Berg Bjorn P, Erdogan S Ayca, Lobo Jennifer Mason, Pendleton Kathryn
Division of Health Policy and Management, University of Minnesota, Minneapolis, Minnesota.
Department of Industrial and Systems Engineering, San Jose State University, San Jose, California.
MDM Policy Pract. 2020 Oct 20;5(2):2381468320963063. doi: 10.1177/2381468320963063. eCollection 2020 Jul-Dec.
Variability in outpatient specialty clinic schedules contributes to numerous adverse effects including chaotic clinic settings, provider burnout, increased patient waiting times, and inefficient use of resources. This research measures the benefit of balancing provider schedules in an outpatient specialty clinic. We developed a constrained optimization model to minimize the variability in provider schedules in an outpatient specialty clinic. Schedule variability was defined as the variance in the number of providers scheduled for clinic during each hour the clinic is open. We compared the variance in the number of providers scheduled per hour resulting from the constrained optimization schedule with the actual schedule for three reference scenarios used in practice at M Health Fairview's Clinics and Surgery Center as a case study. Compared to the actual schedules, use of constrained optimization modeling reduced the variance in the number of providers scheduled per hour by 92% (1.70-0.14), 88% (1.98-0.24), and 94% (1.98-0.12). When compared with the reference scenarios, the total, and per provider, assigned clinic hours remained the same. Use of constrained optimization modeling also reduced the maximum number of providers scheduled during each of the actual schedules for each of the reference scenarios. The constrained optimization schedules utilized 100% of the available clinic time compared to the reference scenario schedules where providers were scheduled during 87%, 92%, and 82% of the open clinic time, respectively. The scheduling model's use requires a centralized provider scheduling process in the clinic. Constrained optimization can help balance provider schedules in outpatient specialty clinics, thereby reducing the risk of negative effects associated with highly variable clinic settings.
门诊专科诊所排班的 variability 会导致诸多不良影响,包括诊所环境混乱、医护人员倦怠、患者候诊时间增加以及资源利用效率低下。本研究衡量了在门诊专科诊所平衡医护人员排班的益处。我们开发了一个约束优化模型,以最小化门诊专科诊所医护人员排班的 variability。排班 variability 被定义为诊所在营业时间内每小时安排的医护人员数量的方差。我们将约束优化排班产生的每小时安排的医护人员数量的方差与 M Health Fairview 诊所及手术中心实际应用的三个参考场景的实际排班进行了比较,作为案例研究。与实际排班相比,使用约束优化模型使每小时安排的医护人员数量的方差分别降低了92%(从1.70降至0.14)、88%(从1.98降至0.24)和94%(从1.98降至0.12)。与参考场景相比,总的以及每个医护人员分配的诊察时间保持不变。使用约束优化模型还减少了每个参考场景实际排班期间每个时段安排的医护人员的最大数量。与参考场景排班相比,约束优化排班分别在诊所开放时间的87%、92%和82%安排医护人员,而约束优化排班利用了100%的可用诊察时间。该排班模型的使用需要诊所进行集中的医护人员排班流程。约束优化有助于平衡门诊专科诊所的医护人员排班,从而降低与高度可变的诊所环境相关的负面影响的风险。