Evidence map›Paper›PMID 38714970›Full record

ArticleBMC health services research2024

Lower comorbidity scores and severity levels in Veterans Health Administration hospitals: a cross-sectional study.

Matthew P Dizon, Adam Chow, Michael K Ong, Ciaran S Phibbs, Megan E Vanneman, Yue Zhang, Jean Yoon

Abstract read
In one paragraph

Article in BMC health services research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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

7 authors.

Matthew P DizonCenter for Innovation to Implementation, Veterans Affairs Palo Alto Health Care System, 795 Willow Road (152 MPD), Menlo Park, CA, USA. Matthew.Dizon@va.gov.
Adam ChowHealth Economics Resource Center, Veterans Affairs Palo Alto Health Care System, Menlo Park, CA, USA.
Michael K OngCenter for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
Ciaran S PhibbsDepartment of Health Policy, Stanford University School of Medicine, Stanford, CA, USA.
Megan E VannemanInformatics, Decision-Enhancement and Analytic Sciences Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
Yue ZhangInformatics, Decision-Enhancement and Analytic Sciences Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
Jean YoonHealth Economics Resource Center, Veterans Affairs Palo Alto Health Care System, Menlo Park, CA, USA.

Funding

CTSA UM1 Program at University of UtahUM1TR004409 · NCATS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI RACHEL HESS, Jennifer Juhl Majersik · 2023 to 2026
$21.9M
NCATS NIH HHS UM1 TR004409
6 · The paper itself

Abstract

backgroundPrevious studies found that documentation of comorbidities differed when Veterans received care within versus outside Veterans Health Administration (VHA). Changes to medical center funding, increased attention to performance reporting, and expansion of Clinical Documentation Improvement programs, however, may have caused coding in VHA to change.

methodsUsing repeated cross-sectional data, we compared Elixhauser-van Walraven scores and Medicare Severity Diagnosis Related Group (DRG) severity levels for Veterans' admissions across settings and payers over time, utilizing a linkage of VHA and all-payer discharge data for 2012-2017 in seven US states. To minimize selection bias, we analyzed records for Veterans admitted to both VHA and non-VHA hospitals in the same year. Using generalized linear models, we adjusted for patient and hospital characteristics.

resultsFollowing adjustment, VHA admissions consistently had the lowest predicted mean comorbidity scores (4.44 (95% CI 4.34-4.55)) and lowest probability of using the most severe DRG (22.1% (95% CI 21.4%-22.8%)). In contrast, Medicare-covered admissions had the highest predicted mean comorbidity score (5.71 (95% CI 5.56-5.85)) and highest probability of using the top DRG (35.3% (95% CI 34.2%-36.4%)).

conclusionsMore effective strategies may be needed to improve VHA documentation, and current risk-adjusted comparisons should account for differences in coding intensity.

Indexed as

ComorbidityHospitals, VeteransSeverity of Illness IndexAgedAged, 80 and overCross-Sectional StudiesDiagnosis-Related GroupsFemaleHumansMaleMedicareMiddle AgedUnited StatesUnited States Department of Veterans AffairsVeteransAdministrative dataDocumentationHealth care qualityObservational study designsOutcomes research

Identifiers

PMID38714970
PMCPMC11077812

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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.