The Self-Adapting Focused Review System: Probability Sampling of Medical Records to Monitor Utilization and Quality of Care

Medical record review is increasing in importance as the need to identify and monitor utilization and quality of care problems grow. To conserve resources, reviews are usually performed on a subset of cases. If judgment is used to identify subgroups for review, this raises the following questions: H...

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Bibliographic Details
Published in:Medical care 1990-11, Vol.28 (11), p.1025-1039
Main Authors: Ash, Arlene, Shwartz, Michael, Susan M. C. Payne, Restuccia, Joseph D.
Format: Article
Language:English
Subjects:
Bias
Datasets
Health care quality
Health Services Misuse
Hospital admissions
Hospital utilization rate
Hospitals - utilization
Humans
Information storage and retrieval systems
Medical Audit
Medical records
Modeling
Models, Statistical
Multivariate Analysis
Physicians
Population estimates
Probabilities
Probability
Quality Assurance, Health Care
Sampling Studies
United States
Utilization Review
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Summary:Medical record review is increasing in importance as the need to identify and monitor utilization and quality of care problems grow. To conserve resources, reviews are usually performed on a subset of cases. If judgment is used to identify subgroups for review, this raises the following questions: How should subgroups be determined, particularly since the locus of problems can change over time? What standard of comparison should be used in interpreting rates of problems found in subgroups? How can population problem rates be estimated from observed subgroup rates? How can the bias be avoided that arises because reviewers know that selected cases are suspected of having problems? How can changes in problem rates over time be interpreted when evaluating intervention programs? Simple random sampling, an alternative to subgroup review, overcomes the problems implied by these questions but is inefficient. The Self-Adapting Focused Review System (SAFRS), introduced and described here, provides an adaptive approach to record selection that is based upon model-weighted probability sampling. It retains the desirable inferential properties of random sampling while allowing reviews to be concentrated on cases currently thought most likely to be problematic. Model development and evaluation are illustrated using hospital data to predict inappropriate admissions.
ISSN:0025-7079
1537-1948
DOI:10.1097/00005650-199011000-00005