Evidence map›Paper›PMID 40996085›Full record

ArticleJournal of medical Internet research2025

Understanding the Role of Clinical Decision Support Systems Among Hospital Nurses Using the FITT (Fit Between Individuals, Tasks, and Technology) Framework: Qualitative Study.

Matthijs Berkhout, Koen Smit, Danielle Sent, Rob Kusters, Johan Versendaal, Thijs van Houwelingen

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
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  3. Observational
  4. Review
  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Matthijs BerkhoutResearch Group on Digital Ethics, Research Centre for Learning and Innovation, University of Applied Sciences Utrecht, Utrecht, The Netherlands.ORCID https://orcid.org/0000-0003-3708-3658
Koen SmitResearch Group on Digital Ethics, Research Centre for Learning and Innovation, University of Applied Sciences Utrecht, Utrecht, The Netherlands.ORCID https://orcid.org/0000-0002-1161-7941
Danielle SentResearch Group on Artificial Intelligence, University of Applied Sciences Utrecht, Utrecht, The Netherlands.ORCID https://orcid.org/0000-0002-4703-5345
Rob KustersFaculty of Science, Open University of the Netherlands, Heerlen, The Netherlands.ORCID https://orcid.org/0000-0003-4069-5655
Johan VersendaalResearch Group on Digital Ethics, Research Centre for Learning and Innovation, University of Applied Sciences Utrecht, Utrecht, The Netherlands.ORCID https://orcid.org/0000-0003-2776-9541
Thijs van HouwelingenResearch Group on Technology for Healthcare Innovations, Research Centre for Healthy and Sustainable Living, University of Applied Sciences Utrecht, Utrecht, The Netherlands.ORCID https://orcid.org/0000-0002-6173-2454

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical decision support systems (CDSSs) have gained prominence in health care, aiding professionals in decision-making and improving patient outcomes. While physicians often use CDSSs for diagnosis and treatment optimization, nurses rely on these systems for tasks such as patient monitoring, prioritization, and care planning. In nursing practice, CDSSs can assist with timely detection of clinical deterioration, support infection control, and streamline care documentation. Despite their potential, the adoption and use of CDSSs by nurses face diverse challenges. Barriers such as alarm fatigue, limited usability, lack of integration with workflows, and insufficient training continue to undermine effective implementation. In contrast to the relatively extensive body of research on CDSS use by physicians, studies focusing on nurses remain limited, leaving a gap in understanding the unique facilitators and barriers they encounter.

objectiveThis study aimed to explore the facilitators and barriers influencing the adoption and use of CDSSs by nurses in hospitals, using an extended Fit Between Individuals, Tasks, and Technology (FITT) framework.

methodsA qualitative study was conducted using semistructured interviews with 22 nurses from across the Netherlands, representing 3 hospital types: general (n=9), top-clinical (n=12), and academic (n=1). The sample included a diverse mix of practicing nurses, nurses-in-training, and clinical nurse information officers, with clinical experience ranging from 1.5 to 38 years. Interview transcripts were analyzed thematically, beginning with an inductive coding approach to identify key factors. These were then categorized deductively using the extended FITT framework. In total, 988 code instances were examined. To ensure analytical rigor, the coding process was separately conducted by 2 researchers and reviewed by an expert panel.

resultsA total of 26 distinct factors were identified, categorized into 4 FITT dimensions: technology-individual, technology-task, task-individual, and organizational context. Of these, 11 factors were facilitators (eg, cognition, clarification, and prevention), 7 were barriers (eg, alarm fatigue, poor design, and limited digital proficiency), and 8 were both facilitators and barriers depending on the context (eg, acceptance, workload, and training). In addition, key value tensions emerged, such as the balance between standardization and professional autonomy, and the trade-off between enhanced decision support and increased administrative burden.

conclusionsThe findings underscore the complexity of CDSS adoption in nursing practice, highlighting the interaction of facilitators and barriers across FITT dimensions. Practical recommendations include participatory design processes, targeted training programs, advanced alert management systems, and strong organizational support. Addressing value tensions and aligning CDSS functionality with nurses' workflows can enhance adoption and optimize patient outcomes.

Indexed as

Decision Support Systems, ClinicalNursing Staff, HospitalAdultFemaleHumansInterviews as TopicMaleNetherlandsQualitative ResearchCDSSclinical decision support systemsFITT frameworkhospital nursingnursingqualitative researchtechnology adoptionusability

Identifiers

PMID40996085
PMCPMC12511814

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.