Evidence map›Paper›PMID 42752334›Full record

Observational studyJournal of medical Internet research2026

Digital Health Readiness and Medicare Primary Care Spending: Nationwide County-Level Observational Analysis.

Zhaoqiang Zhou, John Geracitano, Baiming Zou, Saif Khairat

Abstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 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
–field-weighted citation impact
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

4 authors.

Zhaoqiang ZhouSchool of Data and Information Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID http://orcid.org/0009-0007-1046-3243
John GeracitanoSchool of Data and Information Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID http://orcid.org/0009-0003-6029-1778
Baiming ZouDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID http://orcid.org/0000-0002-7879-9460
Saif KhairatCarolina Health Informatics Program, University of North Carolina at Chapel Hill, 428 Carrington Hall, CB 7460, Chapel Hill, NC, 27514, United States, 1 919-843-5413.ORCID http://orcid.org/0000-0002-8992-2946

Funding

Center for Virtual Care Value and Excellence (ViVE). RC2TR004380 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Saif Khairat · 2023 to 2026
$3.0M
NCATS NIH HHS RC2 TR004380
6 · The paper itself

Abstract

Background: Digital health technologies are increasingly promoted as mechanisms to improve care coordination, enhance access, and reduce health care costs. However, whether community-level digitalization is associated with lower primary care spending in Medicare-participating safety net settings remains unclear, particularly in federally qualified health centers (FQHCs) and rural health clinics (RHCs). Objective: This study examined the association between county-level digitalization and Medicare primary care spending for FQHC and RHC services across US counties. Specifically, we assessed whether higher digitalization was associated with lower spending, whether observed associations reflected persistent differences between counties or changes within counties over time, and how digitalization interacted with health care access and socioeconomic conditions. Methods: We conducted a county-level observational study of 2993 US counties from 2017 to 2023. Digitalization was measured using the Digital Health Index (DHI) digitalization subindex, and the outcome was geographically adjusted for per capita Medicare spending on FQHC and RHC services. A hybrid within-between panel model was used to distinguish between-county from within-county associations. Additional analyses included threshold models, digitalization trajectory analyses, and interaction models. Using 2023 cross-sectional data, multivariable regression and Extreme Gradient Boosting models with Shapley additive explanations were applied to evaluate the relative contributions of digitalization, health care access, and socioeconomic conditions to spending variation. Results: Higher county-level digitalization was associated with lower Medicare primary care spending. In longitudinal analyses, the association was driven primarily by persistent between-county differences (β=-67.62, 95% CI -73.51 to -61.74; Conclusions: Higher county-level digitalization was associated with lower Medicare primary care spending in FQHC and RHC settings. However, the association reflected long-standing structural differences across counties rather than short-term changes in digital capacity. These findings suggest that digitalization functions as a component of broader health system capacity and may contribute to improved primary care efficiency when supported by sustained investment, adequate health care access, and favorable local conditions.

Indexed as

Digital HealthHealth ExpendituresMedicarePrimary Health CareHumansUnited Statesdigital healthhealth economicshealth informaticshealth policypopulation healthprimary care

Identifiers

PMID42752334
PMCPMC13585016

What OpenQuestion holds

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Read underepoch 390

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.