  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.
