ArticleBMC health services research2022
Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities.
Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Access improvement in healthcare: a 12-step framework for operational practice.Frontiers in health services · 2024Pooled it
- Earlier medically ready for discharge date entry associated with lower unnecessary bed days.BMC health services research · 2026Article
- Patient- and Caregiver-Informed Considerations for the Design and Implementation of Generative AI-Supported Patient-Centered Clinical Decision Support: Qualitative Study.Journal of medical Internet research · 2026Article
- Climate resilience as a strategic lever for long-term healthcare sustainability: a narrative review.British journal of anaesthesia · 2026Review
- Physicians' perceptions and attitudes on artificial intelligence in hospital management: a qualitative study.BMC health services research · 2025Article
- Building and Prospectively Evaluating a Prediction Model to Forecast Urgent Dialysis Needs across Four Tertiary Hospitals.American journal of nephrology · 2025Article
- Forecasting the future of smart hospitals: findings from a real-time delphi study.BMC health services research · 2024Article
- Artificial Intelligence in Perioperative Care: Opportunities and Challenges.Anesthesiology · 2024Article
- Artificial intelligence and telemedicine in the field of anaesthesiology, intensive care and pain medicine: A European survey.European journal of anaesthesiology and intensive care · 2023Article
- Profiling of patients with type 2 diabetes based on medication adherence data.Frontiers in public health · 2023Article
- An Evaluation on Five Dimensions of a Mobile Health Application for Patient Counseling in Ambulatory Care Pharmacy: A Single-center Cross-sectional Survey Based on Pediatric Caregiver's Opinion.Journal of research in pharmacy practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) and machine learning are transforming the optimization of clinical and patient workflows in healthcare. There is a need for research to specify clinical requirements for AI-enhanced care pathway planning and scheduling systems to improve human-AI interaction in machine learning applications. The aim of this study was to assess content validity and prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system.
methodsA prospective content validity assessment was conducted in five university hospitals in three different countries using an electronic survey. The content of the survey was formed from clinical requirements, which were formulated into generic statements of required AI functionalities. The relevancy of each statement was evaluated using a content validity index. In addition, weighted ranking points were calculated to prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system.
resultsA total of 50 responses were received from clinical professionals from three European countries. An item-level content validity index ranged from 0.42 to 0.96. 45% of the generic statements were considered good. The highest ranked functionalities for an AI-enhanced care pathway planning and scheduling system were related to risk assessment, patient profiling, and resources. The highest ranked functionalities for the user interface were related to the explainability of machine learning models.
conclusionThis study provided a comprehensive list of functionalities that can be used to design future AI-enhanced solutions and evaluate the designed solutions against requirements. The relevance of statements concerning the AI functionalities were considered somewhat relevant, which might be due to the low level or organizational readiness for AI in healthcare.
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Registered trials
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.