Evidence map›Paper›PMID 42115573›Full record

ArticleJournal of general internal medicine2026

Exploring the Ripple Effect: Changes in Quality Measures Before and After Whole Health Adoption in Veterans Health Administration.

Xiaoyi Zhang, Steven B Zeliadt, Rendelle E Bolton, Bella Etingen, Scott S Coggeshall, Ethan W Rosser, Travis Y Hee Wai, Gang Luo, Benjamin Kligler, Barbara G Bokhour

Abstract read
In one paragraph

Article in Journal of general internal medicine, 2026. 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
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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.

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

10 authors.

Xiaoyi ZhangVA Center of Innovation (COIN) for Veteran-Centered and Value-Driven Care, VA Puget Sound Healthcare System, Seattle, WA, USA.
Steven B ZeliadtVA Center of Innovation (COIN) for Veteran-Centered and Value-Driven Care, VA Puget Sound Healthcare System, Seattle, WA, USA. szeliadt@uw.edu.ORCID http://orcid.org/0000-0003-3900-743X
Rendelle E BoltonCenter for Health Optimization and Implementation Research (CHOIR), VA Bedford and Boston Healthcare Systems, Bedford and Boston, MA, USA.
Bella EtingenResearch and Development Service, Dallas VA Medical Center, Dallas, TX, USA.
Scott S CoggeshallVA Center of Innovation (COIN) for Veteran-Centered and Value-Driven Care, VA Puget Sound Healthcare System, Seattle, WA, USA.
Ethan W RosserVA Center of Innovation (COIN) for Veteran-Centered and Value-Driven Care, VA Puget Sound Healthcare System, Seattle, WA, USA.
Travis Y Hee WaiVA Center of Innovation (COIN) for Veteran-Centered and Value-Driven Care, VA Puget Sound Healthcare System, Seattle, WA, USA.
Gang LuoDepartment of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA, USA.
Benjamin KliglerOffice of Patient Centered Care & Cultural Transformation, Veterans Health Administration, Washington, DC, USA.
Barbara G BokhourCenter for Health Optimization and Implementation Research (CHOIR), VA Bedford and Boston Healthcare Systems, Bedford and Boston, MA, USA.

Funding

HSRD VA PEC 13-001Quality Enhancement Research Initiative PEC 13-001
6 · The paper itself

Abstract

backgroundThe Veterans Health Administration (VHA) and other health care systems have been moving to patient-centered health care models with a focus on individual goal setting and patient engagement. In VHA, this model of care is called Whole Health, implementation of which began in 2011.

objectiveTo examine whether Whole Health utilization is associated with subsequent improvements in clinical quality measures of chronic disease management and preventive health care services.

designControlled pre/post quality improvement evaluation accounting for secular trends.

participantsVeterans at 125 VHA medical centers who utilized care between 10/01/2022 and 03/31/2023. 

interventionsVeterans with Whole Health utilization that included Whole Health educational classes, coaching activities, and Whole Health clinical care conversations with providers were assessed alongside reference patients who used general VHA care during the same period.  MAIN MEASURES: Change in clinical quality measures one month before using Whole Health care to 6 months after. KEY

resultsThe population included 548,968 Whole Health users and 5,428,413 general VHA users. Improvements were observed in all nine clinical quality measures following Whole Health utilization and continued to improve over 6 months. The proportions of Whole Health cohorts with quality improvements were greater than the reference cohorts over the same period. For example, among patients with diabetes, 80.1% of the Whole Health group had good HbA1c control (<8%) at 6 months compared to 74.9% before using Whole Health, an increase of 5.2% (95% CI 4.8-5.6%). The adjusted proportion improving after accounting for demographic and clinical characteristics as well as secular trends in the reference cohort was 4.4% (95% CI 4.1-4.7%).

conclusionsThese exploratory findings observed that Whole Health utilization was associated with subsequent improvements in health care quality, suggesting this patient-centered care model can potentially augment primary care, achieving a ripple effect improving chronic disease management and preventive care adherence through increased patient activation and engagement.

Indexed as

Quality ImprovementQuality Indicators, Health CareUnited States Department of Veterans AffairsVeterans Health ServicesAgedChronic DiseaseFemaleHumansMaleMiddle AgedPatient-Centered CareUnited StatesVeteransVeterans Healthchronic diseasepatient-centered carepreventive health servicesquality improvementveterans health

Identifiers

PMID42115573
PMCPMC13631129

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Registered trials

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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.