供应商时间分配跟踪工具,有效管理分配承诺

Y. Huang, Narges Shahraki, Erin M. Wallin, Eric W. Klavetter, K. Klarich
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引用次数: 0

摘要

由于卫生服务能力有限,需求不断增加,有效管理可用资源成为减少患者护理延误和避免不必要和昂贵的能力扩张的重要任务。与此同时,员工满意度和/或倦怠是设计最佳时间表时的补充考虑因素。偏离预定计划可能会导致患者访问延迟,并可能导致提供者不满意。平衡需求管理、员工满意度和生成优化的日程安排,很快就会发现需要一种工具来跟踪提供者的时间分配,特别是对于学术医疗保健组织,其中提供者致力于多种任务(临床和非临床)。这个跟踪工具应该允许管理人员主动调整分配,以适应计划外的变化,并增加参与。在这项研究中,开发了一种工具来跟踪罗切斯特梅奥诊所心血管医学科每月的提供者分配。建议的工具为每个提供者和任务产生两个关键输出:1)推荐的目标工作日和2)工作日上限和下限,以适应可变性。该跟踪工具按照实现标准成功实现,并且反馈是积极的。该工具系统地从Mayo数据平台提取数据,并对数据进行必要的分析。它还会根据时间表的变化自动更新推荐目标的值以及一年中剩余月份的上限和下限,以便在年底满足提供商的承诺。
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Provider time allotment tracking tool to effectively manage assignment commitments
Due to the rising demand with limited health service capacity, managing available resources effectively becomes an important task to reduce patient care delays and avoid unnecessary and costly capacity expansions. At the same time, staff satisfaction and/or burnout is a complementary consideration when designing optimal schedules. Deviation from the scheduled plan can cause delays in patient access and may lead to unsatisfaction among providers. Balancing demand management, staff satisfaction and generating optimized schedules quickly reveals the need for a tool that tracks provider time allotment over time, especially for the academic healthcare organization where providers are committed to multiple assignments, clinical and non-clinical. This tracking tool should allow management to proactively adjust allotment to unplanned changes in the schedule and increase participation. In this study, a tool is developed to track monthly provider assignments for the Department of Cardiovascular Medicine at Mayo Clinic, Rochester. The proposed tool produces two key outputs for each provider and assignment: 1) the recommended target workdays and 2) workday upper and lower bounds to accommodate for variability. This tracking tool is successfully implemented with implementation criteria, and the feedback is positive. The tool pulls the data systematically from the Mayo data platform and performs the necessary analysis on the data. It also automatically updates the values for the recommended target as well as upper and lower bounds for the remaining months in a year based on changes in the schedule so that provider commitment can be met at the end of year.
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