The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling

TL;DR

This paper introduces BEDS, a practical, surgeon-autonomy-preserving algorithm for optimizing intra-day surgical scheduling, implemented in EMRs, reducing variability, and adaptable to hospital changes.

Contribution

The paper presents BEDS, a widely applicable heuristic algorithm for surgical scheduling that is easy to implement, preserves surgeon autonomy, and adapts to hospital capacity changes.

Findings

BEDS reduced variability in surgical admissions.

BEDS is implemented in a large pediatric hospital's EMR.

BEDS is freely available as a Tableau dashboard.

Abstract

Surgical scheduling optimization is an active area of research. However, few algorithms to optimize surgical scheduling are implemented and see sustained use. An algorithm is more likely to be implemented, if it allows for surgeon autonomy, i.e., requires only limited scheduling centralization, and functions in the limited technical infrastructure of widely used electronic medical records (EMRs). In order for an algorithm to see sustained use, it must be compatible with changes to hospital capacity, patient volumes, and scheduling practices. To meet these objectives, we developed the BEDS (better elective day of surgery) algorithm, a greedy heuristic for smoothing unit-specific surgical admissions across days. We implemented BEDS in the EMR of a large pediatric academic medical center. The use of BEDS was associated with a reduction in the variability in the number of admissions. BEDS is freely available as a dashboard in Tableau, a commercial software used by numerous hospitals. BEDS is readily implementable with the limited tools available to most hospitals, does not require reductions to surgeon autonomy or centralized scheduling, and is compatible with changes to hospital capacity or patient volumes. We present a general algorithmic framework from which BEDS is derived based on a particular choice of objectives and constraints. We argue that algorithms generated by this framework retain many of the desirable characteristics of BEDS while being compatible with a wide range of objectives and constraints.