Raza, Haider and Rathee, Dheeraj and Amorim, Renato and Fasli, Maria (2024) Optimizing Patient Care Pathways: Impact Analysis of an AI-Assisted Smart Referral System for Musculoskeletal Services. In: IEEE International Conference on Digital Health (ICDH), 2024-07-07 - 2024-07-13, Shenzhen, China.
Raza, Haider and Rathee, Dheeraj and Amorim, Renato and Fasli, Maria (2024) Optimizing Patient Care Pathways: Impact Analysis of an AI-Assisted Smart Referral System for Musculoskeletal Services. In: IEEE International Conference on Digital Health (ICDH), 2024-07-07 - 2024-07-13, Shenzhen, China.
Raza, Haider and Rathee, Dheeraj and Amorim, Renato and Fasli, Maria (2024) Optimizing Patient Care Pathways: Impact Analysis of an AI-Assisted Smart Referral System for Musculoskeletal Services. In: IEEE International Conference on Digital Health (ICDH), 2024-07-07 - 2024-07-13, Shenzhen, China.
Abstract
The UK’s National Health Service (NHS) confronts critical challenges in patient referrals amidst rapidly growing musculoskeletal (MSK) care demands. Current systems contribute to extended waiting times, incomplete referrals, fragmented care, and access disparities. To address this, we proposed an AI-assisted Smart Referral System (SRS) that enhances accuracy, efficiency, and equity. The SRS integrates a patient web portal, AI triage for real-time recommendations, and a digital pathway for seamless data handling. The system aims to streamline, utilizing AI for data analysis and specialist recommendations, potentially reducing waits and administrative burdens. In this study, we examined data spanning from August 2022 to July 2023, covering a period of 12 months, to assess the influence of the SRS platform on service delivery, cost-effectiveness, and time efficiency. The results showed a significant reduction in missing information, coupled with substantial time and cost savings both at the administrative and clinical levels.
Item Type: | Conference or Workshop Item (Paper) |
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Uncontrolled Keywords: | AI; Healthcare; Musculoskeletal; Recommendation Engine |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 02 Oct 2024 15:37 |
Last Modified: | 30 Oct 2024 17:50 |
URI: | http://repository.essex.ac.uk/id/eprint/38779 |
Available files
Filename: IEEE_ICDH_Shenzen_SRS_Provide_CameraReady.pdf