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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.
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.
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.
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.
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.
In recent years, artificial intelligence (AI) in medicine has gained significant attention, with innovative technologies promising better quality of diagnosis [
A recent systematic review by Yin et al [
The transfer of new and innovative technologies into practice is usually associated with barriers and requires employees’ and institutions’ ability to adapt to change [
Although new AI technologies are being developed at a high speed, strategies and practical experiences surrounding the adoption and implementation of AI in health care are lacking [
This study presents the first large-scale web-based survey on the current adoption and implementation of AI technologies in randomly selected German hospitals. We further aimed to gain insights into the number, type, and developmental stage of the AI technologies currently in use. In addition to the literature on AI readiness and adoption, we examined the applicability of existing AI readiness factors to the German health care sector.
A quantitative study design was used to obtain a general overview of the situation in Germany. Data were collected using an anonymous web-based questionnaire. We invited chief information officers (CIOs) from randomly selected German hospitals. We identified CIOs as important intermediaries because their position is linked to the clinical implementation of AI as well as to developers, technology companies, and regulatory authorities. Anonymity was ensured throughout the study.
The study was approved by the Ethics Committee of Heidelberg University Hospital (S-490/2020). The study was conducted according to the Checklist for Reporting Results of Internet E-Surveys checklist for quantitative research [
After consulting existing literature on AI readiness, implementation, and adoption, the authors conducted a creative brainstorming process to develop preliminary survey items. The preliminary items were compared with existing theoretical frameworks.
Jöhnk et al [
The technological-organizational-environmental framework by DePietro et al [
On the basis of these theoretical considerations, LW, JM, and LS refined the survey design and wording of the questions. In the first section, the questionnaire focused on participants’ general professional opinions on AI in hospitals to assess the hospital’s strategic alignment and their stance in the AI adoption phase. The second section asked participants to state their hospital’s use of AI technologies, which helped us gain insight into the dissemination of AI technologies. In the following sections, the survey presents items on known perceived barriers, opportunities, and resources needed for the implementation of AI in hospitals. In addition to these questions, the questionnaire also asked for sociodemographic data of the participants, hospital size, and hospital ownership (private, public, or nonprofit). A translated English version of the survey can be found in
The survey was pretested by 6 researchers from the field of medical informatics, using a cognitive pretesting method [
The final survey did not include any randomized or alternated items. Adaptive questioning was used to reduce the length of the questionnaires. On average, the 10-page questionnaire contained 6.3 items per page. Possible answers were either presented on a 5-point Likert scale or as
From a publicly available database of all hospitals in Germany provided by the German Hospital Federation [
Although all 4 rounds followed the same administrative process, we used additional measures in recruitment rounds 3 and 4 to increase the number of participants. In round 3, we used telephonic prenotifications when an office telephone number was publicly available. In round 4 of recruitment, we designed invitational leaflets that were sent via mail. The leaflets encompassed a short informational text and a QR code, leading to the open survey. For each round, we sent 2 reminders via email. Our survey was not advertised elsewhere, as we wanted to include only members of our specific target group in the sample. No incentives were offered to the study participants.
Data were collected from October 2020 to February 2021. After completion, all data were exported from REDCap to SPSS statistical software (version 27, IBM). All data were checked for plausibility and analyzed by LW. Descriptive analyses were conducted. For open-item responses, recurring keywords and phrases were paraphrased and summarized.
Our database recorded 50 surveys, of which 10 were terminated early, usually in the first third of the survey. A total of 40 surveys were fully completed and were included in the analysis, resulting in a response rate of 6.6%. Timeframes were analyzed, but no unusual timeframes were observed. No statistical corrections were performed.
A total of 40 fully completed surveys were included in the analysis.
Participant characteristics (N=40).
Characteristics | Participants | |
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Female | 5 (13) |
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Male | 33 (83) |
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Prefer not to say | 2 (5) |
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26 to 35 | 2 (5) |
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35 to 45 | 8 (20) |
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46 to 55 | 23 (58) |
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56 to 65 | 5 (13) |
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>65 | 2 (5) |
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Public | 30 (75) |
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Nonprofit | 8 (20) |
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Private | 2 (5) |
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Academic | 15 (38) |
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Nonacademic | 25 (63) |
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1 to 199 | 3 (8) |
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200 to 399 | 5 (13) |
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400 to 599 | 7 (18) |
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600 to 799 | 4 (10) |
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>800 | 21 (52) |
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Chief information officer or head of IT | 26 (65) |
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Chief data officer | 1 (3) |
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Chief medical officer | 1 (3) |
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IT department employee | 7 (18) |
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Research associate | 4 (10) |
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Data scientist | 3 (8) |
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No answer | 1 (3) |
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Other | 3 (8) |
aSelection of multiple items possible.
Most participants were either undecided or said they rather disagreed with the statement that AI is relevant for the current health care provision in their hospital and in Germany. However, most participants agreed or rather agreed that AI would be relevant in the future, both in Germany (37/40, 93%) and in their own hospital (36/40, 90%). This fits well with most participants fully agreeing or rather agreeing that AI plays a role in their hospital’s strategy (22/40, 55%). On the topic of information about the possible application of AI in hospitals, the participants were more undecided. In all, 13% (5/40) of the participants fully agreed with the statement that they were well informed, and 38% (15/40) of the participants rather agreed that they were well informed. A total of 38% (15/40) of the respondents were undecided, and 13% (5/40) of the respondents said they were rather uninformed. Overall, the participants were rather optimistic about the use of AI technologies in their hospitals. Of the 40 participants, 14 (35%) rather agreed that their hospital was ready for AI, 14 (35%) were undecided, 7 (18%) said they were rather not ready, and only 4 (10%) stated that their hospital was not ready at all. One participant did not respond to this question.
The next section of the questionnaire focused on AI tools and technologies. In the first subcategory, participants were asked whether their hospital used or planned to use AI technologies. Of the 40 participants, 26 (65%) answered “yes.” Through the following questions, participants were asked to describe these technologies in more detail. Most AI technologies were used or planned for patient care, followed by biomedical research, administration, and logistics and central purchasing. Other areas mentioned by the participants in free text were marketing, malware detection, and pathology. Participants were presented with a list of common AI technologies when they answered “yes” to the first question in this subcategory (
Sensorics and communication systems were the least picked (10/26, 38%). Most technologies were in the planning phase.
Concerning the integration of these technologies into the overarching system architecture, 27% (7/26) of the participants stated that technologies in their hospital were integrated, in 23% (6/26) of hospitals, technologies were not integrated but integration was planned, 38% (10/26) were partly integrated, and 12% (3/26) were not integrated. In free text, participants provided reasons for the lack of integration, which included missing interfaces; missing standards for interfaces, processes, and organization; unfavorable cost-benefit relationship; missing evaluation and overall concepts; and immaturity of the AI technology.
In a question allowing for multiple choice, participants stated that some or all AI technologies in their institution were commonly developed with industry partners (23/26, 88%) or university-based research partners (9/26, 35%). Only 12% (3/26) of the participants stated that some or all of their AI technologies were developed within their own institutions.
The second subcategory included questions about perceived barriers to the use of AI (
Perceived barriers to implementation and use of artificial intelligence (N=40).
Ranking | Barrier | Total participants in agreement and sample percentages, n (%)a |
1 | Lacking resources (staff, knowledge, and financial) | 36 (90) |
2 | Lacking compatibility or interoperability with existing IT infrastructure | 33 (83) |
3 | Quality of data | 30 (75) |
4 | Availability of data | 26 (65) |
5 | Ethical aspects (eg, liability issues) | 24 (60) |
6 | Product range on the market | 23 (58) |
7 | Data protection | 22 (55) |
7 | Quantity of data | 22 (55) |
8 | Legal regulations | 19 (48) |
9 | Consent of the work council | 15 (38) |
10 | Corporate culture | 13 (33) |
11 | User (eg, physicians, nurses, and administration) acceptance | 9 (23) |
12 | Leadership acceptance | 4 (10) |
12 | Patient acceptance | 4 (10) |
aResponses of “agree” or “rather agree” were grouped together.
In the third subcategory, participants were asked about positive prospects possibly associated with AI (
A detailed presentation and graphs presenting the results of these 2 subcategories can be found in
Perceived opportunities associated with the implementation and use of artificial intelligence (N=40).
Ranking | Opportunity | Total participants in agreement and sample percentages, n (%)a |
1 | Increase in efficiency due to time-saving effects | 29 (73) |
2 | Competitive advantage | 27 (69) |
3 | Increase in quality of care | 25 (66) |
4 | Easing the workload of employees | 21 (53) |
5 | Financial savings | 16 (40) |
aResponses of “agree” or “rather agree” were grouped together.
For the fourth subcategory, we focused on the resources required for the use of AI technologies in hospitals. Again, the participants were presented with a list of known critical resources for AI implementation, and they had to indicate their level of agreement with these findings from literature (
Resources needed for use and implementation of artificial intelligence (N=40).
Ranking | Resource | Total participants in agreement and sample percentages, n (%)a |
1 | Staffing resources | 35 (90) |
2 | Time | 34 (87) |
3 | Knowledge | 33 (85) |
4 | Financial resources | 32 (84) |
5 | Technical resources | 31 (79) |
6 | Data base | 27 (69) |
7 | Organizational frameworks | 25 (64) |
aResponses of “agree” or “rather agree” were grouped together.
The next item asked participants whether their hospital needed to fulfill any further requirements or resources besides those already mentioned in a yes or no format. A total of 60% (24/40) of the participants answered “yes” and provided explanations in free text. Here, organizational aspects were most common (eg, competencies and responsibilities), followed by workflow and legal issues. Technical aspects were described in detail, such as lacking hardware and software, interoperability, difficulties with data transfer from old to new systems, need for additional modules for data capture, and Wi-Fi availability and speed.
Considering the tech industry and its offerings on the market, the participants were highly undecided. Furthermore, 58% (23/40) of the participants said that they did not know if the supply met the demand for AI technologies in their hospital. Only 7% (3/40) of the participants stated that offerings on the market were sufficient.
This study provided insights into the current and planned dissemination of AI tools as well as perceived barriers and opportunities for the implementation and adoption of AI tools in 40 hospitals in Germany. We designed a web-based survey based on existing literature on the implementation of AI in hospitals. Our participants were mainly from an IT background, with 28 decision makers in leadership positions. Two-thirds of the participants said that they used or planned to use AI tools in their institution. Speech recognition and text analysis systems, systems for picture recognition, and robotics and autonomous systems were the tools or systems most commonly used, or their use was planned. We did not find differing opinions among hospitals of different sizes or ownership. The results showed that most participants recognized the implementation of AI in hospitals as a relevant, forthcoming part of their IT strategy. However, lack of resources and compatibility or interoperability with the existing IT infrastructure were identified as barriers to implementation. Staffing resources, time, knowledge, financial resources, and technical resources required for the implementation of AI were all highly relevant resources. A possible increase in efficiency because of time-saving effects, competitive advantage, and increase in quality of care was seen as the most important opportunity associated with AI use. We conclude that AI readiness factors derived from the literature are applicable to the hospital context in Germany. The following discussion highlights the most relevant barriers to AI readiness, adoption, and implementation while also presenting possible ways to overcome these barriers.
AI readiness as a concept has been described recently [
Although there are both expectations and observations of AI as a possible tool to save cost and generate high revenue [
With regard to further barriers to the implementation of AI,
Leadership acceptance and support have been identified as important antecedents for AI implementation [
Finally, the issue of AI acceptability can be addressed by investing in the concept of
Another finding in our study was that only 7% (3/40) of the participants said that the supply of applicable AI solutions to the tech market was sufficient for their needs. Another 58% (23/40) of participants reported that they were unsure. One reason could be that we did not reach the right people in the institution, and they were thus unable to assess the tech market. Another possibility could be that our participants did not spend time researching the offerings in the tech market. This could be especially true for those who are not using or planning to use AI tools. However, it could also be possible that the offerings on the market do not fit the requirements of their potential clients. This result could be of value for tech companies trying to reach decision makers in hospitals. This finding is especially important considering that only 12% of the AI tools were developed within the hospitals in our survey. Hence, partnerships for the development of AI tools are common and must be fostered.
We created this survey instrument based on an extensive literature research and theoretical frameworks and used cognitive pretesting to ensure understandability. Participants usually completed the survey in <10 minutes. Hence, our survey instrument enabled us to collect data both efficiently and in a theoretically informed manner. This survey could serve as a template for other studies, especially in countries with a similar level of dissemination of AI technologies. Country-specific items, such as the work council, should be adapted to the context in question. Although our survey included these country-specific aspects, they did not appear to be of high relevance in our sample. However, we think that these aspects should be surveyed, as their importance in other contexts is not predictable.
This study investigated the status quo of AI technologies in 40 German hospitals and the applicability of AI readiness factors derived from the literature. Owing to the low response rate and resulting small sample size, our results are not representative but describe a first impression. We surveyed hospital CIOs, a group we identified as important intermediaries for digital innovation adoption and implementation. While other studies about the perceptions, barriers, and issues surrounding AI questioned users (eg, physicians and health professionals), patients, or other stakeholders [
We analyzed the differences in opinions of hospitals differing in size and ownership, which did not produce relevant results. This finding should be interpreted cautiously, as our sample size could be too small to produce significant results.
Owing to technical limitations, we were unable to report the number of unique site visitors. This impedes the calculation of correct survey response rates. Although we used various recruitment methods (emails, letters, and telephone calls) over a prolonged period, our sample size remained small compared with the number of hospitals in Germany (1914 hospitals in 2019 [
Considering the demographics of the survey respondents, the sample was very homogeneous, as most participants were middle-aged and male. This distribution was expected and represents the composition of IT departments in Germany [
As AI is a new and complex technology, it is possible that our participants misunderstood some questions or falsely claimed that they had used AI in their hospital. We managed this risk by closely aligning our survey design with the results from the 6 pretests. Pretest participants suggested not to include a general definition of AI but to give examples for the specific tools in question 2 (“Please assess the current stage of implementation of these AI tools in your hospital”). To keep the survey as short as possible and by keeping in mind that our target group consisted of experts in a related field, we followed this suggestion. However, this risk must be considered when comparing our results.
This study paints a mixed picture of the status quo of AI in German hospitals. In our sample, 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, which complicates decision-making and resource attribution. We also observed a mismatch or lack of information about AI offerings in the tech market. This is another important aspect to be monitored, as most AI technologies that are already in use were developed in cooperation with external partners. Therefore, these relationships should be fostered. IT decision makers in hospitals 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 AI readiness factors to determine whether improvements can be made over time, especially with regard to government-supported investments in AI technologies that could alleviate the financial burden. Qualitative studies with hospital IT decision makers should be conducted to explore the reasons for slow AI adoption in more detail. The results of our study may infer that AI adoption is not only a topic solely for the IT department but also for the whole hospital as an enterprise, including management, medical staff, and business in terms of an important building block of the digital transformation.
Translated survey.
Bar graphs.
artificial intelligence
chief information officer
Research Electronic Data Capture
The authors would like to thank all study participants for their contributions to this study. The authors would also like to thank Carolin Anders (Heidelberg University Hospital, Institute of Medical Informatics) for her support. This study was funded by the Baden-Wuerttemberg (Germany) Ministry of Science, Research and the Arts, under reference number 42-04HV.MED(19)/15/1 as part of the project ZIV (
LW drafted and prepared the original manuscript. OH was the principal investigator of the study. LW and JM were responsible for study design and protocol. All authors contributed to the concept and design of the study and preparation of the manuscript. LW, JM, and LS constructed and tested the survey design and the quantitative data collection tool. LW analyzed survey data. LW interpreted and phrased the results of the quantitative data. All the authors provided substantial comments and approved the final version of the manuscript.
None declared.