Hospital size, uncertainty, and pay-for-performance

We construct statistical models to assess whether hospital size will impact the ability to identify "true" hospital ranks in pay-for-performance (P4P) programs. We use Bayesian hierarchical models to estimate the uncertainty associated with the ranking of hospitals by their raw composite s...

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Bibliographic Details
Published in:Health care financing review 2007-01, Vol.29 (1), p.45-57
Main Authors: Davidson, Gestur, Moscovice, Ira, Remus, Denise
Format: Article
Language:English
Subjects:
Bayes Theorem
Central service department
Community-Acquired Infections - therapy
Coronary vessels
Diagnosis related groups
DRGs
Fines & penalties
Guideline Adherence - standards
Health administration
Health Facility Size
Heart Failure - therapy
Heart surgery
Hospitals
Hospitals - classification
Hospitals - standards
Humans
Medicaid
Medicare
Monetary incentives
Myocardial Infarction - therapy
Outcome Assessment (Health Care)
Pay for performance
Quality Indicators, Health Care - statistics & numerical data
Quality of Health Care
Reimbursement, Incentive
Size
Statistical analysis
Studies
Success
Uncertainty
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Summary:We construct statistical models to assess whether hospital size will impact the ability to identify "true" hospital ranks in pay-for-performance (P4P) programs. We use Bayesian hierarchical models to estimate the uncertainty associated with the ranking of hospitals by their raw composite score values for three medical conditions: acute myocardial infarction (AMI), heart failure (HF), and community acquired pneumonia (PN). The results indicate a dramatic inverse relationship between the size of the hospital and its expected range of ranking positions for its true or stabilized mean rank. The smallest hospitals among the augmented dataset would likely experience five to seven times more uncertainty concerning their true ranks.
ISSN:0195-8631
1554-9887