ArticleHealth services research2018
Improving Hospital Performance Rankings Using Discrete Patient Diagnoses for Risk Adjustment of Outcomes.
Article in Health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Medical service performance evaluation of tertiary general hospitals in Sichuan Province in China based on diagnosis-related groups.BMC health services research · 2025Article
- Evaluating medical service performance of hospitals in Sichuan Province, China: exploratory factor analysis and hierarchical clustering analysis based on diagnosis-related groups.Frontiers in public health · 2025Article
- Application of the Risk Stratification Index to Multilevel Models of All-condition 30-Day Mortality in Hospitalized Populations Over the Age of 65.Medical care · 2021Article
- Intersections of machine learning and epidemiological methods for health services research.International journal of epidemiology · 2021Article
- Impact of DRGs-based inpatient service management on the performance of regional inpatient services in Shanghai, China: an interrupted time series study, 2013-2019.BMC health services research · 2020Article
- Development and Testing of Improved Models to Predict Payment Using Centers for Medicare & Medicaid Services Claims Data.JAMA network open · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo assess the changes in patient outcome prediction and hospital performance ranking when incorporating diagnoses as risk adjusters rather than comorbidity indices. DATA SOURCES: Healthcare Cost and Utilization Project State Inpatient Databases for New York State, 2005-2009. STUDY
designConducted tree-based classification for mortality and readmission by incorporating discrete patient diagnoses as predictors, comparing with traditional comorbidity indices such as those used for Centers for Medicare and Medicaid Services (CMS) outcome models. PRINCIPAL
findingsDiagnosis codes as predictors increased predictive accuracy 5.6 percent (95% CI: 4.5-6.9 percent) relative to CMS condition categories for heart failure 30-day mortality. Most other outcomes exhibited statistically significant accuracy gains and facility ranking shifts. Sensitivity analysis showed improvements even when predictors were limited to only the diagnoses included in CMS models.
conclusionsDiscretizing patient severity information beyond the levels of traditional comorbidity indices improves patient outcome predictions and substantially shifts facility rankings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.