Evidence map›Paper›PMID 41313170›Full record

ArticleJournal of medical Internet research2025

Digital Health Technology Adoption Among Chinese Physicians: Latent Profile Analysis and Cross-Sectional Study.

Zhichao Wang, Jiao Lu, Zhongliang Zhou, Guanping Liu, Xiaohui Zhai, Dan Cao, Shaoqing Gong

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

7 authors.

Zhichao Wang *School of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, 710049, China, 86 18629510661.ORCID http://orcid.org/0000-0002-4397-8469
Jiao LuSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, 710049, China, 86 18629510661.ORCID http://orcid.org/0000-0003-0866-8104
Zhongliang Zhou *School of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, 710049, China, 86 18629510661.ORCID http://orcid.org/0000-0002-3850-4752
Guanping LiuSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, 710049, China, 86 18629510661.ORCID http://orcid.org/0000-0003-1842-1033
Xiaohui ZhaiSchool of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China.ORCID http://orcid.org/0000-0003-1756-7081
Dan CaoSchool of Public Administration, Xi'an University of Architecture and Technology, Xi'an, China.ORCID http://orcid.org/0000-0002-8143-0998
Shaoqing Gong *Luohe Medical College, Luohe, China.ORCID http://orcid.org/0009-0006-4910-1090

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Digital health technologies (DHTs) are transforming global health care delivery, yet physician adoption remains highly variable and influenced by a complex interplay of individual, institutional, and technological factors. In China, despite national initiatives such as the "Healthy China 2030" strategy promoting DHT integration, understanding physicians' heterogeneous perceptions is essential for effective implementation. Objective: This study aimed to identify distinct latent profiles of Chinese physicians based on their perceptions of DHT benefits, barriers, and behavioral intention, and to examine the demographic and occupational factors associated with profile membership. Methods: A cross-sectional survey was conducted among 4851 physicians (female, n=2994, 60.69% ; mean age 38.4, SD 8.7 years; 51.58% (n=2502) with more than 10 y working experience) from 46 hospitals in Shaanxi Province, China, between October and December 2023. Assessment included nine indicators across three domains: Perceived Benefits (4 items), Adoption Barriers (4 items), and Behavioral Intention (1 item). Latent profile analysis was used to identify distinct subgroups of physicians based on their response patterns. Multinomial logistic regression examined predictors of profile membership, and results were reported as odds ratios (ORs) with 95% CIs. Results: The latent profile analysis identified 5 distinct profiles: Reform-Adaptable (n=516, 10.64%; 95% CI 9.76%-11.52%), Negative (n=1003, 20.68%; 95% CI 19.50%-21.86%), Neutral (n=2276, 46.92%; 95% CI 45.50%-48.34%), Reform-Conservative (n=545, 11.23%; 95% CI 10.33%-12.13%), and Positive (n=511, 10.53%; 95% CI 9.66%-11.40%). Significant intergroup differences were observed in demographic and occupational characteristics. For instance, compared with the Negative profile, male physicians were less likely to belong to the Neutral (OR 0.76, 95% CI 0.64-0.90; P=.001) and Reform-Conservative (OR 0.67, 95% CI 0.54-0.84; P=.001) profiles. Compared to the Neutral profile, physicians with a master's degree or above were less likely to be in the Reform-Conservative profile (OR 0.75, 95% CI 0.59-0.96; P=.052). Those working in tertiary hospitals were less likely to belong to the Positive group (OR range 0.56-0.66, P=.001). High-income physicians were more likely to be in the Reform-Conservative group (OR range 1.83-2.38, P=.001). In addition, higher occupational stress was associated with a greater likelihood of Positive profile membership (OR range 1.12-1.26, P=.001), while better work satisfaction predicted higher odds of Positive profile membership (OR range 1.04-1.16, P=.001). Conclusions: This study introduces a novel, person-centered approach by identifying five distinct perceptual typologies among physicians, moving beyond traditional variable-centered analyses. This typology provides an evidence-based foundation for tailored interventions. For instance, the Reform-Adaptable group may need barrier reduction, while the Reform-Conservative group may require clearer value demonstrations. This nuanced understanding can help healthcare systems enhance the impact and scalability of digital health technologies in real-world clinical practice.

Indexed as

Attitude of Health PersonnelDigital TechnologyPhysiciansAdultChinaCross-Sectional StudiesDigital HealthEast Asian PeopleFemaleHumansMaleMiddle AgedSurveys and Questionnairesdigital health technologyheterogeneous adoption profilesimplementation strategieslatent profile analysisphysician adoption

Identifiers

PMID41313170
PMCPMC12661596

What OpenQuestion holds

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LicenceCC BY
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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.