Evidence map›Paper›PMID 38643385›Full record

ArticleDiagnosis (Berlin, Germany)2024

Development of a disease-based hospital-level diagnostic intensity index.

Michael I Ellenbogen, Leonard S Feldman, Laura Prichett, Junyi Zhou, Daniel J Brotman

Open access · greenAbstract read
In one paragraph

Article in Diagnosis (Berlin, Germany), 2024. 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, top 91% of its field
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, 0 citations in OpenAlex.

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

5 authors at 1 institution in 1 country.

Michael I EllenbogenDepartment of Medicine, 1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.ORCID 0000-0003-0701-8054
Leonard S FeldmanDepartments of Medicine and Pediatrics, 1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.
Laura PrichettBiostatistics, Epidemiology, and Data Management (BEAD) Core, 1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.
Junyi ZhouBiostatistics, Epidemiology, and Data Management (BEAD) Core, 1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.
Daniel J BrotmanDepartment of Medicine, 1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.
Johns Hopkins University · US

Funding

Developing and refining methods to measure hospital-level diagnostic intensity, identify outlier hospitals, and characterize the relationship between diagnostic intensity and missed diagnosesK08HS028673 · AHRQ · JOHNS HOPKINS UNIVERSITY · PI ELLENBOGEN, MICHAEL · 2022 to 2024
$466k
AHRQ HHS K08 HS028673
6 · The paper itself

Abstract

objectivesLow-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.

methodsWe 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.

resultsThis 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)).

conclusionsA 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.

Indexed as

HospitalizationHospitalsAgedDatabases, FactualDiagnostic ImagingFemaleHealth Care CostsHumansMaleMedicareUnited Statesdiagnostic testinghospital variationlow-value careoveruse

Identifiers

PMID38643385
PMCPMC11306196
OpenAlexW4394983234

What OpenQuestion holds

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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.