This analysis demonstrates improvements in clinical decision-making and workforce accommodations, suggesting AI can foster trust in healthcare settings.
Human-centered artificial intelligence (AI) is transforming how health systems and workforces approach crisis response, workforce accommodations and clinical decision-making. This study proposes a methodological framework of federated machine learning, differential privacy and transparency artifacts that aligns with NIST AI Risk Management Framework (RMF), ONC interoperability mandates and SAMHSA 988 crisis guidelines. Using simulated emergency department (ED) triage workflows and workforce accommodation datasets, the results show substantial improvements: 17% increase in triage accuracy, 22% reduction of physical restraints and increase in clinician trust in AI-assisted output. Workforce accommodation approvals improved by 19% with faster turn-around times. These outcomes highlight the importance of socio-technical design of AI to lower cognitive burden, enhance equity, and promote safe, trustworthy decision-making for the workforce and for clinical practice
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Islam et al. (2023) studied this question.
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