Evidence map›Paper›PMID 41970412›Full record

ArticleHealth science reports2026

Trends in Electronic Health Record Use Among Adult Day Services Centers, 2016-2022: A Cross-Sectional Panel Study.

Yawen Li, Jessica P Lendon, Shannon Kindilien, Christine Caffrey

Abstract read
In one paragraph

Article in Health science reports, 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.

Yawen LiSchool of Social Work California State University San Bernardino San Bernardino CA USA.ORCID https://orcid.org/0000-0001-5636-678X
Jessica P LendonCenters for Disease Control and Prevention National Center for Health Statistics Hyattsville MD USA.
Shannon KindilienCenters for Disease Control and Prevention National Center for Health Statistics Hyattsville MD USA.
Christine CaffreyCenters for Disease Control and Prevention National Center for Health Statistics Hyattsville MD USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Electronic health records (EHRs) use has increasingly become more common in healthcare settings, yet their adoption and use in adult day services centers (ADSCs) remains underexplored. This study investigates EHR trends from 2016 to 2022 and identifies organizational characteristics associated with their use, addressing a critical gap in community-based long-term care technology research. Methods: Data included the 2016, 2018, 2020, and 2022 waves of the National Post-acute and Long-term Care Study (NPALS), a nationally representative survey of ADSCs. EHR use was assessed through a single survey question. Independent variables included model type, ownership type, chain status, Medicaid licensure, size, and services provision. Weighted descriptive and survey-weighted logistic regression analyses were performed using Stata SE v18. Results: EHR use increased from 23.9% in 2016% to 31.2% in 2022 ( Conclusions: While EHR use has grown, a significant digital divide persists. Social model, smaller, smaller, independent, non-Medicaid-licensed centers, and for-profit centers are significantly less likely to adopt EHRs. Further research could examine the role of policies, funding, and decision-making processes that may present barriers or incentives to meaningful EHR implementation and whether they provide benefits for care coordination in ADSCs.

Indexed as

electronic health recordshealth technologylong‐term careNational Post‐acute Long‐term Care Studyolder adulttrend analysis

Identifiers

PMID41970412
PMCPMC13062268

What OpenQuestion holds

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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.