Bhandari, Pawan

Mayo Clinic Health System — Southwest Minnesota Region, one of four practice regions in the Midwest, provides primary and speciality care in ambulatory, procedural and hospital settings. Clinic appointment fill rates, no-show rates and late cancellation rates are closely monitored operational metrics, but at the time this study began, less was known about the refill rates of late appointment cancellations. This metric is of importance because these unfilled appointment times contribute to increased expenses for healthcare organisations, unnecessary delays in care and decreases in revenue. The main objectives of this study were to understand the current state and potential opportunities for refilling late cancellations. Qualitative and quantitative methods were used, including variance analysis, the Tukey post hoc test, Pareto analysis, the completion of interviews with important stakeholders and data extraction from the institution’s electronic health record. Findings showed that late cancellation rates remained steady in 2021 and that late cancellations were not being refilled consistently across departments and regions. Appointment refill rates were higher in departments with few or no requirements for preparatory evaluation, whereas in specialties that did require preparatory evaluations, refill rates were lower across the four regions of the Mayo Clinic Health System. Even in departments with high demand, the refill rates were lower, resulting in missed opportunities for patient visits. Mapping the workflow for late cancellations indicated inconsistent methods for refilling late cancellation openings and the lack of a defined process for refilling them that was understood by the entire care team. The logistics of optimising refill rates for late cancellations were affected by provider preference, selfscheduling functionality and appointment requirements. This study identified opportunities for optimising workflows to use the waiting list proactively to refill late cancellations.

With current and projected staffing shortages, healthcare settings must find innovative ways to address workload problems. One possible solution in the outpatient setting is the use of patient self-check-in kiosks. This paper aimed to understand the effectiveness of the use of self-check-in kiosks in a rural community-based clinic setting. Data was gathered retrospectively on patients seen in our clinic during 2022. Two-sample z-tests were used to compare the use of kiosks before and after the implementation of intentional interventions to help motivate patients to check in using kiosks. Binomial logistic regression was used to determine factors that influence use of kiosks. We then analysed patient satisfaction surveys to determine whether the interventions affected patient satisfaction. A total of 143,208 patients were included in the study. Significantly more patients used the self-check-in kiosks during the post-intervention period (24.9 per cent) than during the pre-intervention period (6.5 per cent) ( z = −102.79, P < 0.001). The outcome of the binomial logistic regression in the form of a model was significant ( χ2(9) = 2,274, P < 0.001) and showed that gender, age, financial (insurance) class and time of check-in significantly affect the likelihood of patients using the self-check-in kiosks. Patient satisfaction was not affected by the interventions. The study findings suggest that it is possible to increase the use of self-check-in kiosks with intentional interventions in a community-based clinic setting without negatively affecting patient satisfaction. Expecting that patients will use technology just because it is there may not be feasible, as evidenced by this case. Non-use of the technology by patients could lead to negative consequences, such as longer wait times and effects on overall patient flow.

As modern medicine offers novel surgical therapies for conditions simple and complex, the proper resource allocation of operating rooms, surgical staff and supporting services is becoming increasingly important for healthcare organisations. Surgical services are one of the most essential parts of any healthcare system, substantially influencing patient choice of healthcare services. These services are typically the centre for revenue but also impart considerable costs. Given the complexity of surgical schedule planning, expectations of high reliability and demand on resources, many healthcare organisations have resorted to using a static block schedule based on historic utilisation patterns. This is based on the dogma that surgical schedules are not predictable, surgical blocks are stable, and surgical yield has no direct correlation with outpatient schedules. Here we share our two-year experience with a Surgical Predictor Tool using our surgical practice management principles and the cost-effective use of these means to meet the needs of patients and our surgical staff in an integrated community health system.

The COVID-19 pandemic caused a transformation in healthcare delivery at a pace never before experienced, including by the child psychiatry practice at Mayo Clinic Health System in Mankato, Minnesota. The aim of this study was to describe the outcomes of that practice’s transition from in-person to virtual care. This retrospective study comprised two groups of patients who received care in the child psychiatry practice at our institution: before COVID-19, 1 March 2019 to 28 February 2020; and after COVID-19, 1 March 2020 to 28 February 2021. We extracted data related to patient visits from the electronic health record and satisfaction data from a Medical Practice Survey given to patients after visits. Virtual visits increased by 306 per cent within a month of transitioning to virtual care and remain the main visit type for established patients. Findings from patient surveys showed substantial overall improvements in patient experiences with virtual visits, and no-show appointments decreased substantially (from up to 23 per cent to 1 per cent). This study provided data for an overview of how the COVID-19 pandemic affected a child psychiatry practice and of the positive outcomes of transitioning from 100 per cent in-person visits before the pandemic to more than 80 per cent virtual visits. The quick transition to virtual care delivery in the child psychiatry practice was a positive change for patients, families and the care team. We believe that other healthcare systems can learn from our experience.

The demand for telehealth services in the United States saw an unprecedented increase following the 2020 COVID-19 pandemic. Prior to the pandemic, intake and virtual rooming functions for scheduled, synchronous video appointments at Mayo Clinic were supported by a small centralised team of virtual agents within its Center for Connected Care (Connected Care). In response to the pandemic-generated demand, Connected Care leveraged its existing model and the support of temporary staff to rapidly scale support for the increased video visit demand across the enterprise. Once demand stabilised in the summer of 2020, Mayo Clinic’s destination medical centres in Minnesota, Florida and Arizona, and Mayo Clinic Health System’s sites in the Midwest, launched pilots to evaluate alternative telehealth delivery models. The goal of these pilots was to test the flexibility of alternative intake models for supporting long-term scalability and growth. Through these pilots, each site developed unique workflows and staffing plans with the overall intake function remaining standardised across all locations. The workflows delineated responsibilities between clinical departments and virtual agents, leveraged current infrastructure and maximised best practices from the original model. This paper outlines the models implemented, and presents lessons learned and best practices garnered from each implementation.

Background Communication gaps, whether incomplete or fragmented communication, have been the cause of many disasters in human civilisation. Coordination of healthcare is directly related to proper communication and handoffs among multidisciplinary teams throughout multiple shifts during a patient’s hospitalisation. Local problem Patient surveys and direct patient feedback at Mayo Clinic Health System in Mankato, Minnesota, indicated that patient communication with physicians and nurses had declined in 2017 and 2018. Viewing this as an opportunity for improvement, our leadership initiated several changes to increase physician and nurse communication with patients, which resulted in no notable improvements. Methods A systematic quality improvement approach was implemented by using Six Sigma methodology. Stakeholders from multidisciplinary teams were assembled as the project team. The five steps of Six Sigma methodology (Define, Measure, Analyse, Improve and Control) were followed to create a quality improvement intervention. Intervention We developed a standardised and easy-to-use bedside team rounding tool to improve patient communication with physicians and nurses. Results Postintervention patient satisfaction top-box scores exceeded target improvements for both physician (from 78.5% to 82.0%, p<0.01) and nurse (from 80.5% to 83.1%, p=0.04) communication domains. Physicians had a 33-point increase in percentile rank (from 41st to 74th percentile rank), and nurses had a 25-point increase in percentile rank (from 59th to 84th percentile rank). This increase in communication ranked our institution at the top of national benchmark organisations. Conclusions Overwhelmingly positive patient feedback was achieved, and postintervention employee satisfaction was primarily positive when compared with preintervention satisfaction.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has impacted health care organizations throughout the world. The Southwest Minnesota Region of Mayo Clinic Health System, a community-based health care system, was not immune, and in March 2020, our outpatient services were deferred and decreased by 90%. Method: This article is a review of the approach we used to safely reactivate outpatient care, the tools that we developed, and the outcomes of these reactivation efforts. A novel Outpatient Practice Reactivation Framework was established and used that included Outpatient Clinic Appointment Dashboard, Decision Matrix, Access Management, Virtual Care, and Patient Safety. This framework was guided by patient demand for care and by safety principles, as recommended by state and federal agencies and our internal infectious disease department guidelines. Results and Conclusions: Over the course of 9 weeks, ambulatory visit volumes and clinic utilization rates returned to pre-COVID levels (Pre-COVID fill rate range: 87% to 94%, post-COVID fill rate range: 86% to 89%) exceeding target fill rate of 80%, as a result of establishing the initiative as a shared priority, committing to a robust schedule and decisive actions, creating and maintaining a well-defined structure, taking an inclusive approach, overcommunicating and providing sufficient data for transparency, developing guiding principles, and training and educating staff.