No abstract

PURPOSE:To use lean analysis to identify how often and when wait times occur during a glaucoma visit to identify opportunities for additional patient engagement. METHODS:This prospective observational time-motion study measured process and wait times for 77 patient visits from 12 ophthalmologists at an academic glaucoma clinic over a 3-month period. Value stream maps visually diagrammed the process of a clinic visit from the patient's perspective. Descriptive statistics were calculated for process times, wait times, and the frequency of 10+ minute wait times during each part of the visit. Key stakeholders participated in a root cause analysis to identify reasons for long wait times. The main outcome measure was average times (hours:minutes: seconds) for process times and wait times.RESULTS: 29 new visit (NV) patients and 48 return visit (RV) patients were included. Total time in clinic was 187.1 ± 44.5 (mean ± SD) minutes for NV patients and 102.0 ± 44.7 minutes for RV patients. Wait time for NV patients was 63.7 ± 33.4 minutes (33.1% of total appointment time) and for RV patients was 52.6 ± 31.6 minutes (49.4% of the total appointment time). All NV patients and 87.5% of RV patients had at least one 10+ minute wait time during their clinic visit and the majority (75.9% NV, 60.4% RV) had >1. CONCLUSIONS:Currently, sufficient wait time exists during the visit for key portions of glaucoma education such as teaching eye drop instillation.

Background Lean methodology helps maximize value by reducing waste, first by defining what value and waste are in a system. In ophthalmology clinics, value is determined by the number of patients flowing through the clinic for a given time. We aimed to increase value using a lean-methodology guided policy change, then assessed its impact on clinic flow using an automated radiofrequency identification (RFID) based real-time locating system (RTLS). Methods A total of 6813 clinical visits occurred at a single academic institution’s outpatient glaucoma clinic between January 5, 2018 to July 3, 2018. Over that period, 1589 patients comprising 1972 (29%) of visits were enrolled, with 1031 clinical visits occurring before and 941 visits after a policy change. The original policy was to refract all patients that improved with pinhole testing. The policy change was not to refract patients with a visual acuity ≥20/30 unless a specific request was made by the patient. Pre-post analysis of an automated time-motion study was conducted for the data collected 3 months before and 3 months after the policy change occurred on March 30, 2018. Changes to process and wait times were summarized using descriptive statistics and fitted to linear mixed regression models adjusting for appointment type, clinic volume, and daily clinic trends. Results One thousand nine hundred twenty-three visits with 1588 patients were included in the analysis. Mean [SD] age was 65.9 [14.7] years and 892 [56.2%] were women. After the policy change, technician process time decreased by 2.9 min (p < 0.0001) while daily clinical patient volume increased from 51.9 ± 16.8 patients to 58.4 ± 17.4 patients (p < 0.038). No significant difference was found in total wait time (p = 0.18) or total visit time (p = 0.83). Conclusions Real-time locating systems are effective at capturing clinical flow data and assessing clinical practice change initiatives. The refraction policy change was associated with reduced technician process time and overall the clinic was able to care for 7 more patients per day without significantly increasing patient wait time.