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Cited by 50Artificial Intelligence in Healthcare and EducationHealth Policy Implementation ScienceElectronic Health Records Systems

Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research

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Liao, Xiwen, Yao, Chen, Jin, Feifei, Zhang, Jun, Liu, Larry·2024·BMJ Open
DOI 10.1136/bmjopen-2024-084398PMC PMC11409362
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Abstract

Objectives: To identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as a theoretical basis to develop strategies that promote effective implementation. Design: This qualitative study involved semistructured interviews with key stakeholders from both clinical settings and industry. Interview guide development, coding, analysis and reporting of findings were thoroughly informed by the updated CFIR. Setting: Four healthcare institutions in Beijing and Shanghai and two vendors of AI-assisted decision-making software for lung nodules detection and diabetic retinopathy screening were selected based on purposive sampling. Participants: A total of 23 healthcare practitioners, 6 hospital informatics specialists, 4 hospital administrators and 7 vendors of the selected AI-assisted decision-making software were included in the study. Results: Within the 5 CFIR domains, 10 constructs were identified as barriers, 8 as facilitators and 3 as both barriers and facilitators. Major barriers included unsatisfactory clinical performance (Innovation); lack of collaborative network between primary and tertiary hospitals, lack of information security measures and certification (outer setting); suboptimal data quality, misalignment between software functions and goals of healthcare institutions (inner setting); unmet clinical needs (individuals). Key facilitators were strong empirical evidence of effectiveness, improved clinical efficiency (innovation); national guidelines related to AI, deployment of AI software in peer hospitals (outer setting); integration of AI software into existing hospital systems (inner setting) and involvement of clinicians (implementation process). Conclusions: The study findings contributed to the ongoing exploration of AI integration in healthcare from the perspective of China, emphasising the need for a comprehensive approach considering both innovation-specific factors and the broader organisational and contextual dynamics. As China and other developing countries continue to advance in adopting AI technologies, the derived insights could further inform healthcare practitioners, industry stakeholders and policy-makers, guiding policies and practices that promote the successful implementation of imaging-based diagnostic AI-assisted decision-making software in healthcare for optimal patient care.

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  • Perceived unsatisfactory clinical performance of AI systems is a barrier to AI adoption in diabetes management.

    “The relevant barrier under this domain is the unsatisfactory clinical performance of the AI system.”

    From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [52]

    The Results (Innovation) identify “unsatisfactory clinical performance” as a barrier and report clinicians’ concerns about accuracy, false positives, and lesion classification. The study concerns imaging-based diagnostic AI in Chinese hospitals, however; it does not establish this barrier specifically for AI adoption in diabetes management. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Limited training among healthcare workers is a barrier to AI adoption in diabetes management.

    “The principal barrier related to the inner setting is the lack of adequate training or limited knowledge and training on the AI system [52,54,55].”

    From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [52]

    The Results section, under “Access to knowledge and information,” identifies inadequate training as a barrier to clinicians’ use of AI-assisted software. The study concerns imaging-based diagnostic software in Chinese hospitals, not AI adoption in diabetes management; it also does not establish training as the principal inner-setting barrier. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Ease of integration with simple installation is a facilitator in the inner setting domain.

    “The only inner setting facilitator identified under this domain is the ease of integration, with the associated simple installation [51,52].”

    From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [52]

    In Results > Inner setting, the paper identifies integration with existing hospital systems as a facilitator, and Table 5 says PACS integration reduced workflow interruptions. It does not describe installation as simple. Table 5 also lists regular departmental communication as an inner-setting facilitator, so integration was not the only one. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Tensions between GPs and specialists with the concern of a lower referral rate is a barrier in the outer setting domain.

    “which includes the related tensions between GPs and specialists with the concern of a lower referral rate [51,52].”

    From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [52]

    The Results (Outer setting; page unavailable) identify a lack of collaboration between primary/secondary and tertiary hospitals as a barrier that delays referrals. They do not describe tensions between GPs and specialists or concern about a lower referral rate. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Ease of use is a facilitator to AI adoption in diabetes management.

    “easiness of use [52,54]”

    From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [52]

    The Results section, under “Innovation complexity (+): easiness of use,” identifies ease of use as a facilitator of implementing imaging-based diagnostic AI software in hospitals. The paper does not establish this finding specifically for AI adoption in diabetes management; its mention of diabetic retinopathy does not make that the setting of the ease-of-use finding. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Liao, X., Yao, C., Jin, F., Zhang, J., & Liu, L.
Affiliations
  • Peking University (CN)
  • MSD R&D (China) Co., Ltd, Beijing, China
  • Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
Corresponding author
Yao, Chen
ORCID
Venue
BMJ Open
Publisher
BMJ
Published
2024-09
Volume
14
Issue
9
Pages
e084398
ISSN
2044-6055 (ISSN-L)
DOI
10.1136/bmjopen-2024-084398
PMC
PMC11409362
OpenAlex
W4402464029
Cited by
50
References
15
Citations by year
2026: 24 · 2025: 26
FWCI
4.67
Type
journal article
Language
en
Open access
Licence
Locations
  • BMJ Open (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
MSD R&D (China) Co., Ltd. (N/A)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
AI implementation, lung nodule detection, diabetic retinopathy screening, barriers and facilitators, Consolidated Framework for Implementation Research, hospital informatics, clinical implementation of AI, data quality, stakeholder perspectives, China
MeSH terms
Artificial Intelligence, China, Diabetic Retinopathy, Hospitals, Humans, Interviews as Topic, Software, Qualitative Research

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