Reducing Surgical Ward Congestion Through Improved Surgical Scheduling and Uncapacitated Simulation

Abstract

High surgical bed occupancy levels often result in heightened staff stress, frequent surgical cancellations, and long surgical wait times. This congestion is in part attributable to surgical scheduling practices, which often focus on the efficient use of operating rooms but ignore resulting downstream bed utilization. This paper describes a transparent and portable approach to improve scheduling practices, which combines a Monte Carlo simulation model and a mixed integer programming (MIP) model. For a specified surgical schedule, the simulation samples from historical case records and predicts bed requirements assuming no resource constraints. The MIP model complements the simulation model by scheduling both surgeon blocks and patient types to reduce peak bed occupancies. Scheduling guidelines were developed from the optimized schedules to provide surgical planners with a simple and implementable alternative to the MIP model. This approach has been tested and delivered to planners in a health authority in British Columbia, Canada. The models have been used to propose new surgical schedules and to evaluate the impact of proposed system changes on ward congestion.

Monitoring changes in individual surgeon’s workloads using anesthesia data

“…Previous reports have included oneweek 3,5 and four weeks. 4 Also, the maximum wait must be a multiple of the duration of the master surgery schedule. For example, one surgeon has one block each week, e.g., on Tuesday the first week and on Friday the second week.…”

“…3 Small changes can predict and balance use of surgical equipment (e.g., a robot) and workload in surgical wards and intensive care units. 4,5 Small changes can also influence resident education. 6 Additional block time (OR resources) can be planned appropriately for certain individual surgeons to increase the number of cases of targeted procedures.…”

Integrated decision making in healthcare : an operations research and management science perspective

“…Often, surgical case scheduling is done in isolation. However, efficiency gains may be achieved by also considering decisions in other care services [81,84,102,274,398]. For example, without coordination with the ICU, a scheduled case may be rejected on its day of surgery due to a full ICU [398].…”

“…For example, without coordination with the ICU, a scheduled case may be rejected on its day of surgery due to a full ICU [398]. The contributions [13,81,102,177,256,337,366,398,433] do incorporate other care services, such as the patient wards and ICUs.…”

“…Methods: computer simulation [10,57,102,137,140,142,143,146,170,226,300,302,306,307,433,470,511], heuristics [10,13,83,131,136,175,177,225,226,256,306,308,341,416,422,455,486], Markov processes [196,228,365,381], mathematical programming [13,23,81,82,83,95,102,129,130,131,175,176,177,…”