the health system to ameliorate the patients’ fall risk.View the toolkit