July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Improving Clinic Wait Times with Scheduling Templates Developed from Computer-Based Simulation using Electronic Health Record Data
Author Affiliations & Notes
  • Michelle Hribar
    DMICE, OHSU, Portland, Oregon, United States
  • Sarah Read-Brown
    Ophthalmology, OHSU, Portland, Oregon, United States
  • Isaac Goldstein
    Ophthalmology, OHSU, Portland, Oregon, United States
  • Leah Reznick
    Ophthalmology, OHSU, Portland, Oregon, United States
  • Michael F Chiang
    Ophthalmology, OHSU, Portland, Oregon, United States
    DMICE, OHSU, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Michelle Hribar, None; Sarah Read-Brown, None; Isaac Goldstein, None; Leah Reznick, None; Michael Chiang, Clarity Medical Systems (S), Novartis (C)
  • Footnotes
    Support  NIH Grant P30 EY010572, NLM Grant R00LM012238, and Unrestricted departmental funding from Research to Prevent Blindness
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 5248. doi:
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    • Get Citation

      Michelle Hribar, Sarah Read-Brown, Isaac Goldstein, Leah Reznick, Michael F Chiang; Improving Clinic Wait Times with Scheduling Templates Developed from Computer-Based Simulation using Electronic Health Record Data. Invest. Ophthalmol. Vis. Sci. 2018;59(9):5248.

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      © ARVO (1962-2015); The Authors (2016-present)

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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.

 

Table 1: Evaluation of new scheduling template, comparing 3 months post implementation to the same 3 months the previous year. Mean patient wait time improved by over 3 minutes (14%), while the session volume increased by one.

Table 1: Evaluation of new scheduling template, comparing 3 months post implementation to the same 3 months the previous year. Mean patient wait time improved by over 3 minutes (14%), while the session volume increased by one.

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