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AI-Driven Integration and Workflow Optimization in Modern Healthcare Facilities
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Author(s): S. Usharani (Artificial Intelligence and Machine Learning, IFET College of Engineering, India), Manju Bala P. (IFET College of Engineering, India), A. Devi (IFET College of Engineering, India), K. Rajkumar (SRM Institute of Science and Technology, India)and D. Saravanan (Sathyabama Institute of Science and Technology, India)
Copyright: 2026
Pages: 44
Source title:
Evaluation and Assessment of AI-Driven Systems in Hospitals
Source Author(s)/Editor(s): Anandavalli M. (King Khalid University, Saudi Arabia), Prabhu Chakkaravarthy (SRM Institute of Science and Technology, India)and Dhanalakshmi J. (SRM Institute of Science and Technology, India)
DOI: 10.4018/979-8-3373-2787-7.ch009
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Abstract
This chapter explores AI services for administrative and clinical workflows, emphasizing measurable gains in patient experience, efficiency, and diagnostic accuracy. The project applied predictive analytics for bed occupancy and inventory, NLP for clinical documentation, AI for medical imaging, and automation for routine tasks. A structured framework guided data collection, model building, process mapping, deployment, and feedback. Cybersecurity, interoperability, and ethics ensured responsible use. Case studies showed X-ray accuracy improved from 88.5% to 94.2%, pneumonia sensitivity from 86.1% to 91.8%, and specificity from 89.4% to 92.6%. NLP entity extraction F1 scores rose from 0.83 to 0.89, and AUC-ROC from 0.91 to 0.96. Patient wait times dropped 42% (48→28 mins), no-shows fell 60% (15 to 6%), and admin task time declined 40% (35 to 21 mins). Inventory refill shrank 38% (9 to 5.5 hrs), and ICU bed forecasts had a 2.1 unit MAE. These results confirm that AI, applied through ethical frameworks, drives measurable hospital improvements.
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