Resource Scheduling through Resource-Aware Simulation of Emergency Departments
by Seung Yeob Shin, Hari Balasubramanian, Yuriy Brun, Philip L. Henneman, Leon J. Osterweil
Abstract:
This paper proposes using resource-aware, discrete-event simulation to measure the effects of resource scheduling in hospital emergency departments. Determining staffing and resource allocation is a complex constraint-optimization problem that has significant impact on hospital costs and patient care quality. We developed detailed models of the emergency department process of caring for patients, the resources available to support that process, and the scheduling constraints on the deployment of those resources. We then ran a battery of discrete-event simulations of this process, varying details of process, resource mixes, and scheduling constraints, to analyze the effects of resource availability (e.g., staffing patterns) on patient length of stay. Our simulation approach proved to be particularly adept at supporting the systematic investigation of two issues of particular interest to domain experts: (1) an excessive focus on minimizing the average length of stay (the objective most typically used for optimizing emergency department staffing) can have undesirable, previously unappreciated effects, (2) too strong a focus on one particular kind of resource as the preferred vehicle for decreasing patient length of stay can tend to obscure the value of considering other kinds of resources. The unexpected nature of some of our results raises open questions about how to validate the results of complex simulations.
Citation:
Seung Yeob Shin, Hari Balasubramanian, Yuriy Brun, Philip L. Henneman, and Leon J. Osterweil, Resource Scheduling through Resource-Aware Simulation of Emergency Departments, in Proceedings of the 5th International Workshop on Software Engineering in Health Care (SEHC), 2013, pp. 64–70.
Bibtex:
@inproceedings{Shin13SEHC,
  author = {{Seung Yeob} Shin and Hari Balasubramanian and Yuriy Brun and Philip
  L. Henneman and Leon J. Osterweil},
  title = {\href{http://people.cs.umass.edu/brun/pubs/pubs/Shin13SEHC.pdf}{Resource
  Scheduling through Resource-Aware Simulation of Emergency Departments}},
  booktitle = {Proceedings of the 5th International Workshop on Software
  Engineering in Health Care (SEHC)},
  venue = {SEHC},
  address = {San Francisco, CA, USA},
  month = {May},
  date = {20--21},
  year = {2013},
  pages = {64--70},
  accept = {$\frac{16}{30} \approx 53\%$},
  doi = {10.1109/SEHC.2013.6602480},
  note = {\href{http://dx.doi.org/10.1109/SEHC.2013.6602480}{DOI:
  10.1109/SEHC.2013.6602480}},

  abstract = {This paper proposes using resource-aware, discrete-event
  simulation to measure the effects of resource scheduling in hospital emergency
  departments. Determining staffing and resource allocation is a complex
  constraint-optimization problem that has significant impact on hospital costs
  and patient care quality. We developed detailed models of the emergency
  department process of caring for patients, the resources available to support
  that process, and the scheduling constraints on the deployment of those
  resources. We then ran a battery of discrete-event simulations of this
  process, varying details of process, resource mixes, and scheduling
  constraints, to analyze the effects of resource availability (e.g., staffing
  patterns) on patient length of stay. Our simulation approach proved to be
  particularly adept at supporting the systematic investigation of two issues of
  particular interest to domain experts: (1) an excessive focus on minimizing
  the average length of stay (the objective most typically used for optimizing
  emergency department staffing) can have undesirable, previously unappreciated
  effects, (2) too strong a focus on one particular kind of resource as the
  preferred vehicle for decreasing patient length of stay can tend to obscure
  the value of considering other kinds of resources. The unexpected nature of
  some of our results raises open questions about how to validate the results of
  complex simulations.},

  fundedBy = {NSF IIS-1239334, NSF CNS-1258588, NSF IIS-0705772},
}