ArticleJMIR human factors2023
Intensive Care Unit Physicians' Perspectives on Artificial Intelligence-Based Clinical Decision Support Tools: Preimplementation Survey Study.
Article in JMIR human factors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning models for predicting postoperative delirium in non-cardiac surgery patients - systematic review and meta-analysis.GeroScience · 2026Pooled it
- Digital-Use/Efficiency Phenotypes Versus Professional Role in Explaining Perceived Digital Benefit in Anaesthesia and Intensive Care.Healthcare (Basel, Switzerland) · 2026Article
- The landscape of artificial intelligence-enabled medical devices in the EU and the US intended for intensive care units.NPJ digital medicine · 2026Article
- The regulation of artificial intelligence in intensive care units: from narrow tools to generalist systems.NPJ digital medicine · 2026Review
- Review
- Improving AI-Based Clinical Decision Support Systems and Their Integration Into Care From the Perspective of Experts: Interview Study Among Different Stakeholders.JMIR medical informatics · 2025Article
- Expectations of Intensive Care Physicians Regarding an AI-Based Decision Support System for Weaning From Continuous Renal Replacement Therapy: Predevelopment Survey Study.JMIR medical informatics · 2025Article
- Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review.Nursing reports (Pavia, Italy) · 2025Review
- An AI-Based Clinical Decision Support System for Antibiotic Therapy in Sepsis (KINBIOTICS): Use Case Analysis.JMIR human factors · 2025Article
- Exploring Stakeholder Perceptions about Using Artificial Intelligence for the Diagnosis of Rare and Atypical Infections.Applied clinical informatics · 2025Article
- Preparing hospitals and health organizations for AI: practical guidelines for the required infrastructure.Frontiers in digital health · 2025Article
- Cautiously optimistic: paediatric critical care nurses' perspectives on data-driven algorithms in low-resource settings-a human-centred design study in Malawi.BMC global and public health · 2024Article
- Bridging the Gap: Challenges and Strategies for the Implementation of Artificial Intelligence-based Clinical Decision Support Systems in Clinical Practice.Yearbook of medical informatics · 2024Review
- Human Factors and Organizational Issues in Health Informatics: Review of Recent Developments and Advances.Yearbook of medical informatics · 2024Review
- Building a house without foundations? A 24-country qualitative interview study on artificial intelligence in intensive care medicine.BMJ health & care informatics · 2024Article
- Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians.BMC health services research · 2023Article
- Current challenges in adopting machine learning to critical care and emergency medicine.Clinical and experimental emergency medicine · 2023Article
- AI support in prehospital telemedicine: Perspectives of tele-emergency physicians and ethical considerations.Digital healthArticle
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence-based clinical decision support (AI-CDS) tools have great potential to benefit intensive care unit (ICU) patients and physicians. There is a gap between the development and implementation of these tools.
objectiveWe aimed to investigate physicians' perspectives and their current decision-making behavior before implementing a discharge AI-CDS tool for predicting readmission and mortality risk after ICU discharge.
methodsWe conducted a survey of physicians involved in decision-making on discharge of patients at two Dutch academic ICUs between July and November 2021. Questions were divided into four domains: (1) physicians' current decision-making behavior with respect to discharging ICU patients, (2) perspectives on the use of AI-CDS tools in general, (3) willingness to incorporate a discharge AI-CDS tool into daily clinical practice, and (4) preferences for using a discharge AI-CDS tool in daily workflows.
resultsMost of the 64 respondents (of 93 contacted, 69%) were familiar with AI (62/64, 97%) and had positive expectations of AI, with 55 of 64 (86%) believing that AI could support them in their work as a physician. The respondents disagreed on whether the decision to discharge a patient was complex (23/64, 36% agreed and 22/64, 34% disagreed); nonetheless, most (59/64, 92%) agreed that a discharge AI-CDS tool could be of value. Significant differences were observed between physicians from the 2 academic sites, which may be related to different levels of involvement in the development of the discharge AI-CDS tool.
conclusionsICU physicians showed a favorable attitude toward the integration of AI-CDS tools into the ICU setting in general, and in particular toward a tool to predict a patient's risk of readmission and mortality within 7 days after discharge. The findings of this questionnaire will be used to improve the implementation process and training of end users.
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