Data-Driven Scheduling for Improving Patient Efficiency in Ophthalmology Clinics.

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.

Erratum: Addendum: Outcomes of a Five-Year Formal Ophthalmology Residency Mentorship Program.

[This corrects the article DOI: 10.1055/s-0042-1756133.].

Outcomes of a Five-Year Formal Ophthalmology Residency Mentorship Program.

Objective: This article describes a formal ophthalmology residency mentorship program, identifies its strengths and weaknesses over 5 years of implementation, and proposes strategies to improve qualitative outcomes of the mentorship program. Design: Cross-sectional anonymous online survey. Subjects: All current and former mentees and mentors at the Casey Eye Institute (CEI) residency program from 2016 to 2021. Methods: All eligible participants were contacted via email to complete a survey to describe and analyze their experiences with the CEI's formal residency mentorship program. Results: Of the 65 surveyed participants, 82% preferred in-person meetings and met up from 2 to 3 times (44%) to 4 to 6 times (38.5%) annually at 15 minutes to 1 hour (48%) or 1 to 2 hours (42%) duration. Sixty-two percent of meetings were initiated by mentors, 8% by mentees, and 32% shared responsibilities equally. Participants also identified the three most important qualities for successful mentor-mentee relationship as personality (33.6%), communication styles (29.2%), and extracurricular interests/hobbies (16.8%). Mentees valued career advising, networking, and wellness support over academic and research mentorship. Subjective outcomes showed 25% of the mentee and 43% of the mentors agreed the mentorship program was a valuable experience. Comparably, 14% of the mentees and 38% of the mentors prioritized the relationship. There was a strong correlation between participants who prioritized the relationship and acknowledged it as a valuable experience (p < 0.01). Eighteen percent of the mentees and 43% of the mentors found the relationship effective and met their expectations. Twenty-one percent of the mentees and 38% of the mentors believed they had the tools and skills necessary to be effective in their respective roles. Conclusion: Our survey identified that weaknesses of the mentorship program include ineffective communications, inadequate preparation in their respective roles, and lack of priority focus on the relationship. We propose strategies to strengthen our program through creating workshops to clarify roles and responsibilities, emphasizing accountability with a contract statement, and implementing a new matching algorithm to customize participants' experience. Additional studies from other residencies with formal mentorship programs are warranted to identify, strategize, and foster high-quality mentorship.

Analysis of Total Time Requirements of Electronic Health Record Use by Ophthalmologists Using Secondary EHR Data.

Electronic Health Records (EHRs) are widely used in the United States for clinical care and billing activities. Their widespread adoption has raised a variety of concerns about their effects on providers and medical care. As researchers address these concerns, they will need to understand how much time providers actually spend on the EHR. This study develops and validates methods for calculating total time requirements for EHR use by ophthalmologists using secondary EHR data from audit logs. Key findings from this study are that (1) Secondary EHR data can be used to estimate lower bounds on provider EHR use, (2) Providers spend a large amount of time using the EHR, (3) Most time spent on the EHR is spent reviewing information. These findings have important implications for practicing clinicians, and for EHR system design in the future.

Ophthalmic imaging in children: current practice patterns and perceived barriers.

Pediatric ophthalmologists were surveyed to determine current practice patterns regarding ophthalmic imaging for children and to identify perceived barriers to the adoption of imaging technologies in their practices. Some form of imaging was available in the majority of practices (94%), but its use varied widely among different clinical scenarios. The two most frequently perceived barriers to performing imaging in children were cooperation and lack of sufficient data supporting ophthalmic imaging in clinical practice.

Secondary use of electronic health record data for clinical workflow analysis.

Objective: Outpatient clinics lack guidance for tackling modern efficiency and productivity demands. Workflow studies require large amounts of timing data that are prohibitively expensive to collect through observation or tracking devices. Electronic health records (EHRs) contain a vast amount of timing data - timestamps collected during regular use - that can be mapped to workflow steps. This study validates using EHR timestamp data to predict outpatient ophthalmology clinic workflow timings at Oregon Health and Science University and demonstrates their usefulness in 3 different studies. Materials and Methods: Four outpatient ophthalmology clinics were observed to determine their workflows and to time each workflow step. EHR timestamps were mapped to the workflow steps and validated against the observed timings. Results: The EHR timestamp analysis produced times that were within 3 min of the observed times for >80% of the appointments. EHR use patterns affected the accuracy of using EHR timestamps to predict workflow times. Discussion: EHR timestamps provided a reasonable approximation of workflow and can be used for workflow studies. They can be used to create simulation models, analyze EHR use, and quantify the impact of trainees on workflow. Conclusion: The secondary use of EHR timestamp data is a valuable resource for clinical workflow studies. Sample timestamp data files and algorithms for processing them are provided and can be used as a template for more studies in other clinical specialties and settings.

Time Requirements for Electronic Health Record Use in an Academic Ophthalmology Center.

Importance: Electronic health record (EHR) systems have transformed the practice of medicine. However, physicians have raised concerns that EHR time requirements have negatively affected their productivity. Meanwhile, evolving approaches toward physician reimbursement will require additional documentation to measure quality and cost of care. To date, little quantitative analysis has rigorously studied these topics. Objective: To examine ophthalmologist time requirements for EHR use. Design, Setting, and Participants: A single-center cohort study was conducted between September 1, 2013, and December 31, 2016, among 27 stable departmental ophthalmologists (defined as attending ophthalmologists who worked at the study institution for ≥6 months before and after the study period). Ophthalmologists who did not have a standard clinical practice or who did not use the EHR were excluded. Exposures: Time stamps from the medical record and EHR audit log were analyzed to measure the length of time required by ophthalmologists for EHR use. Ophthalmologists underwent manual time-motion observation to measure the length of time spent directly with patients on the following 3 activities: EHR use, conversation, and examination. Main Outcomes and Measures: The study outcomes were time spent by ophthalmologists directly with patients on EHR use, conversation, and examination as well as total time required by ophthalmologists for EHR use. Results: Among the 27 ophthalmologists in this study (10 women and 17 men; mean [SD] age, 47.3 [10.7] years [median, 44; range, 34-73 years]) the mean (SD) total ophthalmologist examination time was 11.2 (6.3) minutes per patient, of which 3.0 (1.8) minutes (27% of the examination time) were spent on EHR use, 4.7 (4.2) minutes (42%) on conversation, and 3.5 (2.3) minutes (31%) on examination. Mean (SD) total ophthalmologist time spent using the EHR was 10.8 (5.0) minutes per encounter (range, 5.8-28.6 minutes). The typical ophthalmologist spent 3.7 hours using the EHR for a full day of clinic: 2.1 hours during examinations and 1.6 hours outside the clinic session. Linear mixed effects models showed a positive association between EHR use and billing level and a negative association between EHR use per encounter and clinic volume. Each additional encounter per clinic was associated with a decrease of 1.7 minutes (95% CI, -4.3 to 1.0) of EHR use time per encounter for ophthalmologists with high mean billing levels (adjusted R2 = 0.42; P = .01). Conclusions and Relevance: Ophthalmologists have limited time with patients during office visits, and EHR use requires a substantial portion of that time. There is variability in EHR use patterns among ophthalmologists.