The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling (2203.08146v1)

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.

• The paper introduces the greedy BEDS algorithm that integrates with existing electronic medical records and preserves surgeon autonomy.
• It employs routinely collected data to minimize the variability in daily elective surgical admissions.
• Discrete event simulation validation demonstrated a significant reduction in admission variance, enhancing operational efficiency.

The paper, "The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling," presents an algorithm–termed the BEDS (Better Elective Day of Surgery) algorithm–aimed at optimizing surgical scheduling to smooth the variability in daily surgical admissions. BEDS is a greedy heuristic designed to aid hospitals in scheduling elective surgeries while retaining surgeon autonomy and leveraging pre-existing electronic medical record (EMR) infrastructure.

Key Objectives and Features of BEDS:

1. Compatibility with Existing Workflows: The algorithm integrates seamlessly into conventional surgical scheduling processes, requiring minimal changes in standard operations.
2. Implementation in Constrained Environments: BEDS is implementable with limited computational resources and operates within the functionality of commonly used EMRs like EPIC and Cerner.
3. Autonomy Preservation: It is designed to work without necessitating reductions in surgeon autonomy or centralized scheduling, allowing flexibility in surgeon decisions and preferences.
4. Adoption and Flexibility: The algorithm is robust to fluctuations in demand, changes in hospital capacity, and overhauls in operations.
5. Availability: BEDS is freely accessible for use through Tableau, emphasizing its ready deployability in various hospital settings.

Methodology and Implementation:

• BEDS uses routinely collected data such as procedure duration, surgeon availability, patient availability, and post-operative unit capacity. The goal is to minimize daily variance in hospital admissions by using a simple heuristic that schedules surgeries on days predicted to have lower bed occupancy.
• The model retains adaptability, allowing for modifications in the recommendation logic to fit specific institutional needs by adjusting the objective function based on local objectives and constraints.

Experimental Validation:

• Prior to full adoption, the BEDS algorithm was validated using a discrete event simulation model. Historical data from the Lucile Packard Stanford Children's Hospital was used, demonstrating a significant reduction in variability of daily surgical admissions post-implementation.

Initial Results:

• The implementation of BEDS in a Stanford children's hospital led to measurable improvements, with a significant reduction in the variability of elective surgical admissions compared to corresponding historical periods. For example, the coefficient of variation of daily elective admissions in post-operative units saw reductions, making post-surgical resource planning more predictable and efficient.

Conclusion:

The paper concludes that BEDS offers a pragmatic solution to scheduling challenges faced by hospitals by reducing variability in surgical admissions and aligning with real-world clinician practices and patient needs. This approach can potentially serve as a benchmark for more complex algorithmic solutions in operating room and bed demand management while maintaining operational simplicity and practitioner autonomy. BEDS is ready for deployment and offers a straightforward implementation pathway for institutions aiming to enhance their surgical scheduling efficiencies.