JMIR Publications

ABSTRACT

Background:

New AI tools are being developed at a high speed. Yet, strategies and practical experiences surrounding the adoption and implementation of AI in health care are lacking. This is likely due to AI’s high implementation complexity, legacy IT infrastructure and unclear business cases, thus complicating AI adoption. Research has recently started to identify factors influencing organizations' readiness for AI.

Objective:

Our study aimed to investigate the factors influencing AI readiness as well as possible barriers to AI adoption and implementation in German hospitals. We also tried to assess the status quo concerning the dissemination of AI tools in German hospitals. We focused on IT decision makers which is a seldom studied but highly relevant group.

Methods:

We created an online survey based on recent AI readiness and implementation literature. The survey was pretested. Possible participants were identified through a publicly accessible database and contacted via e-mail or via invitational leaflets sent by mail, in some cases accompanied by a telephonic pre-notification. Survey responses were analyzed through descriptive statistical methods.

Results:

Overall, we contacted 609 possible participants and our database recorded 40 fully completed surveys. Most participants agreed or rather agreed with the statement that AI will be relevant in the future, both in Germany (37/40, 92,5%) and in their own hospital (36/40, 89,5%). Participants were asked whether their hospital used or planned using AI technologies. 65% (26/40) answered this question with “yes”. Most AI technologies were used or planned in patient care, followed by biomedical research, administration, and logistics and central purchasing. The most important barriers to AI were lacking resources (staff, knowledge, financial). Relevant possible opportunities of using AI were increases in efficiency due to time saving effects, competitive advantages, and increases in quality of care. Most AI tools in use or in planning were developed with external partners.

Conclusions:

Few tools have been implemented in routine care and many hospitals do not use or do not plan on using AI in the future. This can likely be explained by missing or unclear business cases or needed modern IT infrastructure to integrate AI tools in a usable manner, hence complicating decision making and resource attribution. Since most AI technologies already in use were developed in cooperation with external partners, these relationships should be fostered. IT decision makers in hospitals should assess their hospital’s readiness for AI individually with a focus on resources. Further research should continue to monitor the dissemination of AI tools and AI readiness factors to see if improvements can be made over time, especially in regards to governmentally supported investments in AI technologies that could alleviate financial burdens. Qualitative studies with hospital IT decision makers should be conducted to explore the reasons for slow AI adoption in more detail.