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Background: Patient access to care has been a known and continuing struggle for many health care providers. In spite of appointment lead time policies set by government or clinics, the problem persists. Justification for how lead time policies are determined is lacking., Objectives: This paper proposed a data-driven approach for how to best set feasible appointment target lead times given a clinic's capacity and appointment requests., Methods: The proposed approach reallocates patient visits to minimize the deviation between actual appointment lead time and a feasible target lead time. A step-by-step algorithm was presented and demonstrated for return visit (RV) and new patient (NP) types from a Pediatric clinic excluding planned visits such as well-child exam and the same day urgent appointments. The steps are: 1. Obtain appointment requests; 2. Initialize a target lead time; 3. Set up an initial schedule; 4. Check the feasibility based on appointment availability; 5. Adjust schedule backward to fill appointment slots earlier than the target; 6. Adjust schedule forward for appointments not able to be scheduled earlier or on target to the later slots; 7. Trial different target lead times until the difference between earlier and later lead time is minimized., Results: The results indicated a 59% lead time reduction for RVs and a 45% reduction for NPs. The lead time variation was reduced by 75% for both patient types. Additionally, the opportunity for the participating clinic to achieve their organization's goal of a two-week lead time for RVs and a two-day lead time for NPs is discussed by adjusting capacity to increase one slot for NP and reduce one slot for RV., Conclusions: The proposed approach and study findings may help clinics identify feasible appointment lead times.

Current outpatient delivery systems have been problematic in their ability to effectively schedule appointments and grant patients access to care. A better appointment system has demonstrated improvement on these issues. The objective of this study is to develop a grid appointment system to further improve the scheduling flexibility by determining the minimum length of appointment slots that optimizes the total costs of patient waiting, physician idling, and overtime. This minimum length is used for the patient type requiring the least amount of treatment time such as return visit (RV), and multiplications of the minimum length are for patient types with longer treatment such as new patients (NP). The results indicated that the proposed grid system adjusts to demand changes at least 15% more cost-effective when grouping two RVs into an NP or dividing an NP into two RVs compared to the base-line scheduling approaches that build around the mean treatment time.

Background and Objectives: Continuity of care is an integral aspect of high-quality patient care in primary care settings. In the Department of Family Medicine at Mayo Clinic, providers have multiple responsibilities in addition to clinical duties or panel management time (PMT). These competing time demands limit providers' clinical availability. One way to mitigate the impact on patient access and care continuity is to create provider care teams to collectively share the responsibility of meeting patients' needs. Methods: This study presents a descriptive characterization of patient care continuity based on provider types and PMT. Care continuity was measured by the percentage of patient a ppointments s een by a provider in their o wn c are t eam (ASOCT) with the aim of reducing the variability of provider care team continuity. The prediction method is iteratively developed to illustrate the importance of the individual independent components. An optimization model is then used to determine optimal provider mix in a team. Results: The ASOCT percentage in current practice among care teams ranges from 46% to 68% and the per team number of MDs varies from 1 to 5 while the number of nurse practitioners and physician assistants (NP/PAs) ranges from 0 to 6. The proposed methods result in the optimal provider assignment, which has an ASOCT percentage consistently at 62% for all care teams and 3 or 4 physicians (MDs) and NP/PAs in each care team. Conclusions: The predictive model combined with assignment optimization generates a more consistent ASOCT percentage, provider mix, and provider count for each care team. [ABSTRACT FROM AUTHOR]

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]