Evidence map›Paper›PMID 41987022›Full record

ArticleHealth services research2026

Hospital-Wide Quality Assessment Using All-Condition Versus Summed-Condition Risk Adjustment.

Valérie Gopinath, Kelsey Chalmers, Vikas Saini

Abstract read
In one paragraph

Article in Health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Valérie GopinathLown Institute, Needham, Massachusetts, USA.
Kelsey ChalmersLown Institute, Needham, Massachusetts, USA.
Vikas SainiLown Institute, Needham, Massachusetts, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo compare risk-adjusted 30-day mortality using two hospital-wide methods: one across all admissions ("all-condition") versus a model with combined results from 15 condition/procedure cohorts ("summed-condition"). STUDY SETTING AND

designWe developed models on 2019 and 2020 (validated prospectively on 2020 and 2021) using Medicare admissions for patients aged 65 to 94 years. We measured hospitals' 30-day risk standardized mortality rate (RSMR) using both approaches, as well as two covariate selection methods: Risk Stratification Index (RSI) and Hierarchical Condition Categories (HCC). We reported model performance statistics, descriptive statistics comparing hospitals' RSMR between methods, and an exploration of hospital characteristics associated with different RSMR estimates from the two approaches using random forest regression. DATA SOURCES AND ANALYTIC SAMPLE: 100% sample of Medicare fee-for-service claims. We included 3638 general US hospitals with a secondary analysis on 1355 large hospitals. PRINCIPAL

findingsThere was high correlation between RSMRs from all- and summed-condition methods (0.89 correlation using RSI; 0.87 HCC). RSMR correlation between RSI and HCC was higher when using the all-condition versus summed-condition method (0.87 vs. 0.85). Model performance was higher using RSI (C-statistic 0.88 for all-condition; 0.84 to 0.95 summed-condition) than for HCC (C-statistic 0.87 for all-condition; 0.80 to 0.93 summed-condition). Small hospitals and those with a high proportion of non-surgical pulmonary admissions had lower estimated RSMRs under the summed-condition method compared to all-condition. For large hospitals, all- and summed-condition RSMRs were highly correlated using either RSI or HCC (correlation 0.97 RSI; 0.95 HCC).

conclusionsAll- and summed-condition RSMRs using RSI were highly correlated. Some hospital characteristics were associated with comparative RSMR results between all- versus summed-condition. For large hospitals, the choice of RSI versus HCC impacts RSMR more than the choice of all- versus summed-condition method.

Indexed as

Hospital MortalityHospitalsQuality Assurance, Health CareRisk AdjustmentAgedAged, 80 and overFemaleHumansMaleMedicareRisk AssessmentUnited Stateshospital performancehospital rankingrisk adjustment

Identifiers

PMID41987022
PMCPMC13083594

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.