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1.
  • Goienetxea Uriarte, Ainhoa, 1983-, et al. (author)
  • How can decision makers be supported in the improvement of an emergency department? : A simulation, optimization and data mining approach
  • 2017
  • In: Operations Research for Health Care. - : Elsevier. - 2211-6923 .- 2211-6931. ; 15, s. 102-122
  • Journal article (peer-reviewed)abstract
    • The improvement of emergency department processes involves the need to take into considerationmultiple variables and objectives in a highly dynamic and unpredictable environment, which makes thedecision-making task extremely challenging. The use of different methodologies and tools to support thedecision-making process is therefore a key issue. This article presents a novel approach in healthcarein which Discrete Event Simulation, Simulation-Based Multi-Objective Optimization and Data Miningtechniques are used in combination. This methodology has been applied for a system improvementanalysis in a Swedish emergency department. As a result of the project, the decision makers were providedwith a range of nearly optimal solutions and design rules which reduce considerably the length of stayand waiting times for emergency department patients. These solutions include the optimal number ofresources and the required level of improvement in key processes. The article presents and discussesthe benefits achieved by applying this methodology, which has proven to be remarkably valuable fordecision-making support, with regard to complex healthcare system design and improvement.
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2.
  • Hallberg, Stefan, 1955, et al. (author)
  • Developing a simulation model for the patient pathway of cutaneous malignant melanoma
  • 2015
  • In: Operations Research for Health Care. - : Elsevier BV. - 2211-6923. ; 6, s. 23-30
  • Journal article (peer-reviewed)abstract
    • In this study, a system dynamic simulation model for a part of the patient pathway of cutaneous malignant melanoma was developed. A model was built that produced quantified out-put of diagnosed patients staged by severity, using System dynamic simulation. Data from the Swedish Melanoma Registry were combined with current scientific results and used in this analytical tool. Solutions to how these results are adapted into a simulation are described. The simulation model has the ability to generate alternative quantitative output of patient health in a population corresponding to realistic interventions in health care programmes as well as population and incidence development.
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