Modeling Family Medicine Provider Care Team Design to Improve Patient Care Continuity.

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.

Balancing Clinician Workload Through Strategic Patient Panel Designs.

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.

Coordination of Intraoperative Neurophysiologic Monitoring Technologist and Surgery Schedules.

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.

Mitigating Nonattendance Using Clinic-Resourced Incentives Can Be Mutually Beneficial: A Contingency Management-Inspired Partially Observable Markov Decision Process Model.

OBJECTIVES: Nonattendance of appointments in outpatient clinics results in many adverse effects including inefficient use of valuable resources, wasted capacity, increased delays, and gaps in patient care. This research presents a modeling framework for designing positive incentives aimed at decreasing patient nonattendance. METHODS: We develop a partially observable Markov decision process (POMDP) model to identify optimal adaptive reinforcement schedules with which financial incentives are disbursed. The POMDP model is conceptually motivated based on contingency management evidence and practices. We compare the expected net profit and trade-offs for a clinic using data from the literature for a base case and the optimal positive incentive design resulting from the POMDP model. To accommodate a less technical audience, we summarize guidelines for reinforcement schedules from a simplified Markov decision process model. RESULTS: The results of the POMDP model show that a clinic can increase its net profit per recurrent patient while simultaneously increasing patient attendance. An increase in net profit of 6.10% was observed compared with a policy with no positive incentive implemented. Underlying this net profit increase is a favorable trade-off for a clinic in investing in a targeted contingency management-based positive incentive structure and an increase in patient attendance rates. CONCLUSIONS: Through a strategic positive incentive design, the POMDP model results show that principles from contingency management can support decreasing nonattendance rates and improving outpatient clinic efficiency of its appointment capacity, and improved clinic efficiency can offset the costs of contingency management.

Return to work after deceased donor kidney transplant under the kidney allocation system.

BACKGROUND: The Kidney Allocation System (KAS) includes a scoring system to match transplant candidate life expectancy with expected longevity of the donor kidney, and a backdating policy that gives waitlist time credit to patients waitlisted after starting dialysis treatment (post-dialysis). We estimated the effect of the KAS on employment among patient subgroups targeted by the policy. METHODS: We used a sample selection model to compare employment after transplant before and after KAS implementation among patients on the kidney-only transplant waitlist between December 4, 2011 and December 31, 2017. RESULTS: Post-dialysis transplant recipients aged 18-49 were significantly more likely to be employed 1-year post transplant in the post-KAS era compared to the pre-KAS era. Transplant recipients aged 35-64 with no dialysis treatment were significantly less likely to be employed 1 year after transplant in the post-KAS era compared to the pre-KAS era. CONCLUSIONS: This study provides the first assessment of employment after DDKT under the KAS and provides important information about both the methods used to measure employment after transplant and the outcome under the KAS. Changes in employment after DDKT among various patient subgroups have important implications for assessing long-term patient and societal effects of the KAS and organ allocation policy.

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.

Provider Scheduling to Maximize Patient Access.

INTRODUCTION: We describe and demonstrate an efficient method for assigning clinic days to urology providers in academic and large urology group practices given their numerous scheduling constraints including evaluation and management visits, office or operating room procedures/surgeries, teaching, trainee mentorship, committee work and outreach activities. METHODS: We propose an integer programming model for scheduling providers for clinic shifts in order to maximize patient access to appointments considering the aforementioned scheduling constraints. We present results for a case study with an academic urology clinic and lessons learned from implementing the model generated schedule. RESULTS: The integer programming model produced a feasible schedule that was implemented after pairwise and 3-way switches among attending providers to account for preferences. The optimized schedule had reduced variability in the number of providers scheduled per shift (standard deviation 1.409 vs 0.999, p=0.01). While other confounding factors are possible we noted a significant increase in the number of encounters after implementing changes from the model (1,370 vs 1,196 encounters, p=0.011). CONCLUSIONS: Optimization models offer an efficient and transferable method of generating a clinic template for providers that takes into account other clinical and academic responsibilities, and can increase the number of appointments for patients. Optimization of schedules may be performed periodically to address changes in providers or provider constraints.

Estimating the cost of no-shows and evaluating the effects of mitigation strategies.

OBJECTIVE: To measure the cost of nonattendance ("no-shows") and benefit of overbooking and interventions to reduce no-shows for an outpatient endoscopy suite. METHODS: We used a discrete-event simulation model to determine improved overbooking scheduling policies and examine the effect of no-shows on procedure utilization and expected net gain, defined as the difference in expected revenue based on Centers for Medicare & Medicaid Services reimbursement rates and variable costs based on the sum of patient waiting time and provider and staff overtime. No-show rates were estimated from historical attendance (18% on average, with a sensitivity range of 12%-24%). We then evaluated the effectiveness of scheduling additional patients and the effect of no-show reduction interventions on the expected net gain. RESULTS: The base schedule booked 24 patients per day. The daily expected net gain with perfect attendance is $4433.32. The daily loss attributed to the base case no-show rate of 18% is $725.42 (16.4% of net gain), ranging from $472.14 to $1019.29 (10.7%-23.0% of net gain). Implementing no-show interventions reduced net loss by $166.61 to $463.09 (3.8%-10.5% of net gain). The overbooking policy of 9 additional patients per day resulted in no loss in expected net gain when compared with the reference scenario. CONCLUSIONS: No-shows can significantly decrease the expected net gain of outpatient procedure centers. Overbooking can help mitigate the impact of no-shows on a suite's expected net gain and has a lower expected cost of implementation to the provider than intervention strategies.

Effect of weekend hospital admission on gastrointestinal hemorrhage outcomes.

OBJECTIVE: To determine whether outcomes for patients admitted with UGIH differ depending on weekend versus weekday admission, and whether any such differences are mediated by discrepancies in the use and timing of endoscopy. METHODS: This was a cross-sectional comparison of mortality, resource use, and the utilization and timing of esophagogastroduodenoscopy (EGD) among patients admitted with upper gastrointestinal hemorrhage (UGIH) on weekends to those admitted on a weekday. Hospitals in 31 states from the Nationwide Inpatient Sample between 1998 and 2003 were included. This resulted in 75,636 patients admitted during the week and 23,339 admitted on a weekend with UGIH. Multivariable analyses were conducted to evaluate the effect of weekend admission on UGIH outcomes. RESULTS: Compared to patients admitted on a weekday, for those admitted on a weekend: in-hospital mortality was higher (unadjusted mortality 3.76 vs. 3.33%; P = 0.003; adjusted HR = 1.09, 95% CI = 1.00-1.18); adjusted length of stay was 1.7% longer (P = 0.0098); and adjusted in-hospital charges were 3.3% higher (P = 0.0038). Although these patients were less likely to undergo endoscopy (adjusted OR = 0.94; P = 0.004) and waited longer for this procedure (adjusted HR = 0.87; P < 0.001), these discrepancies did not fully explain their inferior outcomes. CONCLUSIONS: Weekend admission for UGIH is associated with an increased risk of death, slightly longer lengths of stay, and marginally higher in-patient charges. Discrepancies in the use and timing of endoscopy do not account for these differences.

A comparison of hospital adverse events identified by three widely used detection methods.

OBJECTIVE: Determine the degree of congruence between several measures of adverse events. DESIGN: Cross-sectional study to assess frequency and type of adverse events identified using a variety of methods. SETTING: Mayo Clinic Rochester hospitals. PARTICIPANTS: All inpatients discharged in 2005 (n = 60 599). INTERVENTIONS: Adverse events were identified through multiple methods: (i) Agency for Healthcare Research and Quality-defined patient safety indicators (PSIs) using ICD-9 diagnosis codes from administrative discharge abstracts, (ii) provider-reported events, and (iii) Institute for Healthcare Improvement Global Trigger Tool with physician confirmation. PSIs were adjusted to exclude patient conditions present at admission. MAIN OUTCOME MEASURE: Agreement of identification between methods. RESULTS: About 4% (2401) of hospital discharges had an adverse event identified by at least one method. Around 38% (922) of identified events were provider-reported events. Nearly 43% of provider-reported adverse events were skin integrity events, 23% medication events, 21% falls, 1.8% equipment events and 37% miscellaneous events. Patients with adverse events identified by one method were not usually identified using another method. Only 97 (6.2%) of hospitalizations with a PSI also had a provider-reported event and only 10.5% of provider-reported events had a PSI. CONCLUSIONS: Different detection methods identified different adverse events. Findings are consistent with studies that recommend combining approaches to measure patient safety for internal quality improvement. Potential reported adverse event inconsistencies, low association with documented harm and reporting differences across organizations, however, raise concerns about using these patient safety measures for public reporting and organizational performance comparison.