June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Improving Clinic Workflows through Simulations
Author Affiliations & Notes
  • Michelle Hribar
    DMICE, OHSU, Portland, Oregon, United States
  • David Biermann
    OHSU, Portland, Oregon, United States
  • Sarah Read-Brown
    Ophthalmology, OHSU, Portland, Oregon, United States
  • Leah G. Reznick
    Ophthalmology, OHSU, Portland, Oregon, United States
  • Lorinna Lombardi
    Ophthalmology, OHSU, Portland, Oregon, United States
  • Mansi Parikh
    Ophthalmology, OHSU, Portland, Oregon, United States
  • Winston Chamberlain
    Ophthalmology, OHSU, Portland, Oregon, United States
  • Thomas R Yackel
    DMICE, 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; David Biermann, None; Sarah Read-Brown, None; Leah Reznick, None; Lorinna Lombardi, None; Mansi Parikh, None; Winston Chamberlain, None; Thomas Yackel, None; Michael Chiang, Clarity Medical Systems (Pleasanton, CA) (S), Novartis (Basel, Switzerland) (C)
  • Footnotes
    Support  NLM grant K99LM012238, NIH grant P30EY10572, unrestricted departmental funding from Research to Prevent Blindness
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 5085. doi:
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      Michelle Hribar, David Biermann, Sarah Read-Brown, Leah G. Reznick, Lorinna Lombardi, Mansi Parikh, Winston Chamberlain, Thomas R Yackel, Michael F Chiang; Improving Clinic Workflows through Simulations. Invest. Ophthalmol. Vis. Sci. 2017;58(8):5085.

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

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Abstract

Purpose : Ophthalmologists struggle with clinic efficiency, but lack guidance about how to improve it. This study demonstrates that simulation models using electronic health record (EHR) timestamp data may be used to improve clinic workflow.

Methods : Clinical workflow was observed and mapped for 4 outpatient ophthalmology clinics (LR, LL, MP, & WC). EHR databases were used to identify timestamps from the medical record and audit log that best correlated with typical clinical activities during one year (10,859 patient visits, >2 million timestamps). Data were used to develop computer simulation models (Arena; Rockwell, Wexford, PA) for the 4 clinics to evaluate different scheduling policies and clinic configurations for minimizing patient wait time. The most optimal of these scheduling policies was tested in clinic (LR) during 10 half-day clinics (123 patient encounters) and compared to 8 previously observed baseline half-day clinics (102 patient encounters) in which patients were not scheduled according to any policy.

Results : Simulation models were used to evaluate resources and scheduling strategies. For all 4 clinics, adding exam rooms or staff to a clinic shortened wait times only up to 3 exam rooms and 3 staff. We determined that scheduling patients requiring the longest times later in the clinic reduced patient wait time, but also increased clinic length (Figure 1). Using the most optimal of these scheduling policies in one clinic (LR) reduced average wait time per patient to 19.4 ± 14.9 minutes, compared to a baseline of 24.9 ± 17.8 minutes (p = 0.02). This represents a mean improvement in waiting time of 5.5 minutes (22%).

Conclusions : Simulation models using EHR timestamps are effective for testing alternative clinic configuration and scheduling policies for decreasing patient wait times. Results from models can help clinic managers make informed decisions about staffing, clinic space and scheduling. Tests in clinics indicate that scheduling long appointments near the end of the clinic session reduces patient wait time. Finally, our results are consistent for all 4 providers’ clinic models, which suggests that they are generalizable.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

Figure 1: Placement of Long Encounters. Long encounters at the start of the session minimize overall clinic length and long encounters at the end of the session minimize patient wait time. Graphical analyses can help clinic managers find the placement that best balances the two competing metrics.

Figure 1: Placement of Long Encounters. Long encounters at the start of the session minimize overall clinic length and long encounters at the end of the session minimize patient wait time. Graphical analyses can help clinic managers find the placement that best balances the two competing metrics.

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