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Development of a disease-based hospital-level diagnostic intensity index.
Ellenbogen, Michael I; Feldman, Leonard S; Prichett, Laura; Zhou, Junyi; Brotman, Daniel J.
Afiliación
  • Ellenbogen MI; Department of Medicine, 1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.
  • Feldman LS; Departments of Medicine and Pediatrics, 1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.
  • Prichett L; Biostatistics, Epidemiology, and Data Management (BEAD) Core, 1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.
  • Zhou J; Biostatistics, Epidemiology, and Data Management (BEAD) Core, 1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.
  • Brotman DJ; Department of Medicine, 1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.
Diagnosis (Berl) ; 11(3): 303-311, 2024 Aug 01.
Article en En | MEDLINE | ID: mdl-38643385
ABSTRACT

OBJECTIVES:

Low-value care is associated with increased healthcare costs and direct harm to patients. We sought to develop and validate a simple diagnostic intensity index (DII) to quantify hospital-level diagnostic intensity, defined by the prevalence of advanced imaging among patients with selected clinical diagnoses that may not require imaging, and to describe hospital characteristics associated with high diagnostic intensity.

METHODS:

We utilized State Inpatient Database data for inpatient hospitalizations with one or more pre-defined discharge diagnoses at acute care hospitals. We measured receipt of advanced imaging for an associated diagnosis. Candidate metrics were defined by the proportion of inpatients at a hospital with a given diagnosis who underwent associated imaging. Candidate metrics exhibiting temporal stability and internal consistency were included in the final DII. Hospitals were stratified according to the DII, and the relationship between hospital characteristics and DII score was described. Multilevel regression was used to externally validate the index using pre-specified Medicare county-level cost measures, a Dartmouth Atlas measure, and a previously developed hospital-level utilization index.

RESULTS:

This novel DII, comprised of eight metrics, correlated in a dose-dependent fashion with four of these five measures. The strongest relationship was with imaging costs (odds ratio of 3.41 of being in a higher DII tertile when comparing tertiles three and one of imaging costs (95 % CI 2.02-5.75)).

CONCLUSIONS:

A small set of medical conditions and related imaging can be used to draw meaningful inferences more broadly on hospital diagnostic intensity. This could be used to better understand hospital characteristics associated with low-value care.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Hospitalización / Hospitales Límite: Aged / Female / Humans / Male País/Región como asunto: America do norte Idioma: En Revista: Diagnosis (Berl) Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Hospitalización / Hospitales Límite: Aged / Female / Humans / Male País/Región como asunto: America do norte Idioma: En Revista: Diagnosis (Berl) Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos