Health Information Technology Commons^™

Closing Racial And Ethnic Disparity Gaps In Pediatric Asthma Follow Up Care - A Quality Improvement Initiative, Ayodele Ayeko

Graduate Theses, Dissertations, and Problem Reports (ETD)

ABSTRACT

Closing racial and ethnic disparity gaps in pediatric asthma follow up care - A quality improvement initiative

Ayodele Ayeko

Introduction/Background: Inadequate management of asthma significantly contributes to poor asthma control, poor quality of life (QOL), and high hospitalization rates among pediatric patients. The Global Initiative for Asthma (GINA) guidelines offer recommendations designed to enhance asthma control and improve the quality of life for those affected (2015), including recommendations for close follow up care. At WVU Medicine Children’s Hospital (WVUMCH), insufficient asthma control has been associated with lower clinic follow-up rates and an increase in hospitalization. Although educational initiatives …

Label Correlated Contrastive Learning For Medical Report Generation, Xinyao Liu, Junchang Xin, Bing Tian Dai, Qi Shen, Zhihong Huang, Zhiqiong Wang

Research Collection School Of Computing and Information Systems

Background and Objective: Automatic generation of medical reports reduces both the burden on radiologists and the possibility of errors due to the inexperience of radiologists. The model that utilizes attention mechanism and contrastive learning can generate medical reports by capturing both general and specific semantics. However, existing contrastive learning methods ignore the specificity of medical data, that is, a patient may suffer from multiple diseases at the same time. This means that the lack of fine-grained relationships for contrastive learning will lead to the problem of insufficient specificity. Methods: To address the above problem, a label correlated contrastive learning method …

The Reliability Gap: How Traditional Search Engines Outperform Artificial Intelligence (Ai) Chatbots In Rosacea Public Health Information Quality, Houston C. Nelson, Morgan T. Beauchamp, April A. Pace

Department of Medicine Faculty Publications

Background: The internet has become a primary source of health information for the public, with important implications for patient decision-making and public health outcomes. However, the quality and readability of this content vary widely. With the rise of generative artificial intelligence (AI) tools such as ChatGPT and Gemini, new challenges and opportunities have emerged in how patients access and interpret medical information.

Objective: To evaluate and compare the quality, credibility, and readability of consumer health information provided by traditional search engines (Google, Bing) and generative AI platforms (ChatGPT, Gemini) using three validated instruments: DISCERN, JAMA Benchmark Criteria, and Flesch-Kincaid Readability …

Personalized Prediction Of Tumor Recurrence With Image-Guided Physics-Informed Computational Model In High-Grade Gliomas, Walia Farzana, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

High grade gliomas are infiltrating tumors characterized by their diffusive invasion and proliferative growth. Across and within patients heterogeneity of tumors makes it challenging to determine tumor spatial extent after surgical resection. Traditionally, tumor growth predictions after surgical resections rely on generalized models and population-based observations, which do not account for individual patient differences. To address this gap, we propose a personalized approach with image-guided computational model (digital twin) that incorporates physics-based modeling to predict tumor recurrence. Our digital twin involves an inverse modeling step, followed by a recurrence model that accounts for varying surgical effects. The physics-guided inverse model …

Predicting Mental Health Disparities Using Machine Learning For African Americans In Southeastern Virginia, Ismail El Moudden, Michael C. Bittner, Matvey V. Karpov, Isaac O. Osunmakinde, Akosua Acheamponmaa, Breshell J. Nevels, Mamadou T. Mbaye, Tonya L. Fields, Karthiga Jordan, Messaoud Bahoura

Department of Obstetrics & Gynecology Faculty Publications

This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from African American adults (18–85) in Southeastern Virginia (2016–2020), we found Mood Affective Disorders were most prevalent (41.66%), followed by Schizophrenia Spectrum and Other Psychotic Disorders. Females predominantly experienced mood disorders, with patient ages typically ranging from late thirties to mid-forties. Medicare coverage was notably high among schizophrenia patients, while emergency admissions and comorbidities significantly impacted total healthcare charges. Machine learning models, including gradient boosting, random forest, neural networks, logistic regression, and Naive Bayes, were validated through 100 repeated 5-fold cross-validations. …

Mobile Health Intervention Tools Promoting Hiv Pre-Exposure Prophylaxis Among Adolescent Girls And Young Women In Sub-Saharan Africa: Scoping Review, Alex Emilio Fischer, Homaira Hanif, Jacob B. Stocks, Aimee E. Rochelle, Karen Dominguez, Eliana Gabriela Armora Langoni, H. Luz Mcnaughton Reyes, Gustavo F. Doncel, Kathryn E. Muessig

Department of Obstetrics & Gynecology Faculty Publications

Background:

In 2022, 3100 adolescent girls and young women in sub-Saharan Africa experienced new HIV infections each week. HIV pre-exposure prophylaxis (PrEP) is effective at preventing HIV but has limited uptake and persistence. Mobile health (mHealth) interventions can improve medication adherence; however, their utility to improve PrEP adherence among adolescent girls and young women is not well established.

Objective:

This scoping review synthesizes evidence supporting mHealth for PrEP among adolescent girls and young women in sub-Saharan Africa and identifies strategies for further evaluation.

Methods:

We searched PubMed and Google Scholar databases, expert referrals, and reference lists using the following eligibility …

Athletic Training Students' Use Of Health Information Technology Professional Behaviors During Clinical Experiences: A Report From The Aate Research Network, Cailee E. Welch Bacon, Julie M. Cavallario, Stacy E. Walker, R. Curtis Bay, Bonnie L. Van Lunen

Rehabilitation Sciences Faculty Publications

Context:

As an essential core competency for high-quality healthcare, health information technology (HIT) leverages the use of technology, such as electronic record systems, to collect and use patient care information to make informed clinical decisions. There is a dearth of information regarding professional athletic training students' (ATSs') use of HIT professional behaviors during clinical experiences.

Objective:

To assess HIT professional behaviors ATSs engage with during clinical experience patient encounters (PEs). DESIGN: Multisite panel design.

Setting:

12 professional athletic training programs.

Participants:

363 ATSs enrolled across the participating professional athletic training programs entered patient encounter information within E*Value during their scheduled …

Virtually Assisted Home Rehabilitation After Acute Stroke (Vast-Rehab): A Descriptive Pilot Study For Young And Underserved Stroke Survivors, Aylen Sosa, Mary E Russell, Sean I Savitz

Faculty, Staff and Student Publications

Introduction: The objective of Virtually Assisted home rehabilitation after acute STroke was to offer a fully remote telerehabilitation intervention to stroke survivors during the COVID-19 pandemic.

Methods: Participants were recruited from acute care, inpatient rehab, or provider/self-referral if they had a stroke within the previous year, prestroke modified Rankin Scale < 3, were recommended to participate in rehab, and had internet access. Exclusions were: prior injury/diagnoses that impacted functional level, life expectancy of less than six months, or safety concerns. Outcomes were within one week prior to initiation and within one week after completion of telerehabilitation sessions. Video call sessions were completed 1-2 times a week for 12 weeks. Occupational therapy, physical therapy, and/or speech therapy were provided. Analyses involved descriptive statistics; qualitative comments were aggregated and analyzed for broader themes. Twenty participants were consented.

Results: Of the 20 participants (39-71 years old, 35% White, 50% female), 12 completed study activities, five completed exit interview; 10 demonstrated improvement on National Institute of Health Stroke Scale from a mean of 5.6 at baseline to 2.7 at end of study, six demonstrated improvement in modified Rankin Score …