Technology acceptance of a machine learning algorithm predicting delirium in a clinical setting: a mixed-methods study
Open accessAbstract
Early identification of patients with life-threatening risks such as delirium is crucial in order to initiate preventive actions as quickly as possible. Despite intense research on machine learning for the prediction of clinical outcomes, the acceptance of the integration of such complex models in clinical routine remains unclear. The aim of this study was to evaluate user acceptance of an already implemented machine learning-based application predicting the risk of delirium for in-patients. We applied a mixed methods design to collect opinions and concerns from health care professionals including physicians and nurses who regularly used the application. The evaluation was framed by the Technology Acceptance Model assessing perceived ease of use, perceived usefulness, actual system use and output quality of the application. Questionnaire results from 47 nurses and physicians as well as qualitative results of four expert group meetings rated the overall usefulness of the delirium prediction positively. For healthcare professionals, the visualization and presented information was understandable, the application was easy to use and the additional information for delirium management was appreciated. The application did not increase their workload, but the actual system use was still low during the pilot study. Our study provides insights into the user acceptance of a machine learning-based application supporting delirium management in hospitals. In order to improve quality and safety in healthcare, computerized decision support should predict actionable events and be highly accepted by users.
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Participants commonly reported that XAI tools support task completion.
“Across these studies participants commonly reported that the XAI tools support task completion [23,26,28,29,31,33].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
In Results > Expert group meetings (page unavailable), the expert group said the application supported delirium-risk assessment and reduced screening time. But Results > Technology acceptance questionnaire (page unavailable) reports mixed views among the 47 surveyed users: 36.2% found it useful for their work and 31.9% did not. The paper supports reports of task support, but not the unqualified suggestion that participants commonly reported it. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIntegration with electronic health records was common, particularly in more mature tools.
“Integration with electronic health records (EHRs) was common, particularly in more mature tools [23,24,26–34,36].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
The Study design section describes one delirium-prediction application integrated into a hospital information system, and the Discussion says it uses EHR-based risk prediction. The paper does not establish that EHR integration was common across tools or particularly prevalent in more mature tools; its Conclusion describes this implementation as among the first of its kind. No page numbers are available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredParticipants in several studies described the role of the model's performance in influencing trust.
“Participants in several studies described the role of the model’s performance in influencing trust [23,25,31–33,36].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
In Results > Expert group meetings (page not provided), participants commented on the model’s predictive accuracy, saying it had “almost 100 % accuracy” and that its risk-group assignments “seem correct.” This supports the performance aspect, but the passage does not say that performance influenced their trust; a search for trust found no explicit account of that link. (LLM, conf=0.76)
Experimental — accuracy not yet measuredHealth professionals positively perceived, and were more likely to use, XAI systems that fit into their existing workflow.
“Health professionals positively perceived, and were more likely to use, XAI systems that fit into their existing workflow [23–26,30,31,33,35].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
The Results (Expert group meetings; page not provided) report positive views of the tool’s ease of use, including its availability in the hospital information system without extra data entry. But the Discussion says some users could not integrate it into their clinical routine and actual use was low. The paper does not establish that professionals whose workflows accommodated the tool were more likely to use it. (LLM, conf=0.85)
Experimental — accuracy not yet measuredA number of studies reported trust could be earned over time as model performance was proven.
“A number of studies also reported trust could be earned over time as model performance was proven [23,25,31–33,36], for example, an electrophysiologist said “I just think I should see that it confirms our decisions in enough cases - then I would feel comfortable about colleagues leaning on it” [33].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
The Conclusion (page unavailable) links the algorithm’s high accuracy with positive acceptance by physicians and nurses, and Results > Expert group meetings reports that experts perceived its accuracy as high. The paper does not report that trust was earned over time as performance was demonstrated. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Health professionals described XAI systems as useful for supporting clinical decision making.
“Health professionals also described XAI systems as useful for supporting clinical decision making [29,31,33,35–37].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
Eight studies linked perceived usefulness to the tool's impact on workload.
“Eight studies linked perceived usefulness to the tool’s impact on workload [26,29–31,33,35,37,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
Five studies did not provide sufficient detail on their qualitative data analysis process, making it difficult to determine the validity of the findings.
“Five studies did not provide sufficient detail on their qualitative data analysis process, and this made it difficult to determine the validity of the findings [31,32,34,37,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
The theme perceived usefulness was identified in 13 papers.
“The theme perceived usefulness was identified in 13 papers, where participants reported the how the systems impacted their work and associated tasks [23,24,26,28–33,35–38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
While several systems had been implemented or were near live, the majority remained at prototype or pre-implementation stages.
“While several systems had been implemented or were near live [26,30,31,33,36], the majority remained at prototype or pre-implementation stages.”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
Perceived usefulness of XAI systems was reported to be user or context dependent.
“Perceived usefulness was reported to be user or context dependent [24,31–33,36,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
Impact on workflow arose as a theme in 9 papers.
“Impact in workflow related to how XAI systems aligned with and influenced work processes, and arose as a theme in 9 papers [23–27,30,31,33,35].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
Perceptions on the model's performance were reported in 13 studies.
“Perceptions on the model’s performance was reported in 13 studies [23–26,29–37].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
Health professionals' perceptions involving design and usability were found in 14 papers.
“Health professionals’ perceptions involving design and usability were found in 14 papers [23–34,37,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
Colour coding was viewed as a helpful visual cue for conveying risk severity.
“Colour coding was viewed as a helpful visual cue for conveying risk severity.”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [31]
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- Authors
- Jauk, S., Kramer, D., Avian, A., Berghold, A., Leodolter, W., & Schulz, S.
- Affiliations
- Medical University of Graz (AT)
- Corresponding author
- Jauk, Stefanie
- Venue
- Journal of Medical Systems
- Published
- 2021-03-01
- Volume
- 45
- Issue
- 4
- Pages
- 48-48
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- PMC
- PMC7921052
- OpenAlex
- W3135561137
- Cited by
- 59
- References
- 32
- Citations by year
- 2026: 12 · 2025: 14 · 2024: 15 · 2023: 10 · 2022: 7 · 2021: 1
- FWCI
- 7.19
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-03-10)
- Open access
- hybrid
- doi.org
- Locations
- Journal of Medical Systems (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Medical University of Graz
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- delirium prediction, machine learning, technology acceptance, technology acceptance model, clinical decision support, hospitalized patients, healthcare workers, perceived usefulness, perceived ease of use, delirium management, inpatient care, user acceptance
- MeSH terms
- Clinical Decision-Making, Machine Learning, Algorithms, Australia, Delirium, Diagnosis, Differential, Diagnostic Errors, Female, Humans, Male, Middle Aged, Pilot Projects, Psychiatric Status Rating Scales, Electronic Health Records
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