Artificial Intelligence in nursing: trustworthy or reliable?

Higgins, Oliver; Chalup, Stephan K; Wilson, Rhonda L

doi:10.1177/17449871231215696

Cited by 3 publications

References 33 publications

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Using machine learning to assist decision making in the assessment of mental health patients presenting to emergency departments

Higgins,

Wilson,

Chalup

2024

DIGITAL HEALTH

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Objective The objective of this study was to assess the predictability of admissions to a MH inpatient ward using ML models, based on routine data collected during triage in EDs. This research sought to identify the most effective ML model for this purpose while considering the practical implications of model interpretability for clinical use. Methods The study utilised existing data from January 2016 to December 2021. After data pre-processing, an exploratory analysis revealed the non-linear nature of the dataset. Six different ML models were tested: Random Forest, XGBoost, CatBoost, k-Nearest Neighbours (kNN), Explainable Boosting Machine (EBM) using InterpretML, and Support Vector Machine using Support Vector Classification (SVC). The performance of these models was evaluated using various metrics including the Matthews Correlation Coefficient (MCC). Results Among the models evaluated, the CatBoost model achieved the highest MCC score of 0.1952, demonstrating superior balanced accuracy and predictive power, particularly in correctly identifying positive cases. The InterpretML model also performed well, with an MCC score of 0.1914. While CatBoost showed strong predictive capabilities, its complexity poses challenges for clinical interpretation. Conversely, the InterpretML model, though slightly less powerful, offers better transparency and is more practical for clinical use. Conclusion The findings suggest that the CatBoost model is a compelling choice for scenarios prioritising the detection of positive cases. However, the InterpretML model's ease of interpretation makes it more suitable for clinical application. Integrating explanation methods like SHAP with non-linear models could enhance model transparency and foster clinician trust. Further research is recommended to refine non-linear models within decision support systems, explore multi-source data integration, understand clinician attitudes towards ML, and develop real-time data collection systems. This study highlights the potential of ML in predicting MH admissions from ED data while stressing the importance of interpretability, ethical considerations, and ongoing validation for successful clinical implementation.

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Using machine learning to assist decision making in the assessment of mental health patients presenting to emergency departments

Higgins,

Wilson,

Chalup

2024

DIGITAL HEALTH

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Trust in AI-Based Clinical Decision Support Systems Among Healthcare Workers: A Systematic Review (Preprint)

Tun,

Rahman,

Naing

et al. 2024

Preprint

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BACKGROUND Artificial intelligence-based Clinical Decision Support Systems (AI-CDSS) have offered personalized medicine and improved healthcare efficiency to healthcare workers. Despite opportunities, trust in these tools remains a critical factor for their successful integration. Existing research lacks synthesized insights and actionable recommendations for providing healthcare workers' trust in AI-CDSS. OBJECTIVE The study aims to identify and synthesize factors for guiding in designing systems that foster healthcare worker trust in AI-CDSS. METHODS We performed a systematic review of published studies from January 2020 to November 2024 that were retrieved from PubMed, Scopus, and Google Scholar, focusing on healthcare workers’ perceptions, experiences, and trust in AI-CDSS. Two independent reviewers utilized the Cochrane Collaboration Handbook and PRISMA 2020 guidelines to develop a data charter and synthesize the study data. The CASP tool was applied to assess the quality of the studies included and evaluate the risk of bias, ensuring a rigorous and systematic review process. RESULTS The review included 27 studies that met the inclusion criteria, across diverse healthcare workers predominantly in hospitalized settings. Qualitative methods dominated (n=16,59%), with sample sizes ranging from small focus groups to over 1,000 participants. Seven key themes were identified: System Transparency, Training and Familiarity, System Usability, Clinical Reliability, Credibility and Validation, Ethical Considerations, and Customization and Control through enablers and barriers that impact healthcare workers’ trust in AI-based CDSS. CONCLUSIONS From seven thematic areas, enablers such as transparency, training, usability, and clinical reliability, while barriers include algorithmic opacity and ethical concerns. Recommendations emphasize the explainability of AI models, comprehensive training, stakeholder involvement, and human-centered design for healthcare worker trust in AI-CDSS.

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Artificial Intelligence in Nursing: Technological Benefits to Nurse’s Mental Health and Patient Care Quality

Dailah,

Koriri,

Sabei

et al. 2024

Healthcare

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Nurses are frontline caregivers who handle heavy workloads and high-stakes activities. They face several mental health issues, including stress, burnout, anxiety, and depression. The welfare of nurses and the standard of patient treatment depends on resolving this problem. Artificial intelligence is revolutionising healthcare, and its integration provides many possibilities in addressing these concerns. This review examines literature published over the past 40 years, concentrating on AI integration in nursing for mental health support, improved patient care, and ethical issues. Using databases such as PubMed and Google Scholar, a thorough search was conducted with Boolean operators, narrowing results for relevance. Critically examined were publications on artificial intelligence applications in patient care ethics, mental health, and nursing and mental health. The literature examination revealed that, by automating repetitive chores and improving workload management, artificial intelligence (AI) can relieve mental health challenges faced by nurses and improve patient care. Practical implications highlight the requirement of using rigorous implementation strategies that address ethical issues, data privacy, and human-centred decision-making. All changes must direct the integration of artificial intelligence in nursing to guarantee its sustained and significant influence on healthcare.

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Artificial Intelligence in nursing: trustworthy or reliable?

Cited by 3 publications

References 33 publications

Using machine learning to assist decision making in the assessment of mental health patients presenting to emergency departments

Using machine learning to assist decision making in the assessment of mental health patients presenting to emergency departments

Trust in AI-Based Clinical Decision Support Systems Among Healthcare Workers: A Systematic Review (Preprint)

Artificial Intelligence in Nursing: Technological Benefits to Nurse’s Mental Health and Patient Care Quality

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