Optimizing outpatient appointment system using machine learning algorithms and scheduling rules: A prescriptive analytics framework
作者：
Highlights：
• Developed prescriptive analytics framework to schedule patients in real-time.
• Used machine learning to classify patients based on their no-show risk.
• Proposed scheduling rules by simultaneously considering multiple design decisions.
• Results provide insights on patient sequencing and overbooking decisions.
• Helps clinicians to move towards customized, patient-centered care.
摘要
•Developed prescriptive analytics framework to schedule patients in real-time.•Used machine learning to classify patients based on their no-show risk.•Proposed scheduling rules by simultaneously considering multiple design decisions.•Results provide insights on patient sequencing and overbooking decisions.•Helps clinicians to move towards customized, patient-centered care.
论文关键词：Patient scheduling,Patient-specific no-shows,Sequential scheduling rules,Machine learning,Prescriptive analytics
论文评审过程：Received 1 November 2017, Revised 17 January 2018, Accepted 10 February 2018, Available online 15 February 2018, Version of Record 19 March 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.02.022
