Introduction At the Naval Medical Center San Diego urology clinic, patients reported waiting for greater than 1 month for an initial consult. A Lean Six Sigma approach was used to improve access to care (ATC) and decrease variation in access by improving scheduling. Methods A Define-Measure-Analyze-Improve-Control approach was used. Delay to new patient visits was identified as the focus of intervention. The scheduling template was changed from a fixed stream to a modified wave based on simulation software analysis of appointment cycle times. Appointment length was adjusted based on cycle time analysis, and two rooms per clinician were used instead of one. The ratio of initial consults relative to established follow-ups and procedures was adjusted upward to better balance with the historic demand. Results Statistically significant improvement was seen in ATC and compliance with the Defense Health Agency (DHA) standard that new consults be seen within 28 days. Average days for a new consult to be seen were reduced by 7.2 days in the pediatric urology clinic (P < 0.0001) and 6.4 days in the adult urology clinic (P < 0.0001). Compliance with the Defense Health Agency 28-day ATC standard increased from a baseline of 69.2% to 88.9% and 61.7% to 84.4%, respectively, in the pediatric and adult clinics (P < 0.001 for both). Patient satisfaction was maintained at or above the goal threshold throughout the project. Conclusions An Lean Six Sigma model was used to improve timeliness of care for our patients, improving the overall quality of their healthcare experience. Simulation software can be used to model the clinic throughput and test alternative scheduling templates. ATC was significantly improved and patient satisfaction was maintained at or above goal thresholds.

Visual review of intracranial electroencephalography (iEEG) is often an essential component for defining the zone of resection for epilepsy surgery. Unsupervised approaches using machine and deep learning are being employed to identify seizure onset zones (SOZs). This prompts a more comprehensive understanding of the reliability of visual review as a reference standard. We sought to summarize existing evidence on the reliability of visual review of iEEG in defining the SOZ for patients undergoing surgical workup and understand its implications for algorithm accuracy for SOZ prediction. We performed a systematic literature review on the reliability of determining the SOZ by visual inspection of iEEG in accordance with best practices. Searches included MEDLINE, Embase, Cochrane Library, and Web of Science on May 8, 2022. We included studies with a quantitative reliability assessment within or between observers. Risk of bias assessment was performed with QUADAS‐2. A model was developed to estimate the effect of Cohen kappa on the maximum possible accuracy for any algorithm detecting the SOZ. Two thousand three hundred thirty‐eight articles were identified and evaluated, of which one met inclusion criteria. This study assessed reliability between two reviewers for 10 patients with temporal lobe epilepsy and found a kappa of .80. These limited data were used to model the maximum accuracy of automated methods. For a hypothetical algorithm that is 100% accurate to the ground truth, the maximum accuracy modeled with a Cohen kappa of .8 ranged from .60 to .85 (F‐2). The reliability of reviewing iEEG to localize the SOZ has been evaluated only in a small sample of patients with methodologic limitations. The ability of any algorithm to estimate the SOZ is notably limited by the reliability of iEEG interpretation. We acknowledge practical limitations of rigorous reliability analysis, and we propose design characteristics and study questions to further investigate reliability.

We compared publication rates of editor‐authored publications between journals that do not blind peer reviewers to author identity with one that does. Our hypothesis was that the if the identity of editors as authors is known to peer reviewers this may potentially bias the recommendation for publication. To do this, we queried Scopus for all publications from five top urology journals from 2013 to 2018, and linked them to a database of editors. Poisson regression analysis was used to compare publication rates of manuscripts with at least one editor as author between blinded journals and a non‐blinded journal. In separate analyses, we compared publication frequency before and after authors became editors and the frequency with which articles were cited. We found that the adjusted rate ratio of editor‐authored manuscripts comparing the non‐blinded journal to the blinded journal was 5.4 (95% CI 3.8–7.6) for ‘total publications’, and 1.9 (95% CI 1.5–2.2) among ‘articles only’. Median citation frequency was slightly higher among articles written by editors compared with non‐editors at 11 (3–26) versus 7 (2–16) (p < 0.001). We concluded that the blinded journal had a smaller representation of their editors as authors of their manuscripts, compared with the non‐blinded journals.