Institutional Repository

Welcome to the UT Southwestern Institutional Repository, which collects, preserves, and distributes digital material pertaining to the clinical, educational, and research missions of the university. Repositories are important tools for preserving an organization's legacy; they facilitate digital preservation and scholarly communication and encourage open access.

 

Communities in DSpace

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Now showing 1 - 3 of 3

Recent Submissions

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Promoting Employee Engagement in the Healthcare Setting Using Neuroaesthetics: A Pilot Initiative
(2024-04-26) Garduno Rapp, Estefanie; Bosler, Katherine; Crawford, Michelle; Valdez, Jovana; Crothers, Courtney; Cook, Allyson H.; Farmer, Suzanne
BACKGROUND: Healthcare professionals face numerous stressors in their roles, including heavy workloads and patient care responsibilities, which can impact their mental health. Neuroaesthetics, the scientific study of how contemplating artwork affects the brain, has shown that integrating art into hospital environments can boost morale among patients, staff, and visitors and potentially aid in healing processes and clinical outcomes. Providing activities like engaging with visual arts can promote resilience in the workplace and support overall employee well-being and engagement. OBJECTIVE: Our aim was to enhance workplace appreciation and employee engagement by offering a neuroaesthetics experience to 30 healthcare professionals. METHODS: We conducted a study with 30 healthcare professionals, including physicians, nurses, and administrators, at the University of Texas Southwestern Medical Center (UTSW). Participants were invited to take part in specialized art tours organized by Women in Art and the UTSW curator. These tours were held in the scientific and clinical buildings of UTSW, allowing participants to directly engage with strategically placed artworks in their professional environment. During the tours, participants received detailed explanations about the artworks, learned about the selection processes, engaged in discussions about color usage, and explored various artistic mediums. After the tours, participants completed an anonymous survey via REDCap to assess their perceptions of the tour's impact on workplace appreciation and well-being. The survey gathered qualitative data through written feedback about their experiences and quantitative data using a rating scale from 0 to 10 to measure overall tour satisfaction. RESULTS: Of the 30 participants 97 responded that the art tour enhanced their appreciation for the workplace and 70% of employees rated their art experience as a 10 on a scale from of 0-10. CONCLUSIONS: Our findings suggest that activities promoting resilience in the workplace can enhance employee engagement and foster a positive work environment. To capitalize on these benefits, future efforts should focus on expanding participation in similar initiatives, such as conducting more art tours and increasing awareness about them on campus.
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Building and Deploying a Cloud Environment for Hosting Custom Application Development Services Within an Academic Tertiary Center
(2023-09-15) Garduno Rapp, Estefanie; Hanna, John J.; Reeder, Jonathan
SIGNIFICANCE: The 21st Century Cures Act (Cures Act) is designed to help accelerate medical product development and bring new innovations and advances to patients who need them faster and more efficiently. Patients can now access their health information including radiology or pathology reports in near real-time. However, a few tools exist to allow patients to interpret these findings. OBJECTIVE: To design an application that enhances patient understanding of diagnostic descriptions by translating medical reports into lay terms. As well as, building and hosting an environment for custom application development services in our academic center. METHODS: We developed a web-based application in java that utilizes open AI to translate medical reports to layman terms. Posteriorly, we deployed our application within Microsoft Azure by building a static web app resource. Subsequently, through Visual Studio Code which was connected to our GitHub account. We downloaded an extension specifically designed to work with Azure to build and deploy static apps. By doing so, we were able to set up an authentication function that only allows access within our hospital network. RESULTS: The application can successfully translate medical jargon into layman terms and the deployment in azure enabled us to implement real-time changes whenever we pushed modifications in GitHub. CONCLUSIONS: The project was a proof of concept to demonstrate the possibilities of leveraging our organization's cloud services for development and hosting purposes. This demonstration serves as an illustration of the broader potential we have for building and hosting applications that can drive development towards a more patient-focused healthcare system within our hospital. By doing so, we facilitated a playground for medical professionals to develop meaningful tools that can bridge the gap between patients understanding and diagnostic information. KEYWORDS: Web-based applications; cloud services; digital technologies.
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Predicting Heart Disease Through Supervised Machine Learning Algorithms
(2022-09-09) Garduno Rapp, Estefanie
INTRODUCTION: Heart disease may present in a variety of forms including rhythm-disturbances, pump-failure, silent ischemia, angina, and sudden death among others. Early diagnosis is a crucial step to decrease serious cardiac events. Machine Learning (ML) is a promising tool to improve healthcare diagnostics and risk prediction in highly relevant and common illnesses such as cardiovascular disease. OBJECTIVE: To develop and evaluate three effective machine learning-supervised models to diagnose heart disease based on individual features. METHODS: We developed three machine learning models (Elastic net, logistic regression, and random forest) to identify individuals with heart disease. The discovery dataset used for model development included 303 subjects (138 with heart disease and 165 controls) and 14 predictor variables (including traditional cardiovascular risk factors). The outcome variable was the diagnosis of heart disease. The discovery dataset was split into training (70%), validation (10%), and testing (20%) subsets. Model development for elastic net and random forest was accomplished using the training and validation splits, whereas logistic regression was fit using only the training split. We selected hyperparameters for the elastic net model through cross validation and selected the predictors for logistic regression by backward stepwise selection. We calculated predictions using the testing split and evaluated the performance of the classifier based on the area under the receiver-operating-characteristic curve (AUC). Lastly, we used an external validation dataset (n=295, 107 cases and 188 controls) to make predictions. RESULTS: In the testing dataset, the elastic net model achieved AUC of 90% and accuracy of 86%; the logistic regression AUC was 95% and accuracy of 90%. For the random forest model, the Out-of-Box error was 25.21%; the number of variables used at each split were 3 and the accuracy in the testing test was 83%. When the model was confronted with an external validation dataset, the accuracy was 77%. CONCLUSION: We developed three models to evaluate ML performance with a discrete dataset. The logistic regression model outperformed the other models with an accuracy of 90% and an AUC of 95%. The final model included 6 variables: Sex, heart rate, exercise induced ST depression, and typical and atypical anginal pain and non-anginal pain. Future work on boosting techniques is required to improve the accuracy of the predictive model. Additionally, developing a comparison analysis between these ML models and conventional clinical approaches may help elucidate the net benefit.
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