title: Can cardiovascular clinical characteristics be identified and outcome models be developed from an in-patient claims database?
abstract: The objective of this study was to assess whether administrative (claims) databases can be used to assess clinical variables and predict outcome. Although administrative databases are useful for assessing resource utilization, their utility for assessing clinical information is less certain. Prospectively gathered clinical databases, however, are expensive and not widely available. The UB92 formulation of the hospital bill was used as an administrative source of data and compared with the clinical cardiovascular database at Emory University. The claims database was compared with the clinical database for 11 variables. Outcome models were developed with multivariate methods. A total of 11,883 patients who underwent catheterization (5,255 underwent percutaneous transluminal coronary angioplasty [PTCA] and 3,794 underwent coronary artery bypass surgery [CABG]) between 1991 and 1995 were included. For some variables, the claims database correlated well (diabetes, sensitivity 87%, specificity 99%), whereas for others the claims database was less accurate (peripheral vascular disease, sensitivity 20%, specificity 99%). Uncertain coding in the claims database, which can result in the same code being used for co-morbid states and severity of disease, as well as complications, limited the ability of claims to predict outcome. Clinical databases may also be limited by lack of objectivity and missing data. The utility of claims databases to assess severity of disease and co-morbid states is limited, and outcome modeling and risk assessment from claims databases may be inappropriate and spurious. Developing better data standards and less expensive methods for acquisition of clinical data is necessary for improved outcome assessment.
authors.affiliation: Department of Medicine, School of Medicine, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA. bill@hp3.eushc.org
authors.forename: W S
authors.initials: WS
authors.lastname: Weintraub
authors.forename: C
authors.initials: C
authors.lastname: Deaton
authors.forename: L
authors.initials: L
authors.lastname: Shaw
authors.forename: E
authors.initials: E
authors.lastname: Mahoney
authors.forename: D C
authors.initials: DC
authors.lastname: Morris
authors.forename: C
authors.initials: C
authors.lastname: Saunders
authors.forename: D
authors.initials: D
authors.lastname: Canup
authors.forename: S
authors.initials: S
authors.lastname: Connolly
authors.forename: S
authors.initials: S
authors.lastname: Culler
authors.forename: E R
authors.initials: ER
authors.lastname: Becker
authors.forename: A
authors.initials: A
authors.lastname: Kosinski
authors.forename: S J
authors.initials: SJ
authors.lastname: Boccuzzi
mesh_terms: D002318:Cardiovascular Diseases / Q000473:pathology*; D019992:Databases as Topic; D005260:Female; D006801:Humans; D016490:Integrated Advanced Information Management Systems; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009816:Office Automation; D017063:Outcome Assessment, Health Care; D012720:Severity of Illness Index