A carve-out model for primary care appointment scheduling with same-day requests and no-shows

Small Changes in Patient Arrival and Consultation Times Have Large Effects on Patients' Waiting Times: Simulation Analyses for Primary Care

(1) Background: Workflows are a daily challenge in general practices. The desired smooth work processes and patient flows are not easy to achieve. This study uses an operational research approach to illustrate the general effects of patient arrival and consultation times on waiting times. (2) Methods: Stochastic simulations were used to model complex daily workflows of general practice. Following classical queuing models, patient arrivals, queuing discipline, and physician consultation times are three key factors influencing work processes. (3) Results: In the first scenario, with patients arriving every 7.6 min and random consultation times, the individual patients' maximum waiting time increased to more than 200 min. The second scenario with random patient arrivals and random consultation times increased the average waiting time by up to 30 min compared to patients arriving on schedule. A busy morning session based on the second scenario was investigated to compare two alternative intervention strategies to reduce subsequent waiting times. Both could reduce waiting times by a multiple for each minute of reduced consultation time. (4) Conclusions: Aiming to improve family physicians' awareness of strategies for improving workflows, this simulation study illustrates the effects of strategies that address consultation times and patient arrivals.

Comparing 2 Appointment Scheduling Policies Using Discrete-Event Simulation

BACKGROUND AND OBJECTIVES

Two of the most important policies for dealing with the negative effects of high rates of no-show patients and appointment cancellations include double-booking and walk-in admission policies. This study aimed to compare these policies to identify their differences and their effects as well as the best circumstances for using each one.

METHODS

The main approach used in this study was discrete-event simulation using the Arena software application. Moreover, the average waiting time (considering patients' lateness) and the number of missed patients (considering no-show and cancelled patients) were accounted for in the performance evaluation criteria for both of the selected policies.

RESULTS

When the patients' arrival rate was high, the double-booking system resulted in higher productivity, while when it was low, the walk-in admission policy was the best policy for patient admission. The successful appointment rates of the current system, the walk-in admission system, and the double-booking system were 61.18%, 89.45%, and 93.24%, respectively.

CONCLUSIONS

Although both double-booking and walk-in policies reduced the negative effects of cancelled and no-show patients, they had significantly different results in different situations. In general, there is no best system for appointment scheduling, and the choice of the superior system depends on the demand rate and its fluctuations.

Reducing the wait: When time is priceless

ABSTRACT

Today, healthcare providers are not only charged with providing high-quality evidence-based care to improve patient outcomes, but also with completing quick patient visits due to time constraints to necessitate financial reimbursement. This article summarizes current evidence to determine best practices for managing outpatient wait times and improve outcomes, satisfaction, quality, and continuity of care.

Designing schedule configuration of a hybrid appointment system for a two-stage outpatient clinic with multiple servers

Even though several clinics serve patients in more than one stage (e.g., visit nurse and then visit doctor) and employ multiple providers in each stage, most of the previous work on appointment system design considers a simplified single-stage single-server clinic. Motivated by a real-life clinic setting, this paper aims to determine the schedule configuration of a hybrid appointment system (i.e., the number of pre-booking and same-day time slots reserved for a physician along with their positions in the schedule) for a two-stage multi-server clinic. A stochastic optimization model is developed to obtain a schedule configuration that minimizes the expected total cost - a weighted sum of excessive patient waiting time, resource idle time, resource overtime, and denied appointment requests. Owing to its computational complexity, we estimate the expected total cost using the sample average approximation method. The proposed model is verified and validated using small test instances and subject matter experts. A case study of a family medicine clinic in Pennsylvania is used to illustrate the proposed approach. The schedule generated by the proposed model results in a significantly lower expected cost compared to the approximated single-stage system's best schedule configuration and clinic's existing configuration. Further, sensitivity analysis is conducted to assess the impacts of no-show rate, service time variation, and cost ratios on the schedule configuration. Our findings demonstrate that the schedule configuration is sensitive to changes in the average no-show rate and cost ratios but is not significantly impacted by service time variation. Several managerial insights are also drawn from our analysis. Finally, we provide directions for future research that also highlights the potential to use the revenue management approach to address the problem under study.