Evidence map›Paper›PMID 42553739›Full record

ReviewFrontiers in digital health2026

Human-centered AI in healthcare teams: integrating clinical oversight, EHR auditability, and reimbursement pathways for responsible adoption.

Michael C Changaris, Franca V Niameh

Abstract readReview
In one paragraph

Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Michael C ChangarisIntegrated Health Psychology Program, The Wright Institute, Berkeley, CA, United States.
Franca V NiamehIntegrated Health Psychology Program, The Wright Institute, Berkeley, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: AI is being introduced into clinical workforces during a critical transition toward integrated, value-based models of care, where its greatest promise lies in augmenting clinician judgment and expanding the reach of already strained healthcare teams. Yet clinical adoption remains limited because most AI systems lack reimbursement pathways, impose substantial implementation costs, and lack standardized mechanisms for integration into electronic health records (EHRs). These gaps create misalignment between technological capability and clinical usability. This paper identifies financial, regulatory, and workflow structures required for AI to operate safely, predictably, and sustainably across key domains of healthcare. Methods: This narrative synthesis reviews clinical, economic, regulatory, and implementation-science literature from 2022 to 2025. Four domains were analyzed: (1) AI augmentation of clinical workflows; (2) reimbursement structures and CPT coding pathways; (3) EHR-based AI deployment and governance; and (4) economic and equity considerations for large-scale implementation. Sources included peer-reviewed reviews, white papers, consensus statements, and health policy analyses. Results: AI tools demonstrated benefits in diagnostic accuracy, decision support, and documentation efficiency, particularly in radiology, cardiology, and EHR-integrated workflows. Adoption was hindered by absent reimbursement for clinician-reviewed AI outputs. Implementation and monitoring costs fell heavily on health systems, risking widened disparities. Additional concerns included accuracy, bias, generalizability, and limited oversight. Enabling conditions included clinician-in-the-loop review, auditable outputs, equity-centered validation, and alignment with evolving payment models. Conclusions: AI can strengthen healthcare teams and advance value-based care, but scaling requires aligned incentives, rigorous evaluation standards, equity safeguards, and coordinated governance-echoing lessons from national EHR implementation.

Indexed as

artificial intelligenceclinical decision supportelectronic health recordshealth policyimplementation scienceintegrated behavioral healthreimbursement modelsvalue-based care

Identifiers

PMID42553739
PMCPMC13435324

What OpenQuestion holds

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LicenceCC BY
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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.