Patients with acute type A aortic dissection who arrive at hospitals that lack the facilities to treat them must be transferred to a tertiary care facility to receive treatment. The transfer process involves a checkpoint at which the transfer is accepted or denied. Delays in making this decision may lead to suboptimal health outcomes. In light of this, the goal of this project was to devise a way to reduce the time to decision of transfer requests for patients with an acute type A aortic dissection. The project followed the Define-Measure-Analyze-Improve-Control (DMAIC) approach. To better understand the process, data were obtained from the University of Texas Southwestern Medical Center regarding reasons for patient transfer cancellation and the average time until a transfer was denied or accepted. After data analysis, a fishbone diagram was used to display 23 root causes of the delays in time to decision of the transfer request. These were narrowed down to the following four significant causes using a nominal voting technique: (1) no standard on disease-specific information for the handoff, (2) lack of a real-time database, (3) incompatible electronic health record system between facilities, and (4) multiple communication handoffs causing confusion. Solutions to each root cause were evaluated using a solution selection matrix. The final two solutions proposed for implementation were as follows: (1) to establish checklists of required documents and patient transfer criteria and (2) to create a regional database to provide real-time information on hospital capacity.
Objective: University students experience many help-seeking barriers, and thus not all students who could benefit from mental health services enroll in them. This study aimed to examine student enrollment in response to strategic marketing of an online prevention program for anxiety and depression. Method: Data were collected from students at two universities during recruitment phases for the online program. The program was branded as either "The Happiness Challenge" or "ReBoot Camp" through parallel sets of recruitment materials using language intended to address help-seeking barrier concerns (e.g., stigma, inaccessibility). The yielded samples were examined for unaddressed psychological need rates, demographic composition, and differential enrollment by student subgroups into either program brand. Results: Replicated results between Study 1 (n ϭ 651 students; 71.2% undergraduate, 80.3% female, 27.9% White non-Hispanic) and Study 2 (n ϭ 718 students; 60.6% undergraduate, 73.4% female, 53.2% White non-Hispanic) showed that more than a third of students qualified as having "unmet need" for services, enrollment was disproportionately self-identified as female and Asian students, Asian students were less likely to report prior service use and more likely to be categorized as having "unmet need," and ReBoot Camp was disproportionately selected by male students. Conclusion: Findings suggest that recruitment effectively reached students with unaddressed mental health need, including high enrollment by Asian students, who historically seek services less often. Additionally, important gender differences emerged in preferences for program name. These findings could inform how to market services in university settings to reach more students, including those from underserved subgroups. What is the public health significance of this article?This study demonstrates how the advertising and branding of a psychosocial intervention can strategically attract students in need of services. Marketing may be one strategy to reach underserved student groups and more students with unmet need in general.
Purpose To establish optical coherence tomography (OCT)/angiography (OCTA) parameter ranges for healthy eyes (HE) and glaucomatous eyes (GE) for a North Texas based population; to develop a machine learning (ML) tool and to identify the most accurate diagnostic parameters for clinical glaucoma diagnosis. Patients and Methods In this retrospective cross-sectional study, we included 1371 eligible eyes, 462 HE and 909 GE (377 ocular hypertension, 160 mild, 156 moderate, 216 severe), from 735 subjects. Demographic data and full OCTA parameters were collected. A Kruskal–Wallis test was used to produce the normative database. Models were trained to solve a two-class problem (HE vs GE) and four-class problem (HE vs mild vs moderate vs severe GE). A rigorous nested, stratified, group, 5×10 fold cross-validation strategy was applied to partition the data. Six ML algorithms were compared using classical and deep learning approaches. Over 2500 ML models were optimized using random search, with performance compared using mean validation accuracy. Final performance was reported on held-out test data using accuracy and F1 score. Decision trees and feature importance were produced for the final model. Results We found differences across glaucoma severities for age, gender, hypertension, Black and Asian race, and all OCTA parameters, except foveal avascular zone area and perimeter ( p <0.05). The XGBoost algorithm achieved the highest test performance for both the two-class (F1 score 83.8%; accuracy 83.9%; standard deviation 0.03%) and four-class (F1 score 62.4%; accuracy 71.3%; standard deviation 0.013%) problem. A set of interpretable decision trees provided the most important predictors of the final model; inferior temporal and inferior hemisphere vessel density and peripapillary retinal nerve fiber layer thickness were identified as key diagnostic parameters. Conclusion This study established a normative database for our North Texas based population and created ML tools utilizing OCT/A that may aid clinicians in glaucoma management.
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