e-Health Strategy for Surgical Prioritization: A Methodology Based on Digital Twins and Reinforcement Learning DOI Creative Commons
Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan

и другие.

Bioengineering, Год журнала: 2025, Номер 12(6), С. 605 - 605

Опубликована: Июнь 2, 2025

This article presents a methodological framework for elective surgery scheduling based on the integration of patient-specific Digital Twins (DTs) and reinforcement learning (RL). The proposed approach aims to support future development an intelligent e-health platform dynamic, data-driven prioritization surgical patients. We generate scores by modeling clinical, economic, behavioral, social variables in real time optimize access through engine designed maximize long-term system performance. methodology is as modular, transparent, interoperable digital decision-support architecture aligned with goals organizational transformation equitable healthcare delivery. To validate its potential, we simulate realistic scenarios using synthetic patient data. Results demonstrate substantial improvements compared withto traditional strategies, including 55.1% reduction average wait time, 41.9% decrease clinical risk at surgery, 16.1% increase OR utilization, significant socially vulnerable These findings highlight value foundation smart platforms that adaptive, ethically decision-making scheduling.

Язык: Английский

e-Health Strategy for Surgical Prioritization: A Methodology Based on Digital Twins and Reinforcement Learning DOI Creative Commons
Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan

и другие.

Bioengineering, Год журнала: 2025, Номер 12(6), С. 605 - 605

Опубликована: Июнь 2, 2025

This article presents a methodological framework for elective surgery scheduling based on the integration of patient-specific Digital Twins (DTs) and reinforcement learning (RL). The proposed approach aims to support future development an intelligent e-health platform dynamic, data-driven prioritization surgical patients. We generate scores by modeling clinical, economic, behavioral, social variables in real time optimize access through engine designed maximize long-term system performance. methodology is as modular, transparent, interoperable digital decision-support architecture aligned with goals organizational transformation equitable healthcare delivery. To validate its potential, we simulate realistic scenarios using synthetic patient data. Results demonstrate substantial improvements compared withto traditional strategies, including 55.1% reduction average wait time, 41.9% decrease clinical risk at surgery, 16.1% increase OR utilization, significant socially vulnerable These findings highlight value foundation smart platforms that adaptive, ethically decision-making scheduling.

Язык: Английский

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