Application of an Analytic Model to Early Readmission Rates Within the Department of Veterans Affairs

Bibliographic Data

ID9103079
AuthorsNaomi R Wray (0000-0001-7421-3357, Health Services Research & Development, corresponding author), Nelda P Wray Nelda P Wray, Nancy J Peterson (Baylor College of Medicine, corresponding author), Nancy J Peterson Nancy J Peterson, Julianne Souchek (Baylor College of Medicine, corresponding author), Julianne Souchek Julianne Souchek, Carol M Ashton (Baylor College of Medicine, corresponding author), Carol M Ashton Carol M Ashton, John C Hollingsworth (Baylor College of Medicine, corresponding author), John C Hollingsworth John C Hollingsworth
Year1997
Volume35
Issue8
Pages768-781
Publication date1997-08-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/00005650-199708000-00003
PMID9268250
OpenAlexW2317664916
LanguageEN
Citations received5
References cited19

OBJECTIVES: Adverse outcome rates are increasingly used as yardsticks for the quality of hospital care. However, the validity of many outcome studies has been undermined by the application of one outcome to all patients in large, diagnostically diverse populations, many of which lack evidence of a link between antecedent process of care and the rate of the outcome, the underlying assumption of the analysis. METHODS: To address this analytic problem, the authors developed a model that improves the ability to identify quality problems because it selects diseases for which there are processes of care known to affect the outcome of interest. Thus, for these diseases, the outcome is most likely to be causally related to the antecedent care. In this study of hospital readmissions, risk-adjusted models were created for 17 disease categories with strong links between process and outcome. Using these models, we identified outlier hospitals. RESULTS: The authors hypothesized that if the model improved on identifying hospitals with quality of care problems, then outlier status would not be random. That is, hospitals found to have extreme rates in one year would be more likely to have extreme rates in subsequent years, and hospitals with extreme rates in one condition would be more likely to have extreme rates in related disease categories. It was hypothesized further that the correlation of outlier status across time and across diseases would be stronger in the 17 disease categories selected by the model than in 10 comparison disease categories with weak links between process and outcome. CONCLUSIONS: The findings support all these hypotheses. Although the present study shows that the model selects disease-outcome pairs where hospital outlier status is not random, the causal factors leading to outlier status could include (1) systematic unmeasured patient variation, (2) practice pattern variation that, although stable with time, is not indicative of substandard care, or (3) true quality-of-care problems. Primary data collection must be done to determine which of these three factors is most causally related to hospital outlier status

Antecedent (behavioral psychology) · Disease · Health care · Outcome (game theory) · Outlier · Statistics · Veterans Affairs · Heart Failure Treatment and Management · Internal Medicine · Medicine · Primary Care and Health Outcomes · Psychology · Sepsis Diagnosis and Treatment · Social Psychology