title: Reimbursement denial and reversal by health plans at a university hospital.
abstract: OBJECTIVE
  Denial and downgrading of reimbursement for hospital days are two strategies utilized by health plans to maintain profitability. The goal of this study was to describe patterns of discounted reimbursement at a university hospital.
  
  
  METHODS
  We performed a retrospective cohort study of consecutive per diem patients hospitalized in 1999. We defined a discounted day as a day fully denied or downgraded and a reversal day as a day reimbursed at a higher level after appeal. The study outcomes included the probability of a discounted day and the probability of a discounted day to be later reversed. Covariance logistic regression was used to compare these outcomes by plan and physician specialty after adjusting for age, sex, race, length of stay, and diagnosis. Correlations with plan characteristics were analyzed.
  
  
  RESULTS
  Of 59,265 hospital days, 6074 days (10.2%) were initially denied or downgraded. On appeal, 1755 discounted days (28.9%) were reversed. The percentage of days discounted per plan ranged from 1.2% to 18.8% (P <0.001), whereas the percentage of discounted days that were later reversed ranged from 23.2% to 85.3% (P <0.001). The qualitative magnitude of these associations and statistical significance were unchanged in adjusted models. Strong correlations were found between the adjusted odds ratio for a discounted day and net profit margin (R = 0.81) and medical loss ratio (R = -0.77).
  
  
  CONCLUSIONS
  Denials and downgrades are frequent, with marked variation by health plan. More profitable plans had higher denial and discount rates. Evidence-based standards for denials and downgrades are needed to maintain optimal patient care and the fiscal health of hospitals and health plans.
authors.affiliation: Division of Rheumatology, Department of Medicine, New York University School of Medicine, New York, USA.
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Greenberg
authors.forename: Donald R
authors.initials: DR
authors.lastname: Hoover
authors.forename: Ranita
authors.initials: R
authors.lastname: Sharma
authors.forename: Helaine
authors.initials: H
authors.lastname: Noveck
authors.forename: Maureen
authors.initials: M
authors.lastname: Bueno
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: Carson
mesh_terms: D000328:Adult; D000368:Aged; D015331:Cohort Studies; D003953:Diagnosis-Related Groups / Q000191:economics / Q000706:statistics & numerical data; D018588:Fee-for-Service Plans / Q000191:economics* / Q000706:statistics & numerical data; D005260:Female; D006279:Health Maintenance Organizations / Q000191:economics* / Q000706:statistics & numerical data; D017722:Hospital Charges / Q000706:statistics & numerical data*; D017721:Hospital Costs / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data; D006785:Hospitals, University / Q000706:statistics & numerical data*; D006801:Humans; D007349:Insurance, Health, Reimbursement / Q000706:statistics & numerical data*; D007902:Length of Stay; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D012189:Retrospective Studies