title: Coding response to a case-mix measurement system based on multiple diagnoses.
abstract: OBJECTIVE
  To examine the hospital coding response to a payment model using a case-mix measurement system based on multiple diagnoses and the resulting impact on a hospital cost model.
  
  
  METHODS
  Financial, clinical, and supplementary data for all Ontario short stay hospitals from years 1997 to 2002.
  
  
  METHODS
  Disaggregated trends in hospital case-mix growth are examined for five years following the adoption of an inpatient classification system making extensive use of combinations of secondary diagnoses. Hospital case mix is decomposed into base and complexity components. The longitudinal effects of coding variation on a standard hospital payment model are examined in terms of payment accuracy and impact on adjustment factors.
  
  
  RESULTS
  Introduction of the refined case-mix system provided incentives for hospitals to increase reporting of secondary diagnoses and resulted in growth in highest complexity cases that were not matched by increased resource use over time. Despite a pronounced coding response on the part of hospitals, the increase in measured complexity and case mix did not reduce the unexplained variation in hospital unit cost nor did it reduce the reliance on the teaching adjustment factor, a potential proxy for case mix. The main implication was changes in the size and distribution of predicted hospital operating costs.
  
  
  CONCLUSIONS
  Jurisdictions introducing extensive refinements to standard diagnostic related group (DRG)-type payment systems should consider the effects of induced changes to hospital coding practices. Assessing model performance should include analysis of the robustness of classification systems to hospital-level variation in coding practices. Unanticipated coding effects imply that case-mix models hypothesized to perform well ex ante may not meet expectations ex post.
authors.affiliation: Department of Health Policy, Management and Evaluation, University of Toronto, Canada. Colin@preyra.com
authors.forename: Colin
authors.initials: C
authors.lastname: Preyra
mesh_terms: D015897:Comorbidity; D003361:Cost Allocation; D003953:Diagnosis-Related Groups / Q000145:classification* / Q000191:economics; D017598:Efficiency, Organizational; D005568:Forms and Records Control / Q000379:methods*; D006302:Health Services Research; D017721:Hospital Costs; D006751:Hospital Information Systems / Q000458:organization & administration*; D006801:Humans; D007297:Inpatients / Q000145:classification*; D008499:Medical Records / Q000145:classification*; D017059:Models, Econometric; D009864:Ontario; D012044:Regression Analysis; D012051:Reimbursement Mechanisms