title: Modeling hospital infrastructure by optimizing quality, accessibility and efficiency via a mixed integer programming model.
abstract: BACKGROUND
  The majority of curative health care is organized in hospitals. As in most other countries, the current 94 hospital locations in the Netherlands offer almost all treatments, ranging from rather basic to very complex care. Recent studies show that concentration of care can lead to substantial quality improvements for complex conditions and that dispersion of care for chronic conditions may increase quality of care. In previous studies on allocation of hospital infrastructure, the allocation is usually only based on accessibility and/or efficiency of hospital care. In this paper, we explore the possibilities to include a quality function in the objective function, to give global directions to how the 'optimal' hospital infrastructure would be in the Dutch context.
  
  
  METHODS
  To create optimal societal value we have used a mathematical mixed integer programming (MIP) model that balances quality, efficiency and accessibility of care for 30 ICD-9 diagnosis groups. Typical aspects that are taken into account are the volume-outcome relationship, the maximum accepted travel times for diagnosis groups that may need emergency treatment and the minimum use of facilities.
  
  
  RESULTS
  The optimal number of hospital locations per diagnosis group varies from 12-14 locations for diagnosis groups which have a strong volume-outcome relationship, such as neoplasms, to 150 locations for chronic diagnosis groups such as diabetes and chronic obstructive pulmonary disease (COPD).
  
  
  CONCLUSIONS
  In conclusion, our study shows a new approach for allocating hospital infrastructure over a country or certain region that includes quality of care in relation to volume per provider that can be used in various countries or regions. In addition, our model shows that within the Dutch context chronic care may be too concentrated and complex and/or acute care may be too dispersed. Our approach can relatively easily be adopted towards other countries or regions and is very suitable to perform a 'what-if' analysis.
authors.affiliation: KPMG Plexus, Breukelen, The Netherlands. Ikkersheim.david@kpmgplexus.nl
authors.forename: David
authors.initials: D
authors.lastname: Ikkersheim
authors.forename: Marit
authors.initials: M
authors.lastname: Tanke
authors.forename: Gwendy
authors.initials: G
authors.lastname: van Schooten
authors.forename: Niels
authors.initials: N
authors.lastname: de Bresser
authors.forename: Hein
authors.initials: H
authors.lastname: Fleuren
mesh_terms: D002908:Chronic Disease / Q000145:classification; D003198:Computer Simulation; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data*; D017598:Efficiency, Organizational; D006297:Health Services Accessibility / Q000592:standards*; D006761:Hospitals / Q000592:standards* / Q000706:statistics & numerical data; D006782:Hospitals, Special; D006801:Humans; D038801:International Classification of Diseases; D008962:Models, Theoretical; D009426:Netherlands; D011787:Quality of Health Care; D012984:Software; D017749:Total Quality Management