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Can cardiovascular clinical characteristics be identified and outcome models be developed from an in-patient claims
W S Weintraub1, C Deaton, L Shaw
1Department of Medicine, School of Medicine, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA. bill@hp3.eushc.org
Insights
Administrative claims databases have limited accuracy for assessing clinical details and predicting patient outcomes, despite their usefulness for resource utilization. Developing better data standards is crucial for reliable clinical data acquisition and outcome assessment.
Area of Science:
- Health Services Research
- Medical Informatics
- Clinical Epidemiology
Background:
- Administrative databases are valuable for resource utilization but their accuracy for clinical variables is uncertain.
- Prospectively gathered clinical databases are expensive and not widely available.
- Assessing clinical variables and predicting outcomes are crucial for healthcare quality improvement.
Purpose of the Study:
- To evaluate the utility of administrative (claims) databases for assessing clinical variables.
- To determine if claims databases can accurately predict patient outcomes.
- To compare claims data accuracy with a clinical cardiovascular database.
Main Methods:
- Compared UB92 hospital bill claims data with a clinical cardiovascular database at Emory University.
- Included 11,883 patients undergoing catheterization, percutaneous transluminal coronary angioplasty (PTCA), or coronary artery bypass surgery (CABG) between 1991-1995.
- Developed outcome models using multivariate methods and assessed 11 variables for accuracy.
Main Results:
- Claims database showed high accuracy for some variables like diabetes (sensitivity 87%, specificity 99%).
- Accuracy was poor for other variables, such as peripheral vascular disease (sensitivity 20%, specificity 99%).
- Uncertain coding in claims databases limited the prediction of outcomes due to issues with co-morbid states, disease severity, and complications.
Conclusions:
- The utility of claims databases for assessing disease severity and co-morbid states is limited.
- Outcome modeling and risk assessment using claims databases may be inappropriate and unreliable.
- Improved data standards and cost-effective clinical data acquisition methods are necessary for accurate outcome assessment.
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.