Bjorn P Berg, S Ayca Erdogan, Jennifer Mason Lobo, Kathryn Pendleton
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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. <b>Results.</b> 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. <b>Limitations.</b> The scheduling model's use requires a centralized provider scheduling process in the clinic. <b>Conclusions.</b> Constrained optimization can help balance provider schedules in outpatient specialty clinics, thereby reducing the risk of negative effects associated with highly variable clinic settings.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"5 2","pages":"2381468320963063"},"PeriodicalIF":1.9000,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f2/ab/10.1177_2381468320963063.PMC7592316.pdf","citationCount":"0","resultStr":"{\"title\":\"A Method for Balancing Provider Schedules in Outpatient Specialty Clinics.\",\"authors\":\"Bjorn P Berg, S Ayca Erdogan, Jennifer Mason Lobo, Kathryn Pendleton\",\"doi\":\"10.1177/2381468320963063\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b>Background.</b> 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. 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引用次数: 0
摘要
背景。门诊专科诊所时间安排的不稳定性造成了许多不利影响,包括诊所环境混乱、医疗服务提供者倦怠、病人等待时间增加以及资源利用效率低下。本研究衡量了门诊专科诊所平衡医疗服务提供者日程安排的益处。设计。我们开发了一个约束优化模型,以最小化门诊专科诊所中医疗服务提供者日程安排的变化。排班变异性被定义为在门诊开放的每个小时内,为门诊排班的医疗服务提供者数量的变异。我们将 M Health Fairview 诊所和手术中心作为案例研究,比较了受限优化计划与实际计划中三个参考方案下每小时安排的医疗服务提供者数量的差异。结果。与实际日程安排相比,使用约束优化模型可将每小时安排的医疗服务提供者数量差异降低 92%(1.70-0.14)、88%(1.98-0.24)和 94%(1.98-0.12)。与参考方案相比,分配给每个提供者的总门诊时数和每个提供者的总门诊时数保持不变。在每个参考方案中,使用约束优化模型还减少了每个实际日程表中安排的医疗服务提供者的最大数量。与参考方案的时间表相比,约束优化时间表利用了 100%的可用门诊时间,而参考方案的时间表分别安排了 87%、92% 和 82%的开放门诊时间。局限性。该排班模型的使用需要诊所对医疗服务提供者进行集中排班。结论。约束优化有助于平衡门诊专科诊所的医疗服务提供者排班,从而降低因诊所环境高度多变而产生负面影响的风险。
A Method for Balancing Provider Schedules in Outpatient Specialty Clinics.
Background. 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. Design. 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. Results. 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. Limitations. The scheduling model's use requires a centralized provider scheduling process in the clinic. Conclusions. Constrained optimization can help balance provider schedules in outpatient specialty clinics, thereby reducing the risk of negative effects associated with highly variable clinic settings.