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Background and Objectives: Clinician workload is a key contributor to burnout and well-being as well as overtime and staff shortages, particularly in the primary care setting. Appointment volume is primarily driven by the size of patient panels assigned to clinicians. Thus, finding the most appropriate panel size for each clinician is essential to optimization of patient care. Methods: One year of appointment and panel data from the Department of Family Medicine were used to model the optimal panel size. The data consisted of 82 881 patients and 105 clinicians. This optimization-based modeling approach determines the panel size that maximizes clinician capacity while distributing heterogeneous appointment types among clinician groups with respect to their panel management time (PMT), which is the percent of clinic work. Results: The differences between consecutive PMT physician groups in total annual appointment volumes per clinician for the current practice range from 176 to 348. The optimization-based approach for the same PMT physician group results in having a range from 211 to 232 appointments, a relative reduction in variability of 88%. Similar workload balance gains are also observed for advanced practice clinicians and resident groups. These results show that the proposed approach significantly improves both patient and appointment workloads distributed among clinician groups. Conclusion: Appropriate panel size has valuable implications for clinician well-being, patients' timely access to care, clinic and health system productivity, and the quality of care delivered. Results demonstrate substantial improvements with respect to balancing appointment workload across clinician types through strategic use of an optimization-based approach. [ABSTRACT FROM AUTHOR]

Resource coordination in surgical scheduling remains challenging in health care delivery systems. This is especially the case in highly-specialized settings such as coordinating Intraoperative Neurophysiologic Monitoring (IONM) resources. Inefficient coordination yields higher costs, limited access to care, and creates constraints to surgical quality and outcomes. To maximize utilization of IONM resources, optimization-based algorithms are proposed to effectively schedule IONM surgical cases and technologists and evaluate staffing needs. Data with 10 days of case volumes, their surgery durations, and technologist staffing was used to demonstrate method effectiveness. An iterative optimization-based model that determines both optimal surgery and technologist start time (operational scenario 4) was built in an Excel spreadsheet along with Excel's Solver settings. It was compared with current practice (operational scenario 1) and optimization solution on only surgery start time (operational scenario 2) or technologist start time (operational scenario 3). Comparisons are made with respect to technologist overtime and under-utilization time. The results conclude that scenario 4 significantly reduces overtime by 74% and under-utilization time by 86% as well as technologist needs by 10%. For practices that do not have flexibility to alter surgeon preference on surgery start time or IONM technologist staffing levels, both scenarios 2 and 3 also result in substantial reductions in technologist overtime and under-utilization. Moreover, IONM technologist staffing options are discussed to accommodate technologist preferences and set constraints for surgical case scheduling. All optimization-based approaches presented in this paper are able to improve utilization of IONM resources and ultimately improve the coordination and efficiency of highly-specialized resources. [ABSTRACT FROM AUTHOR]

The Transcatheter Aortic Valve Replacement (TAVR) procedure requires an initial consultation and a subsequent procedure by an interventionalist (IC) and surgeon. The IC-surgeon pair coordination is extremely challenging, especially at Mayo Clinic due to provider time commitments distributed across practice, research, and education activities. Current practice aims to establish the coordination manually, resulting in a scheduling process that is cumbersome and time consuming for the schedulers. We develop an algorithm for pairing ICs and surgeons that minimizes the lead time (days elapsed between the clinic consult and procedure). As compared to current practice, this algorithm is able to reduce average lead time by 59% and increase possible IC-surgeon pairs by 7%. The proposed algorithm is shown to be flexible enough to incorporate practice variations such as lead time upper bound and two procedure days for a single consult day. Algorithm alternatives are also presented for practices who may find the proposed algorithm infeasible for their practice. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to evaluate the performance on standardizing appointment slot length in a primary care clinic to understand the impact of providers' preferences and practice differences. Design/methodology/approach - The treatment time data were collected for each provider. There were six patient types: emergency/urgent care (ER/UC), follow-up patient (FU), new patient, office visit (OV), physical exam, and well-child care. Simulation model was developed to capture patient flow and measure patient wait time, provider idle time, cost, overtime, finish time, and the number of patients scheduled. Four scheduling scenarios were compared: scheduled all patients at 20 minutes; scheduled ER/UC, FU, OV at 20 minutes and others at 40 minutes; scheduled patient types on individual provider preference; and scheduled patient types on combined provider preference. Findings - Standardized scheduling among providers increase cost by 57 per cent, patient wait time by 83 per cent, provider idle time by five minutes per patient, overtime by 22 minutes, finish time by 30 minutes, and decrease patient access to care by approximately 11 per cent. An individualized scheduling approach could save as much as 14 per cent on cost and schedule 1.5 more patients. The combined preference method could save about 8 per cent while the number of patients scheduled remained the same. Research limitations/implications - The challenge is to actually disseminate the findings to medical providers and adjust scheduling systems accordingly. Originality/value - This paper concluded standardization of providers' clinic preference and practice negatively impact clinic service quality and access to care. [ABSTRACT FROM AUTHOR]

Patient wait time has long been a recognized problem in modern outpatient health care delivery systems. Despite all the efforts to develop appointment rules and solutions, the problem of long patient waits persists. Regardless of the reasons for this problem, the fact remains that there are few implemented models for effective scheduling that consider patient wait times as well as physician idle time and are generalized sufficiently to accommodate a variety of outpatient clinic settings. This paper presents a solution of designing appointment slots for scheduling appointments in outpatient facilities that both patient wait time and physician idle time meet the declared scheduling policies without overbooking and double-booking. Furthermore, this paper provides the implementation results from three case studies to support the approach. The results confirm that the system can effectively reduce patient wait time as much as 56% without significantly increasing physician idle time per patient and still allow physician to see and schedule exactly same number of patients per clinic session. Consequently, this research improves the outpatient experience for both patients and the medical professions, changes the perception of long waits in a physician's office and ultimately enhances the quality of care. [ABSTRACT FROM PUBLISHER]