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Cited by 33Artificial Intelligence in Healthcare and EducationArtificial Intelligence ApplicationsElectronic Health Records Systems

Perspective of Information Technology Decision Makers on Factors Influencing Adoption and Implementation of Artificial Intelligence Technologies in 40 German Hospitals: Descriptive Analysis

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Weinert, Lina, Müller, Julia, Svensson, Laura, Heinze, Oliver·2022·JMIR Medical Informatics
DOI 10.2196/34678PMC PMC9244653

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Abstract

Background: New artificial intelligence (AI) tools are being developed at a high speed. However, strategies and practical experiences surrounding the adoption and implementation of AI in health care are lacking. This is likely because of the high implementation complexity of AI, legacy IT infrastructure, and unclear business cases, thus complicating AI adoption. Research has recently started to identify the factors influencing AI readiness of organizations. Objective: This study aimed to investigate the factors influencing AI readiness as well as possible barriers to AI adoption and implementation in German hospitals. We also assessed the status quo regarding the dissemination of AI tools in hospitals. We focused on IT decision makers, a seldom studied but highly relevant group. Methods: We created a web-based survey based on recent AI readiness and implementation literature. Participants were identified through a publicly accessible database and contacted via email or invitational leaflets sent by mail, in some cases accompanied by a telephonic prenotification. The survey responses were analyzed using descriptive statistics. Results: We contacted 609 possible participants, and our database recorded 40 completed surveys. Most participants agreed or rather agreed with the statement that AI would be relevant in the future, both in Germany (37/40, 93%) and in their own hospital (36/40, 90%). Participants were asked whether their hospitals used or planned to use AI technologies. Of the 40 participants, 26 (65%) answered “yes.” Most AI technologies were used or planned for patient care, followed by biomedical research, administration, and logistics and central purchasing. The most important barriers to AI were lack of resources (staff, knowledge, and financial). Relevant possible opportunities for using AI were increase in efficiency owing to time-saving effects, competitive advantages, and increase in quality of care. Most AI tools in use or in planning have been developed with external partners. Conclusions: Few tools have been implemented in routine care, and many hospitals do not use or plan to use AI in the future. This can likely be explained by missing or unclear business cases or the need for a modern IT infrastructure to integrate AI tools in a usable manner. These shortcomings complicate decision-making and resource attribution. As most AI technologies already in use were developed in cooperation with external partners, these relationships should be fostered. IT decision makers should assess their hospitals’ readiness for AI individually with a focus on resources. Further research should continue to monitor the dissemination of AI tools and readiness factors to determine whether improvements can be made over time. This monitoring is especially important with regard to government-supported investments in AI technologies that could alleviate financial burdens. Qualitative studies with hospital IT decision makers should be conducted to further explore the reasons for slow AI.

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  • Perceived benefit, perceived usefulness, usability, ease of use, usefulness, accuracy, and reliability of the output of AI are key contributors to adoption.

    “Perceived benefit, perceived usefulness, usability, ease of use, usefulness, accuracy, and reliability of the output of the AI are key contributors to adoption [19,45,62-67].”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [45]

    The Discussion’s “AI Acceptability” section says perceived ease of use and usefulness can improve attitudes toward a technology and, in turn, its acceptability and use. It does not identify output accuracy or reliability as adoption contributors, and its qualified wording is weaker than calling every listed factor a key contributor. The indexed passages provide no page numbers. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Lack of regulation and policies from the government, including uncertainty around legal direction or law, is a barrier to the application of AI technologies.

    “A lack of regulation and policies from the government, including uncertainty around legal direction or law, was presented as a barrier to the application of AI technologies [37,45,57,60,77,78].”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [45]

    Results, Table 2 lists legal regulations as a perceived barrier to AI implementation and use; 19 of 40 respondents agreed or rather agreed. The paper does not specify that the barrier was a lack of government policies or uncertainty about the direction of the law. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Data security was identified as a major contributor toward hesitancy, with concerns around cybersecurity relating to both training and testing data as well as fear of trackers and spyware obtaining unsolicited data.

    “Data security was identified as a major contributor toward hesitancy, with concerns around cybersecurity relating to both training and testing data as well as fear of trackers and spyware obtaining unsolicited data [39,41,42,45,48,77].”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [45]

    In Results, Table 2, 22 of 40 respondents (55%) identified data protection as a barrier to AI implementation and use. The paper does not link that barrier to cybersecurity of training or testing data, or to trackers and spyware obtaining unsolicited data. No page number is available in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Investing into explainable AI that produces a transparent and understandable AI could help address the issue of acceptability.

    “Weinert et al [45] recommended that investing into explainable AI that produces a transparent and understandable AI could help address the issue of acceptability.”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [45]

  • Shortages of personnel with the required skills and the quality of IT infrastructure available for AI implementation were reported as barriers.

    “Shortages of personnel with the required skills were reported as barriers, along with the quality of IT infrastructure available for AI implementation [32,35,36,40,45,50].”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [45]

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Cites

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Research works printed but not linked: 3

  1. [24]Pumplun L Tauchert C Heidt M A new organizational chassis for artificial intelligence - exploring organizational readiness factors Proceedings of the 27th European Conference on Information Systems 2019 ECIS '19 June 8-14, 2019 Stockholm, Sweden 8 14
  2. [25]Alsheibani S Cheung Y Messom C Artificial intelligence adoption: AI-readiness at firm-level Proceedings of 2018 Pacific Asia Conference in Information Systems 2018 PACIS '18 June 26-30, 2018 Yokohama, Japan
  3. [29]Alsheiabni S Cheung Y Messom C Factors inhibiting the adoption of artificial intelligence at organizational-level: a preliminary investigation Proceedings of the 2019 Americas Conference on Information Systems 2019 AMCIS '19 August 15-17, 2019 Cancun, Mexico

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Authors
Weinert, L., Müller, J., Svensson, L., & Heinze, O.
Affiliations
University Hospital Heidelberg (DE)
Corresponding author
Weinert, Lina
ORCID
Venue
JMIR Medical Informatics
Publisher
JMIR Publications Inc.
Published
2022-06-15
Volume
10
Issue
6
Pages
e34678
ISSN
2291-9694 (ISSN-L)
DOI
10.2196/34678
PMC
PMC9244653
OpenAlex
W4226275822
Cited by
33
References
38
Citations by year
2026: 5 · 2025: 14 · 2024: 9 · 2023: 4 · 2022: 1
FWCI
2.11
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Medical Informatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Universitätsklinikum Heidelberg
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
artificial intelligence adoption, AI readiness, IT decision-makers, AI adoption barriers, resource constraints, German hospitals

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