Appointment Template Redesign in a Women’s Health Clinic Using Clinical Constraints to Improve Service Quality and Efficiency

Abstract

SummaryBackground: Patient wait time is a critical element of access to care that has long been recognized as a major problem in modern outpatient health care delivery systems. It impacts patient and medical staff productivity, stress, quality and efficiency of medical care, as well as health-care cost and availability. Objectives: This study was conducted in a Women's Health Clinic. The objective was to improve clinic service quality by redesigning patient appointment template using the clinical constraints. Methods: The proposed scheduling template consisted of two key elements: the redesign of appointment types and the determination of the length of time slots using defined constraints. The reclassification technique was used for the redesign of appointment visit types to capture service variation for scheduling purposes. Then, the appointment length was determined by incorporating clinic constraints or goals, such as patient wait time, physician idle time, overtime, finish time, lunch hours, when the last appointment was scheduled, and the desired number of appointment slots, to converge the optimal length of appointment slots for each visit type. Results: The redesigned template was implemented and the results indicated a 73% reduction in average patient waiting from the reported 40 to 11 minutes. The patient no-show rate was reduced by 4% from 24% to 20%. The morning section on average finished about 11:50 am. The clinic day was finished around 4:45 pm. Provider average idle time was estimated to be about 5 minutes, which can be used for charting/documenting patients. Conclusions: This study provided an alternative method of redesigning appointment scheduling templates using only the clinical constraints rather than the traditional way that required an objective function. This paper also documented the employed methods step by step in a real clinic setting. The implementation results concluded a significant improvement on patient wait time and noshow rate.

Outpatient Appointment Block Scheduling Under Patient Heterogeneity and Patient No‐Shows

“…We also assume a clinic scheduler knows each patient's information in advance, making it possible to assign a type to each patient. This assumption is supported by practitioner‐oriented literature (Huang and Verduzco 2015, Klassen et al. 2010) and observed healthcare practices.…”

“…Clinics may want to categorize their patients into three or more types. For example, the reader may refer to the women's health clinic example presented in Huang and Verduzco (2015). This section extends patient heterogeneity to m ≥ 3 types.…”

“…In Appendix S2, we also evaluate the performance of Algorithm OptBlock m for the practical problem instances in Huang and Verduzco (2015) and conclude that it performs well in the worst and average performance contexts.…”

Scheduling and Routing of Dispatching Medical Staff to Homes Healthcare from Different Medical Centers with considering Fairness Policy

“…According to Cayirli [12], they simulate different appointment and sequence rules by varying service times for new and previous patients and changing the presence of homogeneous absences. In order to converge with optimum appointment lengths for each class, Huang and Verduzco [13] reclassify patients into different types of visits and analyze performance measures, such as patient waiting time and physician downtime. Data mining has been used by Bentayeb et al [14] to develop a time-ofservice model for scheduling appointments using an appointment scheduler.…”

Ability of an Appointment Complexity Categorization System Applied by Non-Medical Personnel to Discriminate Between Patient Categories

“…3 Multiple studies have attempted to redesign appointment scheduling templates by assigning patients to scheduled groups to maximize time management during appointments (i.e., appointment times are no longer or shorter than necessary). [2][3][4][5][6] One study grouped patients according to visit type (emergency/urgent care, follow-up, new patient, etc.) and found that a certain degree of categorization minimized patient wait time, physician idle time, and average overtime when compared to systems without categorization.…”