Evidence map›Paper›PMID 42832696›Full record

ArticleJournal of medical Internet research2026

Beyond AI Literacy: Understanding Health Care Workforce AI Enablement in the Generative AI Era.

Chung-Feng Liu, Yen-Ling Ko

Abstract read
In one paragraph

Article 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

2 authors.

Chung-Feng LiuDepartment of Medical Research, Chi Mei Medical Center, Tainan, Taiwan.ORCID http://orcid.org/0000-0001-6698-0273
Yen-Ling KoDepartment of Internal Medicine, Chi Mei Medical Center, 901 Zhonghua Rd, Yongkang District, Tainan, 710402, Taiwan, 886 62812811 ext 52590.ORCID http://orcid.org/0000-0001-7651-1032

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: The rapid diffusion of generative AI is transforming health care delivery, education, administration, and research. In response, health care organizations have invested heavily in AI literacy initiatives and workforce development programs. Existing frameworks primarily focus on whether health care professionals understand AI and whether they can engage with AI effectively. However, health care practice increasingly reveals that individuals with similar levels of AI literacy and AI engagement often contribute very differently to AI-enabled work and organizational adoption. Some professionals primarily use AI to improve their own work, whereas others facilitate AI adoption, coordinate stakeholders, and integrate AI into routine practice. This observation suggests that current perspectives may overlook an important dimension of workforce AI enablement. In this Viewpoint, we argue that AI literacy and AI engagement alone provide an incomplete explanation of how health care organizations realize the benefits of AI. Drawing upon literature from AI literacy, fluency theory, human-AI interaction, innovation diffusion, implementation science, and health care workforce development, we propose the health care workforce AI enablement matrix (HWAEM). HWAEM conceptualizes workforce AI enablement through 2 complementary capabilities: AI fluency and AI harnessing. AI fluency refers to the capability to engage with AI effectively, appropriately, and responsibly across professional contexts, whereas AI harnessing refers to the capability to identify opportunities for AI-enabled improvement, mobilize stakeholders, facilitate adoption, and integrate AI into collective work practices. The interaction of these capabilities generates 4 workforce profiles: AI novices, AI practitioners, AI facilitators, and AI leaders. Through HWAEM, this Viewpoint argues that health care workforce AI enablement is better understood through the complementary capabilities of AI fluency and AI harnessing than through AI literacy and AI engagement alone. We illustrate how these profiles manifest in health care practice and discuss implications for workforce development and AI implementation. HWAEM offers a new perspective for understanding health care workforce preparedness in the generative AI era and provides a foundation for future empirical research.

Indexed as

Artificial IntelligenceHealth WorkforceGenerative Artificial IntelligenceHumansAIAI enablementAI fluencyAI harnessingAI literacyartificial intelligenceartificial intelligence enablementartificial intelligence fluencyartificial intelligence harnessingartificial intelligence literacyhealth care workforce

Identifiers

PMID42832696
PMCPMC13637978

What OpenQuestion holds

Textmetadata
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.