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.
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http://dx.doi.org/10.1177/2381468320963063 | DOI Listing |
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Department of Pharmaceutics, Sinhgad College of Pharmacy, Vadgaon (Bk.), Pune-411041, Maharashtra, India.
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Sun Yat-Sen University, School of Chemistry, CHINA.
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View Article and Find Full Text PDFSci Rep
January 2025
Jiangxi Tellhow Power Technology Co., Ltd, Nanchang, 330031, China.
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January 2025
Department of Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. Electronic address:
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View Article and Find Full Text PDFNeural Netw
January 2025
Department of Mathematics, Harbin Institute of Technology, Weihai, China. Electronic address:
Nonsmooth nonconvex optimization problems are pivotal in engineering practice due to the inherent nonsmooth and nonconvex characteristics of many real-world complex systems and models. The nonsmoothness and nonconvexity of the objective and constraint functions bring great challenges to the design and convergence analysis of the optimization algorithms. This paper presents a smooth gradient approximation neural network for such optimization problems, in which a smooth approximation technique with time-varying control parameter is introduced for handling nonsmooth nonregular objective functions.
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