Johnson, Ryan R.

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.