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Evaluation of Healthcare Coverage Efficiency Using Agent-Based Simulation . Joe Dinius ECE508 15 Oct 2009. Agenda. Introduction Review of the Literature Hypotheses Model Description Parameters Results Sensitivity Analysis Conclusions. Introduction.

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evaluation of healthcare coverage efficiency using agent based simulation

Evaluation of Healthcare Coverage Efficiency Using Agent-Based Simulation

Joe Dinius

ECE508

15 Oct 2009

agenda
Agenda
  • Introduction
  • Review of the Literature
  • Hypotheses
  • Model Description
  • Parameters
  • Results
  • Sensitivity Analysis
  • Conclusions
introduction
Introduction
  • Healthcare is a pressing issue in American society
    • Economic Recovery
    • Public Welfare
  • Current public health data is insufficient to evaluate impact to public health impact due to uninsured
  • Agent-based simulation provides flexible framework for evaluating impact of uninsured to overall public health
introduction1
Introduction
  • Parameters of interest will be means and standard deviations of
    • Cost
    • Resource Utilization (Hospitalization)
    • Time for epidemic to end
  • Agents are simple
    • Interaction rule is nearest-neighbor disease propagation
    • Hypothetical disease presented requiring hospitalization (resource utilization)
review of the literature
Review of the Literature
  • HUS08 publishes average data
    • Percentage of Americans who are uninsured
    • Cost of private vs. public insurance
  • Average results are misleading as not every condition requires treatment
  • Need data from catastrophic life events requiring hospitalization
      • Provides comparison between privately insured and uninsured
  • Other studies completed comparing FFS vs. managed care case studies for pregnant women in California
hypotheses
Hypotheses
  • Lower percentage of uninsured agents should lead to lower epidemic time
  • Should be difference in cost structure as number of uninsured agents increases
  • Non-profit insurance should ensure better care at less net cost
model description
Model Description
  • One agent is infected at initialization
  • Random draw for susceptibility of nearest-neighbors is performed and agents are infected accordingly
  • Agents are hospitalized one day after being infected and social network is broken
  • Agents are hospitalized until treatment time ends
    • If released before fully treated, time to be cured of the disease increases by a scale factor
model description1
Model Description
  • After infection occurs, agents’ susceptibility goes to 0
  • Simulation runs until number of infected agents is 0
parameters
Parameters
  • Insurance status
    • Cost
    • Treatment time
  • Susceptibility
  • Cure time
  • Scale factor for cure time
results1
Results
  • Refer to paper for statistical tables of output
conclusions
Conclusions
  • Less statistically significant differences than expected
    • Cost
    • Resource Utilization
    • Epidemic Duration
  • Metrics focused on were averaging
    • Less impact on results from transients
  • Hypothetical disease model suggests macroscopic view of contemporary healthcare problem is incomplete