Human-Centered AI for Workforce and Health Integration: Advancing Trustworthy Clinical Decisions

Authors

  • Md Maruful Islam
  • Md Ariful Haque Arif
  • Abdullah Hill Hussain
  • S M Shah Raihena
  • Munadil Rashaq
  • Qazi Rubyya Mariam

DOI:

https://doi.org/10.63682/jns.v14i32S.9123

Keywords:

Human-centered AI, federated learning, workforce accommodations, crisis triage, clinical decision-making, NIST AI RMF, SAMHSA 988

Abstract

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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References

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Published

2023-08-10

How to Cite

1.
Islam MM, Arif MAH, Hussain AH, Raihena SMS, Rashaq M, Mariam QR. Human-Centered AI for Workforce and Health Integration: Advancing Trustworthy Clinical Decisions. J Neonatal Surg [Internet]. 2023 Aug. 10 [cited 2026 Feb. 23];12(1):89-95. Available from: https://jneonatalsurg.com/index.php/jns/article/view/9123

Issue

Vol. 12 (2023): Journal of Neonatal Surgery

Section

Original Article

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