Between-hospital care referrals for severe alcohol-related liver disease during the COVID-19 pandemic, 2020 to 2022.

Increased alcohol consumption during the coronavirus disease 2019 pandemic is projected to impact alcohol-related liver disease (ALD) morbidity and mortality. Inter-hospital escalation-of-care referral requests to our tertiary-care hepatology unit were analyzed from January 2020 through December 2022. Most requests to our center were for ALD with an increase in requests from intermediate care units, suggestive of higher acuity illness.

Reliability of visual review of intracranial electroencephalogram in identifying the seizure onset zone: A systematic review and implications for the accuracy of automated methods.

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.

Cancer stage at diagnosis: Comparison of insurance status in SEER to the Department of Defense Cancer Registry.

BACKGROUND: Military individuals, retirees, and their families have free care or minimal out-of-pocket costs in the US military health system (MHS). In contrast, out-of-pocket costs in the US general population vary substantially. This study compared cancer patients with various insurance types in the general population to those in the MHS in cancer stage at diagnosis. METHODS: Patients were identified from the US Department of Defense's (DoD) Automated Central Tumor Registry (ACTUR) and the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program. Tumor stage at diagnosis of breast, prostate, lung, and colon cancers during 2007-2013 was compared between ACTUR and SEER insurance categories of "insured," "insured-no specifics," "any Medicaid," and "uninsured," A multivariable logistic regression analysis estimated the odds ratio (OR) of late stage (Stages III and IV) versus early stage (Stages I and II) cancers comparing SEER insurance status to ACTUR. RESULTS: There were 18,440 eligible patients identified from ACTUR and 831,959 patients identified from SEER. For all cancer types, patients in the SEER-insured/no specifics, Medicaid, and uninsured groups had significantly greater likelihood of late stage diagnosis compared to ACTUR patients. The adjusted ORs were greatest among uninsured and Medicaid patients. The SEER-insured group also had a significantly higher odds of advanced stage disease than ACTUR patients for prostate cancer and lung cancer. CONCLUSION: Patients in the MHS with universal access to healthcare were diagnosed at an earlier stage than those in the general population. This difference was most evident compared to Medicaid and uninsured groups.

Use of Lean Six Sigma to Improve Access to Care in a Surgical Subspecialty Clinic.

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.