Abstract
Purpose :
Patient wait time is a concern for outpatient ophthalmology clinics; it affects patient satisfaction and followup rates and can worsen as ophthalmologists are pressured to see more patients in less time. Scheduling patients according to their expected appointment length can reduce patient wait time, based on a computer simulation model of an ophthalmology clinics at Oregon Health & Science University (OHSU). This study examines the impact of a new scheduling template on patient wait time after real-world clinic implementation.
Methods :
Clinical workflow was observed and mapped for an outpatient ophthalmology clinic. EHR timestamps were used to derive workflow timings and to build a simulation model of the clinic’s workflow (Arena; Rockwell, Wexford, PA). The simulation model was used to develop and test a new scheduling template that orders patient appointments based on predicted appointment lengths. The new template was implemented in clinic in September 2016. Three months of appointment data post-implementation was compared to the same period in the previous year pre-implementation to determine the effect on clinic efficiency. T-tests were used to compare mean patient wait times, clinic lengths, and clinic volumes.
Results :
Scheduling short appointments at the start of the clinic session and long appointments near the end of the session reduced patient wait time in the simulations and in clinic. The average patient wait time improved by over 3 minutes (a 14% decrease) (p = 0.04), while the average patient volume increased by one (p = 0.004) during the post-implementation period as compared to the pre-implementation period (Table 1). There was no significant change in the half-day clinic session length.
Conclusions :
Computer simulation models based on EHR timestamp data can be used to develop improved clinical scheduling templates that appear to improve patient wait time while increasing average clinic volume. This warrants study in broader settings and may have important implications for delivery and efficiency of clinical care.
This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.