Dashboarding to Monitor Machine-Learning-Based Clinical Decision Support Interventions.

ABSTRACT:

Background

 Existing monitoring of machine-learning-based clinical decision support (ML-CDS) is focused predominantly on the ML outputs and accuracy thereof. Improving patient care requires not only accurate algorithms but also 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 that allows the intervention development team and operational stakeholders to govern and identify potential issues that 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 inter

SUBMITTER: Hekman DJ 

PROVIDER: S-EPMC10901643 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature