Wait time management strategies at centralized intake system for hip and knee replacement surgery: A need for a blended evidence-based and patient-centered approach.

Objectives: Delays in access to specialty care and elective hip and knee total joint replacement (TJR) surgery remain a major concern among patients with osteoarthritis (OA) in Canada. Centralized intake systems as a wait time management strategy in the face of resource constraints can increase access and patient flow through the system but are not standard practice. We examine how wait time management strategies for the assessment and triaging referrals in a centralized intake system can inform quality improvement initiatives. Design: We developed a discrete-event simulation model using all referrals to the Edmonton Bone and Joint Centre centralized intake system from 2012 to 2016 for the base case model. We assessed the combined effect of three wait time management strategies: improved prioritization, improved sorting through screening, and increased conservative management. Outcomes were measured in terms of patient flow and wait times. Results: The screener sees more patient referrals (7094 compared to 6922), and the number of patients who proceed to surgery is reduced by 282 patients (4%) in the wait time management scenario compared to the base case model. Wait times from referral to surgery are reduced by 54 days for surgical patients. Furthermore, urgent surgical patients experienced lower wait times in all stages of care than non-urgent patients, with wait times from referral to surgery reduced by 86 days. Conclusions: Triaging processes addressing prioritization, screening and conservative management of non-surgical patients can improve patient flow and significantly reduce patient wait times in a centralized intake process for TJR.

Alternative care providers in rheumatoid arthritis patient care: a queueing and simulation analysis.

Patients diagnosed with rheumatoid arthritis require lifelong monitoring by a rheumatologist. Initiation of the disease-modifying anti-rheumatic drug therapy within twelve weeks of the onset of symptoms is crucial to prevent joint damage and functional disability. We examine the impact of the engagement of alternate care providers (ACP) in alleviating delay due to limited rheumatologist capacity. Using queueing theory and discrete-event simulation, we model rheumatologist-only and rheumatologist-with-ACP system configurations as closed, multi-class queueing networks with class switching.Using summary data from an actual rheumatology clinic for illustration, we analyze various parameter conditions to aid clinic managers and policymakers in decisions concerning capacity allocations and feasible patient panel size that impact timeliness of care and resource utilization.Results not only confirm that a substantial increase in RA patient panel size with an ACP involved in the care of follow-up patients but also demonstrates the boundaries for feasible panel sizes and workload allocation.

Patient classification based on volume and case-mix in the emergency department and their association with performance.

Predicting daily patient volume is necessary for emergency department (ED) strategic and operational decisions, such as resource planning and workforce scheduling. For these purposes, forecast accuracy requires understanding the heterogeneity among patients with respect to their characteristics and reasons for visits. To capture the heterogeneity among ED patients (case-mix), we present a patient coding and classification scheme (PCCS) based on patient demographics and diagnostic information. The proposed PCCS allows us to mathematically formalize the arrival patterns of the patient population as well as each class of patients. We can then examine the volume and case-mix of patients presenting to an ED and investigate their relationship to the ED's quality and time-based performance metrics. We use data from five hospitals in February, July and November for the years of 2007, 2012, and 2017 in the city of Calgary, Alberta, Canada. We find meaningful arrival time patterns of the patient population as well as classes of patients in EDs. The regression results suggest that patient volume is the main predictor of time-based ED performance measures. Case-mix is, however, the key predictor of quality of care in EDs. We conclude that considering both patient volume and the mix of patients are necessary for more accurate strategic and operational planning in EDs.

Policy choices in dementia care-An exploratory analysis of the Alberta continuing care system (ACCS) using system dynamics.

BACKGROUND: The increase in the incidence of dementia in the aging population and the decrease in the availability of informal caregivers put pressure on continuing care systems to care for a growing number of people with disabilities. Policy changes in the continuing care system need to address this shift in the population structure. One of the most effective tools for assessing policies in complex systems is system dynamics. Nevertheless, this method is underused in continuing care capacity planning. METHODS: A system dynamics model of the Alberta Continuing Care System was developed using stylized data. Sensitivity analyses and policy evaluations were conducted to demonstrate the use of system dynamics modelling in this area of public health planning. We focused our policy exploration on introducing staff/resident benchmarks in both supportive living and long-term care (LTC). RESULTS: The sensitivity analyses presented in this paper help identify leverage points in the system that need to be acknowledged when policy decisions are made. Our policy explorations showed that the deficits of staff increase dramatically when benchmarks are introduced, as expected, but at the end of the simulation period, the difference in deficits of both nurses and health care aids are similar between the 2 scenarios tested. Modifying the benchmarks in LTC only versus in both supportive living and LTC has similar effects on staff deficits in long term, under the assumptions of this particular model. CONCLUSION: The continuing care system dynamics model can be used to test various policy scenarios, allowing decision makers to visualize the effect of a certain policy choice on different system variables and to compare different policy options. Our exploration illustrates the use of system dynamics models for policy making in complex health care systems.

Using Operational Analysis to Improve Access to Pulmonary Function Testing.

Background. Timely pulmonary function testing is crucial to improving diagnosis and treatment of pulmonary diseases. Perceptions of poor access at an academic pulmonary function laboratory prompted analysis of system demand and capacity to identify factors contributing to poor access. Methods. Surveys and interviews identified stakeholder perspectives on operational processes and access challenges. Retrospective data on testing demand and resource capacity was analyzed to understand utilization of testing resources. Results. Qualitative analysis demonstrated that stakeholder groups had discrepant views on access and capacity in the laboratory. Mean daily resource utilization was 0.64 (SD 0.15), with monthly average utilization consistently less than 0.75. Reserved testing slots for subspecialty clinics were poorly utilized, leaving many testing slots unfilled. When subspecialty demand exceeded number of reserved slots, there was sufficient capacity in the pulmonary function schedule to accommodate added demand. Findings were shared with stakeholders and influenced scheduling process improvements. Conclusion. This study highlights the importance of operational data to identify causes of poor access, guide system decision-making, and determine effects of improvement initiatives in a variety of healthcare settings. Importantly, simple operational analysis can help to improve efficiency of health systems with little or no added financial investment.

An observational study of the effectiveness of alternative care providers in the management of obstructive sleep apnea.

Alternative care providers have been proposed as a substitute for physician-based management of obstructive sleep apnea. The purpose of this study was to describe the clinical course of patients with a new diagnosis of obstructive sleep apnea who were treated with continuous positive airway pressure and followed by alternative care providers at a tertiary care sleep clinic. It was hypothesized that care by alternative care providers would result in improvement of daytime sleepiness and satisfactory treatment adherence, and that a specific number of follow-up visits could be identified after which clinical outcomes no longer improved. The Epworth Sleepiness Scale score was measured for each patient at baseline and at each alternative care provider visit. Patients were discharged when they demonstrated a significant improvement in sleepiness and were adherent to therapy. The Epworth Sleepiness Scale score decreased by 3.9 points from baseline to discharge. Patients with three or more visits required more follow-up time to achieve the same clinical improvement as those with only two visits. Continuous positive airway pressure adherence was comparable to previous studies of physician-led care and improved with ongoing alternative care provider follow-up. The current results suggest that clinical care by alternative care providers leads to continued improvements in sleepiness in patients with obstructive sleep apnea who are treated with continuous positive airway pressure, and that a minority of patients require longer follow-up to achieve a satisfactory clinical response to therapy.

Using patient flow simulation to improve access at a multidisciplinary sleep centre.

The lack of timely access to diagnosis and treatment for sleep disorders is well described, but little attention has been paid to understanding how multiple system constraints contribute to long waiting times. The objectives of this study were to identify system constraints leading to long waiting times at a multidisciplinary sleep centre, and to use patient flow simulation modelling to test solutions that could improve access. Discrete-event simulation models of patient flow were constructed using historical data from 150 patients referred to the sleep centre, and used to both examine reasons for access delays and to test alternative system configurations that were predicted by administrators to reduce waiting times. Four possible solutions were modelled and compared with baseline, including addition of capacity to different areas at the sleep centre and elimination of prioritization by urgency. Within the model, adding physician capacity improved time from patient referral to initial physician appointment, but worsened time from polysomnography requisition to test completion, and had no effect on time from patient referral to treatment initiation. Adding respiratory therapist did not improve model performance compared with baseline. Eliminating triage prioritization worsened time to physician assessment and treatment initiation for urgent patients without improving waiting times overall. This study demonstrates that discrete-event simulation can identify multiple constraints in access-limited healthcare systems and allow suggested solutions to be tested before implementation. The model of this sleep centre predicted that investments in capacity expansion proposed by administrators would not reduce the time to a clinically meaningful patient outcome.

Using simulation modeling to improve patient flow at an outpatient orthopedic clinic.

We report on the use of discrete event simulation modeling to support process improvements at an orthopedic outpatient clinic. The clinic was effective in treating patients, but waiting time and congestion in the clinic created patient dissatisfaction and staff morale issues. The modeling helped to identify improvement alternatives including optimized staffing levels, better patient scheduling, and an emphasis on staff arriving promptly. Quantitative results from the modeling provided motivation to implement the improvements. Statistical analysis of data taken before and after the implementation indicate that waiting time measures were significantly improved and overall patient time in the clinic was reduced.

Modeling patient service centers with simulation and system dynamics.

We report on the use of simulation modeling for redesigning phlebotomy and specimen collection centers (or patient service centers) at a medical diagnostic laboratory. Research was performed in an effort to improve patient service, in particular to reduce average waiting times as well as their variability. Discrete-event simulation modeling provided valuable input into new facility design decisions and showed the efficacy of pooling sources of variation, particularly patient demand and service times. Initial performance of the redesigned facilities was positive; however, dynamic feedback within the system of service centers eventually resulted in unanticipated performance problems. We show how a system dynamics model might have helped predict these implementation problems and suggest some ways to improve results.