Risk-based clinical scheduling tool for congenital cardiac catheterization procedures

Abstract

Background

In centers with multiple catheterization laboratories and other complex procedural units, cases are scheduled to occur simultaneously, resulting in shared resources and compounding risk factors within the care environment. Additional complexity arises from the heterogeneous nature of procedure types, the frequency of cases and turnover, and the resource requirements necessary to care for these patients. This complexity necessitates an innovative approach to scheduling that enhances both safety and efficiency.

Methods

In collaboration, The MITRE Corporation and the cardiac catheterization laboratory at Boston Children's Hospital (BCH) developed a tool that allows decision-making about scheduling cases to be based on risk and resource utilization. The aim of this study is to leverage to validate human-interpretable scheduling heuristics that decrease system-level risk, increase system-level efficiency, and are easily integrated into existing scheduler workflows.

Results

The Points Split heuristic produced schedules with much fewer unbalanced days compared to the Baseline and Points heuristics. The median count of unbalanced days per year for the Points Split heuristic was 7, compared to 78 and 110 unbalanced days per year for the Points and Baseline heuristics, respectively.

Conclusions

This machine learning-enhanced scheduling tool effectively aligns patient risk with resource availability, thereby enhancing operational efficiency and safety in pediatric cardiac catheterization labs. The rule-based scheduling heuristic was also found to be robust to a variety of lab configurations, case arrival rates, and patient population conditions. The approach holds promise for broader application in complex medical environments where procedure scheduling impacts patient outcomes.