Data-Driven Scheduling for Improving Patient Efficiency in Ophthalmology Clinics

Abstract

Purpose: 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 1 pediatric ophthalmologist's clinic using 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 5 different pediatric ophthalmologists.

Participants: We observed and timed patient appointments in person (n = 120) and collected EHR timestamps for 2 years of appointments (n = 650). We calculated efficiency measures for 172 clinic sessions before implementation vs. 119 clinic sessions after implementation.

Methods: 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 before vs. after implementation.

Main outcome measures: Measurements of clinical efficiency (mean clinic volume, patient wait time, examination time, and clinic length).

Results: Mean physician examination time calculated from EHR timestamps was 13.8±8.2 minutes and was not statistically different from mean physician examination time from in-person observation (13.3±7.3 minutes; P = 0.7), suggesting that EHR timestamps are accurate. Mean patient wait time for the simulation model (31.2±10.9 minutes) was not statistically different from the observed mean patient wait times (32.6±25.3 minutes; P = 0.9), suggesting that simulation models are accurate. After implementation of the new scheduling template, all 5 pediatric ophthalmologists showed statistically significant improvements in clinic volume (mean increase of 1-3 patients/session; P ≤ 0.05 for 2 providers; P ≤ 0.008 for 3 providers), whereas 4 of 5 had improvements in mean patient wait time (average improvements of 3-4 minutes/patient; statistically significant for 2 providers, P ≤ 0.008). All of the ophthalmologists' examination times remained the same before and after implementation.

Conclusions: Simulation models based on big data from EHRs can test clinic changes before real-life implementation. A scheduling template using predicted appointment length improves clinic efficiency and may generalize to other clinics. Electronic health records have potential to become tools for supporting clinic operations improvement.

Figure 1.. Clinic workflow and electronic health record timestamp mapping.

Patients move from check-in to check-out, and distribution of associated time requirements are gathered from EHR data mart, audit logs, and exam templates.

Figure 2.. Validation of technologies for (A) using electronic health record timestamps for estimating patient exam times, and (B) using computer simulation models for predicting patient wait time.

Dark shaded density plots show in-person observation reference standard times.

Figure 2.. Validation of technologies for (A) using electronic health record timestamps for estimating patient exam times, and (B) using computer simulation models for predicting patient wait time.

Dark shaded density plots show in-person observation reference standard times.

Figure 3:. Computer simulation tests demonstrating the impact of varying the number of ancillary staff and exam rooms on predicted mean patient wait time.

Figure 3:. Computer simulation tests demonstrating the impact of varying the number of ancillary staff and exam rooms on predicted mean patient wait time.

Figure 4.. Example of computer simulation test results to develop new scheduling templates.

Graph displays impact of placement of “long” encounters (slowest 25%) on predicted mean clinic length and predicted mean patient wait time in computer simulation models.