title: Diagnostic certainty and hospital resource use.
abstract: OBJECTIVE
  To determine whether the house staff's subjective probability estimates of their initial admitting diagnoses are independent predictors of in-hospital resource consumption.
  
  
  METHODS
  Descriptive correlational study.
  
  
  METHODS
  Academic medical center inpatient setting.
  
  
  METHODS
  Patients admitted to general medicine wards.
  
  
  METHODS
  A visual analog scale with hash marks at 0, 25, 50, 75 and 100 was used to obtain a subjective probability estimate that the house staff's initial admitting diagnosis was a correct diagnosis. This provided the measure of diagnostic "certainty" at the time of admission. Patient demographic data, prior hospital stays, distance from hospital, MedisGroups scores, outlier status and vital status at discharge were obtained from administrative systems. Length of stay, total charges, cost estimates (total departmental), and number of consultations were obtained from the hospital-based transaction master database.
  
  
  RESULTS
  House staff evaluated 1,778 admissions, 77.2% were assigned a diagnostic certainty rating of 75% or higher. In univariate analysis, the certainty rating did not vary with the MedisGroups score, outlier status or vital status at discharge. It varied with prior stays and measures of resource use. In multivariate analysis, the certainty rating was a significant factor accounting for variation in each of the measures of resource utilization with the exception of adjusted pharmacy charges.
  
  
  CONCLUSIONS
  Even in a teaching hospital only a small proportion of patients had an "uncertain" diagnosis (22.8%). Nonetheless, the certainty variables were significantly related to measures of resource consumption including length of stay, total costs and number of consults obtained.
authors.affiliation: University of Pittsburgh Graduate School of Public Health, PA, USA.
authors.forename: J R
authors.initials: JR
authors.lastname: Lave
authors.forename: R A
authors.initials: RA
authors.lastname: Bankowitz
authors.forename: P
authors.initials: P
authors.lastname: Hughes-Cromwick
authors.forename: N B
authors.initials: NB
authors.lastname: Giuse
mesh_terms: D000328:Adult; D000368:Aged; D016001:Confidence Intervals; D003625:Data Collection; D003657:Decision Making; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D004496:Education, Continuing; D005260:Female; D006302:Health Services Research / Q000379:methods; D017721:Hospital Costs / Q000706:statistics & numerical data; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D007396:Internship and Residency / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D010343:Patient Admission; D010414:Pennsylvania; D016012:Poisson Distribution; D011336:Probability