Data-Driven Scheduling for Improving Patient Efficiency in Ophthalmology Clinics

Abstract

Objective: To improve clinic efficiency through development of an ophthalmology scheduling template developed using simulation models and electronic health record (EHR) data. Design: We created a computer simulation model of one pediatric ophthalmologist's clinic utilizing EHR timestamp data, which was used to develop a scheduling template based on appointment length ("short", "medium", or "long"). We assessed its impact on clinic efficiency after implementation in the practices of five different pediatric ophthalmologists. Subjects, Participants, and/or Controls: We observed and timed patient appointments inperson (n=120) and collected EHR timestamps for two years of appointments (n=650). We calculated efficiency measures for 172 clinic sessions pre-implementation vs. 119 clinic sessions post-implementation. Methods, Intervention, or Testing: We validated clinic workflow timings calculated from EHR timestamps and the simulation models based on them with observed timings. From simulation tests, we developed a new scheduling template and evaluated it with efficiency metrics pre-vs. post-implementation. Main Outcome Measures: Measurements of clinical efficiency (mean clinic volume, patient wait time, exam time, and clinic length).

Artificial Intelligence-Assisted Reduction in Patients’ Waiting Time for Outpatient Procedures: A Matched Case–Control Study

“…It is important to note that patient satisfaction increases when patients are told about their expected wait time. Similar results have been reported in other studies [9,[26][27][28][29][30].…”

Artificial intelligence-assisted reduction in patients’ waiting time for outpatient process: a retrospective cohort study

“…It is important to emphasize that patient satisfaction increases when patients are told about their expected wait time. Analogous results have been reported in other studies [9,[26][27][28][29][30].…”

Modeling the Impact of COVID-19 on Retina Clinic Performance

“…Discrete-event simulation provides a model of the operations of a system in sequence, and it has been used extensively as a tool for analyzing healthcare systems with a goal of quality improvement [10]. In ophthalmology, discrete-event simulation has been effectively employed to model out-patient clinic ow and capacity restraints [11][12][13], and results have been validated by the inclusion of time-stamped EMR data [14]. Our model sought to assess the impact of measures adopted after the outbreak of COVID-19 on retina clinic operations.…”

“…There are limitations to this type of study. Foremost is that timestamped data depends on user input, and in our study, in-person observation or video recording was not available to validate our internal timestamps for accuracy [14]. Another limitation inherent to our model is that it uses assumptions based on EMR timestamps and event rates derived from a period when the clinic was far below historical utilization rates and seeing far fewer patients.…”