Estimating a Composite Measure of Hospital Quality From the Hospital Compare Database
Differences When Using a Bayesian Hierarchical Latent Variable Model Versus Denominator-Based Weights
Bibliographic Data
| ID | 9101927 |
|---|---|
| Authors | Michael Shwartz (0000-0003-4840-1682, VA Boston Healthcare System, corresponding author), Justin Ren (Boston University), Erol A Peköz (0000-0002-7459-7562, Boston University, corresponding author), Xin Wang (0000-0003-2833-6940, Brandeis University), Alan Cohen (0000-0002-5232-8927, VA Boston Healthcare System, corresponding author), Alan B Cohen, Joseph D Restuccia (VA Boston Healthcare System, corresponding author) |
| Year | 2008 |
| Volume | 46 |
| Issue | 8 |
| Pages | 778-785 |
| Publication date | 2008-08-01 |
| 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/mlr.0b013e31817893dc |
| PMID | 18665057 |
| OpenAlex | W2156995153 |
| Language | EN |
| Citations received | 6 |
| References cited | 31 |
BACKGROUND: A single composite measure calculated from individual quality indicators (QIs) is a useful measure of hospital performance and can be justified conceptually even when the indicators are not highly correlated with one another. OBJECTIVE: To compare 2 basic approaches for calculating a composite measure: an extension of the most widely-used approach, which weights individual indicators based on the number of people eligible for the indicator (referred to as denominator-based weights, DBWs), and a Bayesian hierarchical latent variable model (BLVM). METHODS: Using data for 15 QIs from 3275 hospitals in the Hospital Compare database, we calculated hospital ranks using several versions of DBWs and 2 BLVMs. Estimates in 1 BLVM were driven by differences in variances of the QIs (BLVM1) and estimates in the other by differences in the signal-to-noise ratios of the QIs (BLVM2). RESULTS: There was a high correlation in ranks among all of the DBW approaches and between those approaches and BLVM1. However, a high correlation does not necessarily mean that the same hospitals were ranked in the top or bottom quality deciles. In general, large hospitals were ranked in higher quality deciles by all of the approaches, though the effect was most apparent using BLVM2. CONCLUSIONS: Both conceptually and practically, hospital-specific DBWs are a reasonable approach for calculating a composite measure. However, this approach fails to take into account differences in the reliability of estimates from hospitals of different sizes, a big advantage of the Bayesian models
Bayesian probability · Data mining · Decile · Econometrics · Measure (data warehouse) · Quality (philosophy) · Reliability (semiconductor) · Statistics · Computer Science · Healthcare Policy and Management · Mathematics · Medicine · Patient Satisfaction in Healthcare · Primary Care and Health Outcomes
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| Unique citing works | 6 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2014 - 2025 (12) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |