September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Modeling Clinical Workflow In Pediatric Ophthalmology
Author Affiliations & Notes
  • Michelle Hribar
    Department of Medical Informatics and Clinical Epidemiology, OHSU, Portland, Oregon, United States
  • Sarah Read-Brown
    Ophthalmology, OHSU, Portland, Oregon, United States
  • Leah G Reznick
    Ophthalmology, OHSU, Portland, Oregon, United States
  • Thomas Yackel
    Department of Medical Informatics and Clinical Epidemiology, OHSU, Portland, Oregon, United States
  • Michael F Chiang
    Ophthalmology, OHSU, Portland, Oregon, United States
    Department of Medical Informatics and Clinical Epidemiology, OHSU, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Michelle Hribar, None; Sarah Read-Brown, None; Leah Reznick, None; Thomas Yackel, None; Michael Chiang, None
  • Footnotes
    Support  NLM grant K99LM012238; NIH Core Grant P30 EY010572; unrestricted grant from Research to Prevent Blindness
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 5562. doi:
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    • Get Citation

      Michelle Hribar, Sarah Read-Brown, Leah G Reznick, Thomas Yackel, Michael F Chiang; Modeling Clinical Workflow In Pediatric Ophthalmology. Invest. Ophthalmol. Vis. Sci. 2016;57(12):5562.

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

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Abstract

Purpose : Pediatric ophthalmologists have raised concerns that EHRs negatively impact clinical productivity, but lack guidance about how to improve efficiency. This study demonstrates proof-of-concept simulation models using EHR timestamp data may be used to improve clinic workflow.

Methods : Clinical workflow was mapped for a single pediatric ophthalmologist (LGR). EHR databases were used to identify timestamps that best correlated with clinical activities during one year (2258 patient visits, 496,301 timestamps). EHR timestamps were validated against manual timings of activities for 89 patient visits by trained observers. Data were used to develop computer simulation models (Arena; Rockwell, Wexford, PA) to evaluate different scheduling policies and clinic configurations for minimizing patient wait time.

Results : EHR timestamp data were within 3 minutes of manual observed times in 95/146 (65%) of clinical activities. EHR timestamp data shows mean wait time was 36 ± 26 minutes/patient per visit. Simulation models were used to evaluate new scheduling strategies and clinic configurations. We determined that scheduling patients requiring shortest times earlier, and those requiring longer times later, should reduce wait time from 37 to 15 minutes/patient. This optimized schedule was implemented in the clinic setting (3 sessions, 30 patients), and decreased mean wait time to 31± 22 minutes/patient (p=0.04). Further, we determined that using 2 staff members and 3 exam rooms provides the best marginal returns for reducing wait time as shown in Figure 1.

Conclusions : These study findings indicate that EHRs can be extended toward a holistic tool for managing clinical workflow. EHR timestamp data may be used to develop computer simulation models for testing alternative clinic configuration and scheduling policies, which have potential to decrease patient wait time.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

Figure 1: Effects of varying the number of staff and the number of exam rooms on patient wait time. We conclude that 2 ancillary staff members and 3 exam rooms is a good configuration for reducing patient wait time.

Figure 1: Effects of varying the number of staff and the number of exam rooms on patient wait time. We conclude that 2 ancillary staff members and 3 exam rooms is a good configuration for reducing patient wait time.

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