Intelligence modeling for coping strategies to reduce emergency department overcrowding in hospitals

  • Authors:

  • Chien-Lung Chan;Hsin-Tsung Huang;Huey-Jen You

  • Affiliations:

  • Department of Information Management, College of Informatics, Yuan Ze University, Zhongli, Taiwan, ROC;Department of Information Management, College of Informatics, Yuan Ze University, Zhongli, Taiwan, ROC and Medical Affairs Division, Bureau of National Health Insurance, Taipei, Taiwan, ROC;Department of Information Management, College of Informatics, Yuan Ze University, Zhongli, Taiwan, ROC and Medical Affairs Division, Bureau of National Health Insurance, Taipei, Taiwan, ROC

  • Venue:
  • Journal of Intelligent Manufacturing
  • Year:
  • 2012

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Abstract

Emergency department (ED) crowding is a common challenge for hospitals across the globe. The efficiency and effectiveness of ED services can be improved through identifying the causing ED crowding and modeling the prediction of ED crowding. The nature of ED crowding involves a complex dynamics of intertwined processes and workflows among the different departments within a hospital; thus, the problem cannot be tackled by examining ED alone. It is important to build a model which can identify the factors causing ED crowding and validate the coping strategies of hospitals. This study proposes an intelligence model which first introduces the well-know decision tree method to fit an accommodated nonlinear association and obtain intelligent grading rules of ED crowding; Then it integrates the intelligent grading rules and indexes of coping strategies to construct a hierarchical linear model. The results simultaneously solved traditional modeling issue of high correlation among independent variables and un-convergence. It also provides a better illustration of ED crowding phenomena with more accurate model fitting, as well as a clear linkage between coping strategies and the factors causing ED crowding. Furthermore, our proposed model can have a better understanding of problem nature and guild a better bed management for decision makers. It can also detect intelligently whether hospitals have drawn up active or passive bed management strategies to cope with ED crowding.