Selecting Disease-Outcome Pairs for Monitoring the Quality of Hospital Care
Bibliographic Data
| ID | 9100582 |
|---|---|
| Authors | Nelda P Wray (Baylor College of Medicine, corresponding author), Carol M Ashton (Baylor College of Medicine, corresponding author), David H Kuykendall (Baylor College of Medicine, corresponding author), Nancy J Petersen (Baylor College of Medicine, corresponding author), Julianne Souchek (Baylor College of Medicine, corresponding author), John C Hollingsworth (Veterans Health Administration) |
| Year | 1995 |
| Volume | 33 |
| Issue | 1 |
| Pages | 75-89 |
| Publication date | 1995-01-01 |
| 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 | 10.1097/00005650-199501000-00007 |
| PMID | 7823649 |
| OpenAlex | W2027597358 |
| Language | EN |
| Citations received | 4 |
| References cited | 7 |
Health care payors and providers are increasingly monitoring hospital discharge data bases for adverse events as markers for quality of care. The principal criticisms of these analyses have focused on the impediments to risk adjustment posed by the incompleteness and inaccuracy of the data bases. However, efforts to address the inadequacies of the data bases will not correct deficiencies of the analytic process. These deficiencies arise from the application of one adverse outcome to all disease states. Instead, analysis should be restricted to comparisons of subgroups of patients in which a close fit exists between the quality of care for the disease state and the expected outcome. Furthermore, these disease-outcome pairs should be minimally subject to measurement error. The authors present a conceptual framework for developing such meaningful disease-outcome pairs, and using the hospital discharge data base of the Department of Veterans Affairs, show how the framework can be used to devise a monitoring strategy for re-admission
Actuarial science · Business · Computer security · Data quality · Disease · Economics · Health care · Intensive care medicine · Medical emergency · MEDLINE · Operations management · Outcome (game theory) · Political science · Principal (computer security) · Process (computing) · Quality (philosophy) · Quality management · Veterans Affairs · Computer Science · Emergency and Acute Care Studies · Healthcare Policy and Management · Internal Medicine · Medicine · Primary Care and Health Outcomes · Psychology
Application of an Analytic Model to Early Readmission Rates Within the Department of Veterans Affairs
Prediction of Readmissions After Cabg Using Detailed Follow-Up Data
Validation of the Potentially Avoidable Hospital Readmission Rate as a Routine Indicator of the Quality of Hospital Care
A conceptual framework for the study of early readmission as an indicator of quality of care
Validity, Reliability and Implications of an Index of Inpatient Severity of Illness
An Evaluation of Generic Screens for Poor Quality of Hospital Care on a General Medicine Service
Studying Outcomes and Hospital Utilization in the Elderly
Evaluating Hospital Discharge Planning
Measuring Hospital Performance
Investigating Early Readmission as an Indicator for Quality of Care Studies
Bias in the Coding of Hospital Discharge Data and Its Implications for Quality Assessment
| Citations per year | 0.13 |
|---|---|
| Citation span | 1996 - 2006 (11) |
| Highly cited | No |