Digital Commons Network^™

Artificial Intelligence In Health Care: Business Opportunities And Ethical Challenges, Ankita Srivastava,, Marco Marabelli, Jeffrey Moriarty

Ethics Publication

Artificial intelligence (AI), and in particular generative AI (GAI), are making incredible progress toward automation. The pervasive and invasive nature of these technologies are affecting every industry, and health care is no exception. This paper summarizes insights derived from a panel that the Hoffman Center for Business Ethics and the Center for Health and Business at Bentley University hosted in March 2024. The panel invited three qualified health care professionals: Evan Carey, Susan Persky and John Torous. The panelists are all active in multiple aspects of AI in health care but represent a focus on the key areas of policy …

Chalkboards To Chatbots: Helping Faculty Harness Ai For The Future Of Higher Education, Justin C. Grace

Regis University Student Publications (comprehensive collection)

Integrating artificial intelligence (AI) into nursing education presented significant opportunities yet posed challenges due to varied faculty readiness. This Doctor of Nursing Practice (DNP) quality improvement (QI) project evaluated an educational intervention aimed at enhancing nursing faculty's AI proficiency and confidence at Regis University’s Rueckert-Hartman College for Health Professions. Using a mixed-methods, pre- and post-intervention design, validated surveys assessed changes in faculty perceptions, knowledge, and skills related to AI. The intervention included a digital toolkit with nine instructional videos demonstrating practical AI applications using FreedAI’s large language model, ChatGPT, supported by voiceover narration and closed captioning. Data analysis involved descriptive …

Dynamical Measures Of Developing Neuroelectric Fields In Emerging Consciousness, William Bosl, Jenny R. Capua Shenkar

Nursing and Health Professions Faculty Research and Publications

No abstract provided.

Examining The Relationship Between Teen Pregnancy And Educational Outcomes In Ohio, Andrea Szep, Maryyam Durrani

Scholarship in Medicine - All Papers

Objective: The aim of this study is to explore the relationship between educational outcomes,school funding, and teen birth rates in Ohio counties between 2020 and 2024. Methods: Usingdata from County Health Rankings, we performed regression, correlation, and paired t-testanalyses through the SPSS software. Results: The results of our tests showed significantnegative correlations between markers of educational success, including high school graduationrates math and reading standardized test scores, and teen birth rates in both 2020 and 2024.Between 2020 and 2024, the teen birth rate decreased in the state of Ohio, and in 2024, publicschool funding was able to predict 14.8% of …

The Impacts Of Artificial Intelligence In Radiology, Misty Farmer, Wendy Trzyna

Theses, Dissertations and Capstones

Introduction: There has been significant growth in the use of Artificial Intelligence (AI) in the healthcare industry, especially in Medical Imaging. Radiology has been the clear frontrunner in the adoption of AI in medicine, due in part to the massive amount of digital data available for use in Deep Learning (DL) AI integration has the potential to solve multiple challenges in radiology, address workload issues and transform the field.

Purpose of the Study: The purpose of the research was to evaluate the impact of implementing Artificial Intelligence in radiology to determine if these technologies have had an impact …

A Survey-Based Quantitative Analysis Of Stress Factors And Their Impacts Among Cybersecurity Professionals, Sunil Arora, John D. Hastings

Research & Publications

This study investigates the prevalence and underlying causes of work-related stress and burnout among cybersecurity professionals using a quantitative survey approach guided by the Job Demands-Resources model. Analysis of responses from 50 cybersecurity practitioners reveals an alarming reality: 44% report experiencing severe work-related stress and burnout, while an additional 28% are uncertain about their condition. The demanding nature of cybersecurity roles, unrealistic expectations, and unsupportive organizational cultures emerge as primary factors fueling this crisis. Notably, 66% of respondents perceive cybersecurity jobs as more stressful than other IT positions, with 84% facing additional challenges due to the pandemic and recent high-profile …

"Dental Health For Mental Wealth": A Comprehensive Analysis Of The Underexplored Impact Of Oral Health And Behaviors On Neurobiology And Psychological Well-Being, Brooke C. Beck

Mahurin Honors College Capstone Experience/Thesis Projects

This thesis explores the link between oral health and psychological state, specifically, the effect of oral health on psychological state and how appropriate oral health impacts this relationship. A review of the literature was conducted and studies interpreted within the scope of oral health’s impact on psychological health and detailing some of the pathways proposed for such a link. The literature search was limited to the English language from well-known databases mainly from 2015 to 2025. A few outliers are present to include foundational information.

The effects of oral disease and oral self-care behaviors were dissected through a biochemical perspective, …

Artificial Intelligence In Health Care: Business Opportunities And Ethical Challenges, Ankita Srivastava, Marco Marabelli, Jeffrey Moriarty

Philosophy Faculty Publications

Artificial intelligence (AI), and in particular generative AI (GAI), are making incredible progress toward automation. The pervasive and invasive nature of these technologies are affecting every industry, and health care is no exception. This paper summarizes insights derived from a panel that the Hoffman Center for Business Ethics and the Center for Health and Business at Bentley University hosted in March 2024. The panel invited three qualified health care professionals: Evan Carey, Susan Persky and John Torous. The panelists are all active in multiple aspects of AI in health care but represent a focus on the key areas of policy …

Improving Dental Hygiene Students' Knowledge, Attitudes, And Confidence Toward Prenatal Oral Health Through Experiential Learning, Denise C. Mckinney, Sinjini Sikdar, Shillpa Naavaal

Dental Public Health and Policy Publications

Purpose Maintaining optimal oral health is essential for maternal health as well as the health of their child. The purpose of this pilot study was to examine dental hygiene (DH) students' knowledge of prenatal oral health before and after receiving an asynchronous prenatal oral health care training (POHCT), and their attitudes and self-perceived confidence before and after receiving an experiential learning opportunity with a pregnant and/or post-partum client at a Women's Infants and Children nutrition program (WIC) clinic.

Methods A one-group pre/post-test design was used. This IRB-approved study (#2027439-4) utilized a convenience sample of DH student participants (n=37) in their …

"I Wish I Knew That”: Understanding And Bridging The Gap Between Public Health, Stem, And The Cooperative Extensions National Framework For Health Equity And Well-Being, Brione Lockett, Lisa Krajecki

Extension Publications

In his essay “The Creative Process” writer and activist James Baldwin notes, “It goes without saying . . . that if we understand ourselves better we would damage ourselves less” (Baldwin, 1985). This quotation reflects Baldwin’s viewpoint that often individuals knowing and learning about themselves and what impacts them (e.g., education, employment, social environments, policy) and their quality of life is compromised by the action of seeking to know others instead. This paper meets at the intersection of Baldwin’s profound words and the ways health equity and wellness conversations complement rather than compete with STEM fields. The National Framework for …

A Teacher Preparation Program's Stakeholders' Perceptions Since Campus Closure Due To Covid-19, Laurie J. Mccormick

Theses, Dissertations and Capstones

COVID-19 closed colleges and forced faculty and students to learn a new way of teaching and learning. Like most institutions, Marshall University’s College of Education and Professional Development struggled to provide the practicum experience required for teacher preparation when most public schools shifted to remote learning. This qualitative study investigated the reactions of, and ongoing impact on, a university’s college of education’s administrators, faculty, undergraduates, the institution’s instructional designers, and the Counseling Center’s director when the campus closed spring of 2020 due to the pandemic. Initial and ongoing challenges faced, and strategies implemented were categorized by the four themes that …

Associations Between Older Hispanics’ Diabetes Control And Their Caregiver’S Health Literacy, Jorge Rico

Nursing Dissertations - Archive

Background: Older Spanish-speaking Hispanics are a rapidly growing population in the United States that face disproportionally high rates of diabetes and inadequate health literacy. Familism is valued among Hispanics and caregivers may play an important role in overcoming the social determinants of health facing this vulnerable population. Methods: This descriptive correlation study sought to explore correlations between the caregiver’s health literacy and the patient’s control of diabetes. Findings: Mixed effects model found a significant correlation between the caregiver’s print literacy and the patient’s hemoglobin A1c. Conclusion: The findings are encouraging for future research to explore interventions that target the caregivers …

Decreasing Hospital-Acquired Clostridium Difficile Infections, Angel S. Louder

Doctor of Nursing Practice (DNP) Scholarly Projects - Archive

Clostridium Difficle Infection (CDI) is a significant healthcare threat due to its ability to be transmitted rapidly in healthcare settings. Patient engagement and participation in infection prevention initiatives at admission will lead to early implementation of isolation precautions, diagnosis of infectious pathogens, and treatment. A quality improvement project was implemented at a south-central Texas acute care hospital to engage alert and oriented adult patients, using a bundled approach to screen for the early detection of CDI within 48 hours of hospital admission, thereby reaching the goal of reducing hospital-acquired CDIs. This initiative improved patient engagement with CDI screening, reducing confirmed …

Quantifying Multidimensional Effects Of Physicochemical Parameters On Pfas Adsorption Using A Hybrid Response Surface Methodology-Machine Learning Approach, Harsh V. Patel, Jazmin Green, John Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Engineering Management & Systems Engineering Faculty Publications

Per- and polyfluoroalkyl substances (PFAS) contamination has posed a significant environmental and public health challenge due to their ubiquitous nature. Adsorption has emerged as a promising remediation technique, yet optimizing adsorption efficiency remains complex due to the diverse physicochemical properties of PFAS and the wide range of adsorbent materials. Traditional modeling approaches, such as response surface methodology (RSM), struggled to capture nonlinear interactions, while standalone machine learning (ML) models required extensive datasets. This study addressed these limitations by developing hybrid RSM-ML models to improve the prediction and optimization of PFAS adsorption. A comprehensive dataset was constructed using experimental adsorption data, …

Artificial Intelligence In Health Care: Business Opportunities And Ethical Challenges, Ankita Srivastava, Marco Marabelli, Jeffrey Moriarty

Computer Information Systems Faculty Publications

Artificial intelligence (AI), and in particular generative AI (GAI), are making incredible progress toward automation. The pervasive and invasive nature of these technologies are affecting every industry, and health care is no exception. This paper summarizes insights derived from a panel that the Hoffman Center for Business Ethics and the Center for Health and Business at Bentley University hosted in March 2024. The panel invited three qualified health care professionals: Evan Carey, Susan Persky and John Torous. The panelists are all active in multiple aspects of AI in health care but represent a focus on the key areas of policy …

Development And Characterization Of Polysaccharide-Based Controlled Drug Delivery Platforms, Amanda Pepler

Open Access Dissertations

Drug delivery research focuses on overcoming challenges in patient treatment. Most therapeutics are delivered systemically through oral or intravenous routes. However, these administration methods are often inefficient due to therapeutic hydrophobicity, poor bioavailability, and liver metabolic clearance, necessitating frequent dosing. When treatment plans require frequent dosing, patients experience significant fluctuations in therapeutic concentration in the bloodstream, which can increase adverse side effects. These factors contribute to low patient compliance. Therefore, the core focus of drug delivery research is to develop new administration routes for therapeutics. Drug delivery systems (DDS) that can target therapeutics to a localized region offer advantages over …

Explainable Diagnosis Prediction Through Neuro-Symbolic Integration, Qiuhao Lu, Rui Li, Elham Sagheb, Andrew Wen, Jinlian Wang, Liwei Wang, Jungwei W Fan, Hongfang Liu

Faculty, Staff and Student Publications

Diagnosis prediction is a critical task in healthcare, where timely and accurate identification of medical conditions can significantly impact patient outcomes. Traditional machine learning and deep learning models have achieved notable success in this domain but often lack interpretability which is a crucial requirement in clinical settings. In this study, we explore the use of neuro-symbolic methods, specifically Logical Neural Networks (LNNs), to develop explainable models for diagnosis prediction. Essentially, we design and implement LNN-based models that integrate domain-specific knowledge through logical rules with learnable weights and thresholds. Our models, particularly Mmulti-pathway and Mcomprehensive, demonstrate superior performance over traditional models …

Unraveling Complex Temporal Patterns In Ehrs Via Robust Irregular Tensor Factorization, Ren Yifei, Linghui Zeng, Jian Lou, Li Xiong, Joyce C Ho, Xiaoqian Jiang, Sivasubramanium V Bhavani

Faculty, Staff and Student Publications

Electronic health records (EHRs) contain diverse patient data with varying visit frequencies. While irregular tensor factorization techniques such as PARAFAC2 have been used for extracting meaningful medical concepts from EHRs, existing methods fail to capture non-linear and complex temporal patterns and struggle with missing entries. In this paper, we propose REPAR, an RNN REgularized Robust PARAFAC2 method to model complex temporal dependencies and enhance robustness in the presence of missing data. Our approach employs Recurrent Neural Networks (RNNs) for temporal regularization and a low-rank constraint for robustness, enabling precise patient subgroup identification and improved clinical decision-making in noisy EHR data. …

Lowering The Barrier For Reading Primary Literature In Stem, Sebastian G. Alvarado, Maral Tajerian

Open Educational Resources

Building Bridges of Knowledge (BBK) is a cross‑CUNY initiative that integrates artificial intelligence (AI) and open educational resources to lower the barrier to primary literature and enhance research‑focused pedagogy. Recognizing that scientific papers are often dense and inaccessible to newcomers, we developed a suite of AI‑assisted instructional materials and activities to help students interpret complex data and methodology. A central component is the use of large language models (LLMs) and tools such as Scite.ai to provide citation‑context analytics and personalized research support. These tools allow students to filter literature by supportive, neutral, or contrasting citation statements and to troubleshoot laboratory …

Principles Of Biomedical Research, Faina Linkov, Matthew C. Kostek

Open Education Materials

HLT 430/530

CC BY-NC-SA

Reframing Recovery: Comparing Music Interventions And Traditional Rehabilitation Methods For Cognitive Function In Traumatic Brain Injury Patients, Laura Velez

Honors Undergraduate Theses

Traumatic brain injury (TBI) affects millions annually, often resulting in long-term cognitive deficits that hinder independence and quality of life. While traditional rehabilitation methods address many physical and functional limitations, cognitive impairments—such as deficits in attention, memory, and executive function—often persist. Emerging evidence highlights music-based interventions as a promising complement to standard therapies, yet comparative research remains limited. This integrative literature review examined the effects of music interventions versus traditional rehabilitation methods on cognitive function in adults with TBI. Using a systematic search of databases including MEDLINE, CINAHL Ultimate, APA PsycInfo, and Music Index, eight peer-reviewed studies were selected for …

Consequences Of Artificial Intelligence In Health Insurance: Lawsuits, Policy, And Ethics, Alyssa N. Roberts

Honors Undergraduate Theses

In recent years, the healthcare system has been burdened by a multitude of obstacles that hinder the ability to provide effective, affordable, and timely care. Among these, one of the most significant challenges is the role that health insurance plays in shaping the quality of care. Health insurance companies are designed to decrease financial strain on patients, but they have introduced inefficiencies through delayed coverage approvals, increased denials, and administrative costs. Artificial intelligence (AI) has started to play an integral role in resolving these issues for the health insurance industry. Through its quick automated claim processing, fraud screening, and reduced …

Enhancing The Accuracy Of Image Classification For Degenerative Brain Diseases With Cnn Ensemble Models Using Mel-Spectrograms, Sang-Ha Sung, Michael Pokojovy, Do-Young Kang, Woo-Yong Bae, Yeon-Jae Hong, Sangjin Kim

Mathematics & Statistics Faculty Publications

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are prevalent neurodegenerative disorders among the elderly, leading to cognitive decline and motor impairments. As the population ages, the prevalence of these neurodegenerative disorders is increasing, providing motivation for active research in this area. However, most studies are conducted using brain imaging, with relatively few studies utilizing voice data. Using voice data offers advantages in accessibility compared to brain imaging analysis. This study introduces a novel ensemble-based classification model that utilizes Mel spectrograms and Convolutional Neural Networks (CNNs) to distinguish between healthy individuals (NM), AD, and PD patients. A total of 700 voice …

Covariate Selection For Rna-Seq Differential Expression Analysis With Hidden Factor Adjustment, Farzana Noorzahan, Hyeongseon Jeon, Yet Nguyen

Mathematics & Statistics Faculty Publications

In RNA-seq data analysis, a primary objective is the identification of differentially expressed genes, which are genes that exhibit varying expression levels across different conditions of interest. It is widely known that hidden factors, such as batch effects, can substantially influence the differential expression analysis. Furthermore, apart from the primary factor of interest and unforeseen artifacts, an RNA-seq experiment typically contains multiple measured covariates, some of which may significantly affect gene expression levels, while others may not. Existing methods either address the covariate selection or the unknown artifacts separately. In this study, we investigate two integrated strategies, FSR_sva and SVAall_FSR, …

The Impact And Perceptions Of Open Educational Resources And Oer-Enabled Pedagogy On Affordability, Enrollment, And Student Success In Undergraduate Anatomy And Physiology, Justin Burr

Dissertations and Theses

Open educational resources (OER) can help improve the affordability of higher education. While the impacts of OER on student success and cost savings have been studied in some areas, the literature lacks evidence for undergraduate anatomy and physiology. Additionally, the potential impact OER has on student enrollment choices in undergraduate anatomy and physiology remains unstudied. The cost of textbooks has been a notable hurdle to higher education participation and success, particularly among minority and underserved populations. This study aimed to evaluate the impacts of OER on affordability (cost), access (enrollment), and student success. A mixed-methods approach was used to answer …

A Predictive Quality Assurance Model For Patient-Specific Gamma Passing Rate Of Hyperarc-Based Stereotactic Radiotherapy And Radiosurgery Of Brain Metastases, Shane Mccarthy, Brent Harrison, Damodar Pokhrel

Radiation Medicine Faculty Publications

Objective: Measurement-based patient specific quality assurance (PSQA) is an increasingly debated topic among medical physicists. Developments like online adaptive radiotherapy and same-day stereotactic treatments limit the time to do measurement-based PSQA. Herein, we develop a predictive machine learning model to supplement PSQA by predicting the gamma passing rate (GPR) per stereotactic arc. This streamlines PSQA, providing planners the insight to replan potentially sub-optimal plans, to mitigate machine time inefficiencies.

Methods: 122 patients that had previously received HyperArc stereotactic radiosurgery/radiotherapy on a TrueBeam LINAC (Millenium 120 MLCs, 6MV-FFF) were used to generate a long short-term memory (LSTM) recurrent neural network to …

Analysis Of Errors In The Management Of Cutaneous Disorders, Robert J. Pariser, Sarah Alnaif

Department Dermatology Faculty Publications

In this study, we prospectively and retrospectively evaluated the occurrence of errors in the management of cutaneous disorders from patient visits and medical records in a single dermatology practice in southeast Virginia over a 3-year period (June 2020-July 2023). Providers should be able to improve diagnostic accuracy by utilizing established rapid bedside diagnostic techniques.

Estimating Per-Infection Cost And Burden For Dengue And Zika As A Function Of Antibody-Dependent Enhancement, Christopher M. Kribs

Mathematics Faculty Publications - Archive

The complex immune interactions produced by the tetravalent dengue vaccine Dengvaxia have foregrounded the important role of antibody-dependent enhancement (ADE) in dengue infection. Some evidence exists that ADE may extend beyond the four dengue serotypes to Zika, a closely related flavivirus transmitted by the same mosquito species as dengue, and may also account for the increased severity of some cases. Estimates of the public health impact of dengue vaccination may then need to include its effects on the transmission of Zika in addition to dengue. This study gathers primary references to build estimates of per-case economic cost and disease burden …

Utilizing Β-Gal To Visualize Selective Transport Of The Ssa4 Gene In S.Cerevisiae, Ian M. Staertow, Rebecca L. Adams Phd

Science University Research Symposium (SURS)

In eukaryotic cells such as Saccharomyces cerevisiae, protein synthesis begins with the transport of mRNA from the nucleus to the cytoplasm. In order for mRNA to leave the nucleus, it must first bind to specific transport proteins which can then facilitate transport via the nuclear pore complex (NPC), a selectively permeable membrane within the cell's nuclear envelope. Under environmental stress, such as heat shock, S. cerevisiae cells modulate transport rates. In this case, in heat stress or “heat shock” most mRNA transcripts will remain in the nucleus. The SSA4 transcript encodes for a chaperone protein which facilitates the subsequent export …

Artificial Intelligence In Radiology, Olivia Sweeney

Theses, Dissertations and Capstones

Introduction: Artificial intelligence (AI) has increasingly transformed radiologic practice by improving diagnostic accuracy, streamlining workflows, and reducing interpretation errors. As AI integration has expanded across imaging modalities, questions have emerged regarding its effectiveness compared to traditional radiologist-only interpretation.

Purpose of Study: The purpose of this study has been to evaluate the impact of AI-assisted radiology on diagnostic accuracy, efficiency, and error reduction, while also assessing clinician perceptions of AI as a collaborative tool in imaging analysis.

Methodology: This qualitative study has used a systematic review of peer-reviewed literature published between 2015 and 2025, following PRISMA guidelines, combined with an interview …