Evidence map›Paper›PMID 42638355›Full record

ArticleJournal of medical Internet research2026

Digital Health Competence and Attitudes Toward AI Among Health Care Professionals: Convergent Mixed Methods Study.

Segundo Jimenez-Garcia, Manuela Domingo-Pozo, Jose Garcia-Rodriguez, David Tomás, David Ortiz-Perez, Kristina Mikkonen, Erika Jarva, M Flores Vizcaya-Moreno

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

8 authors.

Segundo Jimenez-GarciaDepartment of Nursing, Faculty of Health Sciences, University of Alicante, Carretera San Vicente del Raspeig s/n, Alicante, Alicante, 03690, Spain, 34 965 903 400 ext 2120.ORCID http://orcid.org/0000-0001-9473-0407
Manuela Domingo-PozoDepartment of Nursing, Faculty of Health Sciences, University of Alicante, Carretera San Vicente del Raspeig s/n, Alicante, Alicante, 03690, Spain, 34 965 903 400 ext 2120.ORCID http://orcid.org/0000-0002-4689-9942
Jose Garcia-RodriguezDepartment of Computer Science and Technology, Polytechnic School, University of Alicante, Alicante, Alicante, Spain.ORCID http://orcid.org/0000-0002-7798-3055
David TomásDepartment of Software and Computing Systems, Polytechnic School, University of Alicante, Alicante, Alicante, Spain.ORCID http://orcid.org/0000-0003-3287-9366
David Ortiz-PerezDepartment of Computer Science and Technology, Polytechnic School, University of Alicante, Alicante, Alicante, Spain.ORCID http://orcid.org/0009-0008-4890-8217
Kristina Mikkonen *Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, North Ostrobothnia, Finland.ORCID http://orcid.org/0000-0002-4355-3428
Erika JarvaResearch Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, North Ostrobothnia, Finland.ORCID http://orcid.org/0000-0001-6860-4319
M Flores Vizcaya-Moreno *Department of Nursing, Faculty of Health Sciences, University of Alicante, Carretera San Vicente del Raspeig s/n, Alicante, Alicante, 03690, Spain, 34 965 903 400 ext 2120.ORCID http://orcid.org/0000-0002-8826-9877

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Digital transformation is reshaping health care systems and requires health care professionals to develop advanced digital health competencies. The integration of AI into clinical practice introduces new demands related to critical evaluation, human oversight, ethical responsibility, and regulatory compliance. However, evidence linking validated measures of digital health competence with health care professionals' attitudes toward AI remains limited. Objective: This study aimed to (1) assess digital health competence among health care professionals in Spain, (2) examine organizational conditions supporting competence development, (3) explore perceptions about AI use in the workplace, and (4) examine the association between digital health competence and attitudes toward AI. Methods: A national cross-sectional convergent mixed methods study was conducted between November 2023 and January 2024 with a voluntary convenience sample of 229 health care professionals. Digital health competence was assessed using DigiHealthCom (digital health competence instrument; 42 items, 5 domains) and DigiComInf (the aspects associated with digital health competence instrument; 15 items, 3 domains). Confirmatory factor analysis evaluated structural validity. Open-ended responses regarding AI perceptions were explored using inductive qualitative content analysis. AI attitudes were classified into 4 categories (positive, negative, ambivalent, and uncertain), and their association with digital health competence was examined using multinomial logistic regression adjusted for age and sex. Results: Participants were predominantly female (169/229, 73.8%) and nurses (123/229, 53.7%), with a mean age of 45.9 (SD 10) years. Overall digital health competence was moderate (mean 3.01, SD 0.51), with the highest scores in information and communication technology competence (3.34, SD 0.63) and the lowest in competence related to evaluating and implementing digital solutions (2.83, SD 0.63). Organizational and educational support for competence development was also moderate (mean 2.51, SD 0.57), with organizational planning receiving the lowest DigiComInf scores (2.21, SD 0.76). Confirmatory factor analysis supported the proposed factor structures for both instruments (DigiHealthCom: comparative fit index=0.95, root-mean-square error of approximation=0.049; DigiComInf: comparative fit index=0.95, root-mean-square error of approximation=0.084). Most participants expressed positive attitudes toward AI in the workplace (134/228, 58.77%). Qualitative findings revealed a pattern of conditional optimism, with expected benefits for efficiency and patient care balanced by concerns regarding training, governance, regulation, and human oversight. Conclusions: Digital health competence was positively associated with health care professionals' attitudes toward AI. However, the cross-sectional design does not allow conclusions regarding the direction of this association. Deficiencies in higher-order competencies in evaluation and implementation, together with limited organizational support, highlight areas that may benefit from targeted educational and organizational strategies to promote the safe and responsible use of AI in health care.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelHealth PersonnelAdultCross-Sectional StudiesDigital HealthFemaleHumansMaleMiddle AgedSpainSurveys and QuestionnairesAIdigital healthdigital health competencedigital technologyhealth personnelmixed-methods research.professional competence

Identifiers

PMID42638355
PMCPMC13503649

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.