title: A systems approach to healthcare: agent-based modeling, community mental health, and population well-being.
abstract: OBJECTIVE
  Explore whether agent-based modeling and simulation can help healthcare administrators discover interventions that increase population wellness and quality of care while, simultaneously, decreasing costs. Since important dynamics often lie in the social determinants outside the health facilities that provide services, this study thus models the problem at three levels (individuals, organizations, and society).
  
  
  METHODS
  The study explores the utility of translating an existing (prize winning) software for modeling complex societal systems and agent's daily life activities (like a Sim City style of software), into a desired decision support system. A case study tests if the 3 levels of system modeling approach is feasible, valid, and useful. The case study involves an urban population with serious mental health and Philadelphia's Medicaid population (n=527,056), in particular.
  
  
  RESULTS
  Section 3 explains the models using data from the case study and thereby establishes feasibility of the approach for modeling a real system. The models were trained and tuned using national epidemiologic datasets and various domain expert inputs. To avoid co-mingling of training and testing data, the simulations were then run and compared (Section 4.1) to an analysis of 250,000 Philadelphia patient hospital admissions for the year 2010 in terms of re-hospitalization rate, number of doctor visits, and days in hospital. Based on the Student t-test, deviations between simulated vs. real world outcomes are not statistically significant. Validity is thus established for the 2008-2010 timeframe. We computed models of various types of interventions that were ineffective as well as 4 categories of interventions (e.g., reduced per-nurse caseload, increased check-ins and stays, etc.) that result in improvement in well-being and cost.
  
  
  CONCLUSIONS
  The 3 level approach appears to be useful to help health administrators sort through system complexities to find effective interventions at lower costs.
authors: Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, United States. Electronic address: basil@seas.upenn.edu.
authors: Barry G
authors: BG
authors: Silverman
authors: School of Nursing, University of Pennsylvania, Philadelphia, PA 19104, United States.
authors: Nancy
authors: N
authors: Hanrahan
authors: Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, United States.
authors: Gnana
authors: G
authors: Bharathy
authors: Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, United States.
authors: Kim
authors: K
authors: Gordon
authors: Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, United States.
authors: Dan
authors: D
authors: Johnson
mesh_terms: D003156:Community Mental Health Services / Q000191:economics / Q000379:methods*; D003362:Cost-Benefit Analysis; D003661:Decision Support Techniques; D006293:Health Promotion; D006760:Hospitalization / Q000191:economics; D006801:Humans; D008484:Medicaid; D008603:Mental Health; D010359:Patient Readmission / Q000191:economics; D015143:Philadelphia; D013597:Systems Analysis; D014481:United States
keywords: Agent-based; Community healthcare; Decision support; Mental health systems; Readmission; Systems analysis