Operationalizing Machine Learning and Discrete Event Simulation Models to Improve Clinic Efficiency

运用机器学习和离散事件模拟模型来提高诊所效率

• 批准号: 10460170
• 负责人: Nicole Gray Weiskopf
• 金额: $ 32.73万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10460170
• 关键词: Affect; Ambulatory Care Facilities; Appointment; Books; Childhood; Client satisfaction; Clinic; Clinical; Data; Data Set; Effectiveness; Electronic Health Record; Event; Face; Fast Healthcare Interoperability Resources; Feeling; Glaucoma; Length; Machine Learning; Maps; Methodology; Methods; Modeling; Ophthalmology; Outpatients; Patient Care; Patient Schedules; Patients; Physicians; Production; Provider; Quality of Care; Regulation; Reporting; Research; Resources; Schedule; Testing; Time; Visit; Wait Time; Work; application programming interface; barrier to care; base; burnout; data access; effectiveness evaluation; electronic data; follow-up; improved; individual patient; machine learning model; models and simulation; novel; post implementation; predictive modeling; pressure; prospective; tool

PROJECT SUMMARY Physicians often report feeling pressured to see more patients to maintain revenue, while having less available time for patient care. Systematic data-driven methods for efficiently scheduling patients are important as physicians are pressured to see more and more patients. We propose that real time prediction models of patient visit lengths, the likelihood of missing appointments, and of patient wait times will help schedule patients more efficiently. Clinics will be able to safely overbook to avoid empty slots from missed appointments, have guidance for scheduling urgent add-on patients, and provide wait time estimates for patients when there are delays. We will develop methodologies for accessing data needed for these predictions in real time and propose that the integration of these models into workflows will improve scheduling accuracy, patient wait time, and patient satisfaction, while also increasing clinic volumes.

项目概要 医生经常报告说，他们感到压力很大，需要看更多的病人来维持收入，但可用的病人却越来越少 护理病人的时间。用于有效安排患者的系统数据驱动方法非常重要，因为 医生面临着看诊越来越多患者的压力。我们建议实时预测模型 患者就诊时长、错过预约的可能性以及患者等待时间将有助于安排 患者更有效。诊所将能够安全地超额预订，以避免因错过预约而出现空位， 为安排紧急附加患者提供指导，并在出现紧急情况时为患者提供等待时间估计 是延误。我们将开发实时访问这些预测所需数据的方法 提出将这些模型集成到工作流程中将提高调度准确性、患者等待时间、 和患者满意度，同时也增加了诊所数量。