The Self-Adapting Focused Review System
Probability Sampling of Medical Records to Monitor Utilization and Quality of Care
Bibliographic Data
| ID | 14338291 |
|---|---|
| Authors | Arlene S Ash (0000-0002-8448-0253, Boston University, corresponding author), Arlene Ash, Michael Shwartz (0000-0003-4840-1682, Boston University), Susan M C Payne, Susan Payne (0000-0001-9907-3477, Boston University, corresponding author), Joseph D Restuccia (Boston University) |
| Year | 1990 |
| Volume | 28 |
| Issue | 11 |
| Pages | 1025-1040 |
| Publication date | 1990-11-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/00005650-199011000-00005 |
| PMID | 2250490 |
| OpenAlex | W2014163398 |
| Language | EN |
| References cited | 6 |
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
Intervention (counseling) · Population · Sampling (signal processing) · Simple random sample · Statistics · Computer Science · Healthcare Policy and Management · Mathematics · Medicine · Nursing · Patient Satisfaction in Healthcare · Primary Care and Health Outcomes
Measuring the Accuracy of Diagnostic Systems
A Review of Goodness of Fit Statistics for Use in the Development of Logistic Regression Models1
Screening for utilization review
Peer Review of Medical Care
Patient, provider and hospital characteristics associated with inappropriate hospitalization
The Appropriateness Evaluation Protocol
| Citation velocity | historical |
|---|---|
| Highly cited | No |