title: Prospective payment to encourage system wide quality improvement.
abstract: BACKGROUND
  Casemix-based inpatient prospective payment systems allocate payments for acute care based on what is done within an episode of care without regard for the outcome. To date, they have provided little incentive to improve quality. The Centers for Medicare & Medicaid Services have recently excluded 8 avoidable complications from their payment system.
  
  
  OBJECTIVE
  This study models an inpatient prospective payment system that comprehensively excludes not-present-on-admission and other complication diagnoses from the entire funding process, effectively adding a diagnosis-related group (DRG)-specific average complication payment across all discharges.
  
  
  METHODS
  Complication-averaged cost weights were estimated using the same patient level cost dataset used for estimating the relative resource weights for Victorian public hospitals in 2006-07. All codes with a "C" prefix (secondary diagnoses that are coded as having arisen after admission) and codes that define a condition that prima facie represent a specific complication of care were excluded from the code string. The episodes were then regrouped to DRGs and new complication-averaged cost weights were developed.
  
  
  RESULTS
  When complication codes were excluded across 1.2 million discharges, 1.37% became ungroupable, 14.86% included at least one complication diagnosis code, and 1.56% grouped to another DRG. Modeled funding for individual metropolitan hospitals in Victoria, Australia, was redistributed by -2.5% to 1.8%.
  
  
  CONCLUSIONS
  The cost weights reflect the average cost of preventable and unpreventable complications and have the potential to drive improvements in clinical care. This study is in contrast to previous studies estimating the funding impact of preventing all complications.
authors.affiliation: Funding Policy Unit, Victorian Department of Human Services, Melbourne, Victoria, Australia. peter.mcnair@dhs.vic.gov.au
authors.forename: Peter
authors.initials: P
authors.lastname: McNair
authors.forename: Daniel
authors.initials: D
authors.lastname: Borovnicar
authors.forename: Terri
authors.initials: T
authors.lastname: Jackson
authors.forename: Steve
authors.initials: S
authors.lastname: Gillett
mesh_terms: D000465:Algorithms; D003361:Cost Allocation; D003428:Cross Infection / Q000191:economics / Q000453:epidemiology / Q000517:prevention & control; D016208:Databases, Factual; D003953:Diagnosis-Related Groups / Q000145:classification / Q000191:economics*; D017050:Episode of Care; D005568:Forms and Records Control / Q000592:standards; D006779:Hospitals, Public / Q000191:economics* / Q000592:standards; D006801:Humans; D007049:Iatrogenic Disease / Q000453:epidemiology / Q000517:prevention & control*; D019300:Medical Errors / Q000191:economics / Q000517:prevention & control / Q000706:statistics & numerical data; D016344:Medicare Part A; D017059:Models, Econometric; D009313:National Health Programs / Q000191:economics* / Q000592:standards; D017063:Outcome Assessment, Health Care; D010351:Patient Discharge; D011445:Prospective Payment System; D012052:Reimbursement, Incentive; D017749:Total Quality Management / Q000379:methods*; D014481:United States; D014739:Victoria / Q000453:epidemiology