Enhancing performance of healthcare facility via novel simulation metamodeling based decision support framework

Farrukh Rasheed, Young Hoon Lee

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


A simulation model of patient throughput in the community healthcare center (CHC) located in Seoul, Korea is developed. The aforementioned CHC is providing primary, secondary and tertiary healthcare (HC) services, i.e. diagnostic, illness, treatment, health screening, immunization, family planning, ambulatory care, pediatric and gynecologic along with various other support services to uninsured, under-insured and low income patients residing in the nearby medically underserved areas. The prime aim of this investigation is to identify main imperative variables via statistical analysis of de-identified customer tracking system dataset and based-on expert opinion. Afterwards, using proposed novel simulation metamodeling based decision support framework to gauge their impact on performance measures of interest. The identified independent variables are resource shortage and stochastic demand pattern while performance measures of interest are the average length of stay (LOSa), balking probability (Pb), reneging probability (Pr), overcrowding and resource utilization. Significance: The methodology presented in this research is unique in a sense: a single meta-model represents a single performance measure and the solution found may be sub-optimal, having a detrimental effect on other crucial performance measures of interest if not considered. Hence, it is emphasized to develop all possible meta-models representing all the crucial performance measures individually for the purpose of overcoming aforesaid draw back so that final solution may qualify itself as a real-optimal solution.

Original languageEnglish
Pages (from-to)271-283
Number of pages13
JournalInternational Journal of Industrial Engineering : Theory Applications and Practice
Issue number5
Publication statusPublished - 2014

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All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering


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