Health Information Technology Commons^™

January 2026, Swosu Rural Health Center

RURAL ROCKS

• Age-Friendly Requirements for Hospital Quality Reporting
• APhA secures key congressional win allowing pharmacists to prescribe buprenorphine with specialized training
• "When the hospital leaves town"
• Med Spa Illicit GLP-1s | Supply Chain Safety
• CMS unveils new model aimed at functional, lifestyle medicine

Digital Technology And Women’S Health In Nigeria: A Feminist Sts Walkthrough Analysis Of Mdoc Digital Health, Edith Michael Ogbodo

Dissertations, Master's Theses and Master's Reports

This thesis critically examines how a Nigerian digital health platform, mDoc, represents and constructs women’s health. Drawing on Feminist Science and Technology Studies (STS) and Light et al.’s (2018) walkthrough method, the study interrogates the values, assumptions, and power relations embedded in mDoc’s design, language, and governance structures. The analysis reveals that mDoc, despite positioning itself as an equitable solution to Nigeria’s health crisis, reproduces and depoliticizes the structural inequalities it claims to address. Through its neoliberal emphasis on self-care, the individualization of structurally produced suffering, and the datafication of women’s bodies, the platform encodes gendered, colonial, and class-based assumptions …

Memoryart: Enhancing Llms Via Multi-Memory Models With Adaptive Resonance Theory For Healthcare Agents, Renke Dai, Hebin Hu, Jiahui Zhang, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Though promising in healthcare consultation applications, large language models (LLMs) face critical limitations in retaining and utilizing long-term memory across multiturn interactions. In particular, existing memory enhancing paradigms are constrained by limited context windows and embedding-based retrieval, often failing to maintain task relevance and still suffering from memory prototype collapse in multi-turn healthcare consultation. To address these challenges, we propose a cognitively-inspired memory framework named MemoryART, which is grounded in Adaptive Resonance Theory (ART)—a cognitive and learning theory of how humans and animals adapt to dynamic environments. MemoryART employs three memory modules—working memory, episodic memory, and semantic memory to support …

Analysis Theories On Artificial Intelligence, Chatgpt, Data Science, And Metaverse: The Case Of Digital Medicine, Yin Yang, Xingyun Liu, Jorge Luis Cuyubamba Dominguez, Yuan Fang, Wen Xie, Bairong Shen, Keng Siau

Research Collection School Of Computing and Information Systems

Healthcare organizations are increasingly adopting digital technologies, with Artificial Intelligence (AI), Data Science, and the metaverse driving significant advancements in smart healthcare. Al facilitates personalized medicine and efficient drug development, while Data Science enables predictive analytics and big data management, enhancing patient outcomes and healthcare quality. The metaverse introduces immersive training and telemedicine platforms, revolutionizing patient engagement and healthcare research. This study conducts' a scoping review of 6,171 articles, analyzing the transformational impact of AI, ChatGPT, Data Science, and the metaverse on healthcare. It highlights the benefits and risks of these technologies, identifies research gaps in their application within the …

Hubungan Persepsi Manfaat Terhadap Kesadaran Kesehatan Pengguna Wearable Device Di Kota Banda Aceh, Raihanil Fitri, Tahara Dilla Santi, Hafnidar A. Rani

Perilaku dan Promosi Kesehatan: Indonesian Journal of Health Promotion and Behavior

Background. Wearable devices are portable technologies designed to be worn directly on the body and capable of collecting and analyzing data related to various aspects of a user's health. Aim. This study aims to analyze the influence of perceived benefits on users' health awareness among wearable device users in Banda Aceh City.

Aim. This study aims to analyze association of perceived benefits on users' health awareness among wearable device users in Banda Aceh City.

Method. This research applied a descriptive quantitative design with a cross-sectional approach. Data were collected using a questionnaire from 106 active wearable device …

Using Machine Learning To Predict Women At Risk Having A Child With Congenital Heart Defects, Amany M. Abdo Prof., Asmaa M. Mosallam Ms., Laila M. Abdelhamid Assoc.Prof.

Information Systems

Congenital heart defects (CHD) are heart malformations present at birth, affecting heart function and circulation, and are a leading cause of infant mortality. CHD can result from genetic, environmental, and maternal health factors, making early detection essential. Early diagnosis allows for timely intervention, reducing risks like heart failure or stroke. In countries like Egypt, CHD often remains undiagnosed due to limited healthcare resources. Artificial intelligence (AI) can improve early detection by analyzing risk factors. This study presents a predictive model for CHD using maternal and paternal health factors. Data was collected from 571 families: 260 with a CHD-affected child and …

Mediapipe-Based Extraction Of Joint Rom And Position From Patient Videos For The Functional Movement Screen: An Exploratory Study, Joshua Paul Verdillo, Lily Ann Bautista

Philippine Journal of Physical Therapy

Introduction. The Functional Movement Screen (FMS) is widely used to identify movement deficiencies and potential injury risk. However, its reliance on visual inspection introduces subjectivity, requires specialized training, and necessitates face-to-face assessment. Advances in computer vision, offer opportunities to automate and better objectify functional movement assessment. This exploratory study aimed to evaluate the feasibility of using a MediaPipe-based system, Software-Optimized Movement Assessment (SOMA), to extract joint range of motion (ROM), joint position, and compensatory movement patterns from patient videos during FMS performance.

Methods. SOMA was developed using MediaPipe Pose and MediaPipe Hands libraries integrated with Python-based tools to analyze videos …

Personal Health Advice In The World Of Artificial Intelligence: An Assessment Of Responses From Chatgpt For Three Common Health Conditions In The Southern United States, Marion W. Evans Jr, Julie Harrison Swartz, Harrison Ndetan, Damian Francis, John Moore, Mary Kozub, Ernie Kaninjing, Jennifer Jones-Locklear, Debbie Greene, Josie Doss

Journal of Public Health in the Deep South

Background: Artificial intelligence (AI) applications within the health sciences including education and healthcare practice are now common. Patients are likely to apply AI to better understand their health status or to learn what they can do to improve their health.

Purpose: The purpose of this study was to evaluate outputs from ChatGPT for three common health conditions seen in the southern United States as to whether it would include medically biased care or lifestyle related advice, legitimate sources of information and to assess reading levels generated.

Methods: Health care professionals rated outputs in 4 areas and interrater …

Validating Pharmacogenomics Generative Artificial Intelligence Query Prompts Using Retrieval-Augmented Generation (Rag), Ashley Rector, Beth Breeden, Jay Dorris

Student Scholar Symposium

This study evaluated the performance of Sherpa Rx, an artificial intelligence platform leveraging large language models and retrieval-augmented generation (RAG) for pharmacogenomics, by validating its performance across key response metrics. Sherpa Rx integrated Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines with Pharmacogenomics Knowledgebase (PharmGKB) data to generate contextually relevant responses. A dataset (N=260 queries) spanning 26 CPIC guidelines was used to evaluate drug-gene interactions, dosing recommendations, and therapeutic implications. In Phase 1, only CPIC data was embedded; Phase 2 additionally incorporated PharmGKB. Responses were scored on accuracy, relevance, clarity, completeness (5-point Likert scale), and recall. Wilcoxon signed-rank tests compared accuracy between …

Developing Nlp-Based Open-Source Tools To Improve Medication Order Interpretation In Ehr Systems, Preslie Breton

Student Scholar Symposium

Interpreting free-text medication orders in Electronic Health Record (EHR) systems is challenging due to variability, increasing the risk of errors and non-compliance with Joint Commission (TJC) standards. This research develops Natural Language Processing (NLP) tools to enhance medication order accuracy, particularly for Duplicate PRN orders. Our open-source, vendor-neutral approach ensures broad applicability across healthcare institutions while maintaining data security.

Using Python and the Natural Language Toolkit (NLTK), we implemented a four-phase methodology. First, the NLP framework was developed on Google Colab for collaborative accessibility. Next, models were trained on 10,000 anonymized synthetic free-text medication orders to detect non-compliant entries. Model …

Implementation And Outcomes Of A Digital Onboarding Taskforce In The Acute Care Setting, Julianna Lenoir, Alexzandra Gentsch, Akshay Krishnan, Jeffrey Ndubisi, Marissa Witmer, Kristin L. Rising, Brooke Worster, Angela M. Gerolamo

Jefferson Center for Connected Care Faculty Papers

INTRODUCTION: Use of digital health technology can improve patient health outcomes; however, not all patients have the knowledge and skills to download a health app and access a patient portal. Providing digital onboarding support to hospitalized patients has potential to overcome some barriers to accessing needed education in the community, including both having the time and a location to receive education. To address this, our team developed the Jefferson Digital Onboarding Taskforce (JeffDOT), a group of staff and students who approach hospitalized patients and provide one-on-one teaching on how to sign up for and use a patient portal.

METHODS AND …

Evaluating The Impact Of System, Information, And Service Quality On User Outcomes In Malaysian Public Hospitals: A Structural Equation Modeling Approach, Muhd Siv Azhar Merican Abdullah, Azimatun Noor Aizuddin, Mohd Rizal Abdul Manaf

Makara Journal of Health Research

Background: Increasing use of hospital information systems (HIS) underscores the need to evaluate their impact on User Satisfaction and Perceived Net Benefits. This study investigated the connections among outsourced System Quality, Information Quality, Service Quality, Perceived Usefulness, User Satisfaction, and Perceived Net Benefits in the context of Malaysian public hospitals.

Methods: Data were collected from 1,376 healthcare personnel across six public hospitals and analyzed using structural equation modeling through analysis of moment structures grounded in the DeLone and McLean Information Systems Success Model.

Results: System and Service Qualities significantly influenced User Satisfaction and Perceived Usefulness, while Information Quality impacted …

Objective Vs. Perceived Maternal Smartphone Use And Observed Mother-Infant Interaction Quality, Alison K. Ventura, Lara N. Wolfers, Sarah M. Coyne, Rachel Pfafman, Sabrina Uva, Karla I. Ceja Almontes, Aniruddha S. Shinde, Adam M. Galovan, Brandon Mcdaniel

Health Services and Informatics Research

Introduction:

Prior research links maternal smartphone use to reduced sensitivity and responsiveness to infant cues, but emerging evidence suggests that mothers' perceptions of their smartphone use - such as feelings of empowerment or distress - may also shape the quality of mother-infant interactions. This study examined how both objective and perceived smartphone use related to mother-infant interaction quality in naturalistic settings.

Method:

Participants were 147 mother-infant dyads from a longitudinal study of smartphone use and infant development. When infants were 2 months old, mothers completed 5 days of ecological momentary assessment (EMA), reporting time spent with their infant, perceived smartphone-related …

Dental Care Utilization And Expenditures Among Individuals With Asthma, Shillpa Naaval, Vaibhavi Mone

Health Services and Informatics Research

Objectives To examine dental care utilization and health and dental care expenditures among individuals with asthma.

Methods Data from the 2022 Medical Expenditure Panel Survey (MEPS) (n = 21,405) were used to examine dental care utilization (any dental, preventive dental, and treatment dental visit) among individuals ≥ 1 year with and without asthma, and mean expenditures were summarized.

Results Overall, the asthma diagnosis was 13.2%, 14.7% in working-age adults compared to 9.5% in children. The percentage of treatment dental visits was higher overall (17.0% vs. 14.5%, p <  0.05) among the asthma group compared to the non-asthma group. Among individuals with asthma, those who had preventive dental visits had lower dental expenditures compared to those who had treatment dental visits for all age groups.

Conclusion Individuals with asthma had a higher prevalence of using dental treatment services. …

Referral Strategies For Recruitment And Engagement Of Community Adults Into Online Extension Health Programs, Cheryl L. Eschbach, Dawn A. Contreras, Holly B. Tiret

Journal of Human Sciences and Extension

This article shares details behind building organizational capacity to establish program referrals for statewide recruitment of community adults into Cooperative Extension’s online health programs. This study explores the role of intentional recruitment strategies (active and passive) to increase participant program enrollment into health education and shares examples from one state that has created a trained workforce that offers ongoing research- and evidence-based programs. After many years of trying paper, phone, and electronic medical record methods to increase program reach, Michigan State University Extension tried an electronic referral process via a QR code and web link, accompanied by program marketing, to …

December 2025, Swosu Bulldog Wellness Committee

SWOSU BULLDOG WELLNESS

• SWOSU Wellness 5k
• Employee Massages 12/4 & 12/5
• Flu Season
• Winter Crossword

Ataxia-Telangiectasia Mutated Activation Mediates Transforming Growth Factor Beta Signaling In Acetaminophen-Induced Liver Injury In Mice, Matthew Mcmillin, Christopher S Chu, Elaina Williams, Juliet Venter, Kiersten Bell, Anca D Petrescu, Patrick Mireles, Sharon Demorrow

Faculty and Staff Publications

Acetaminophen (APAP) overdose is associated with increased transforming growth factor beta 1 (TGFβ1) signaling and elevated oxidative stress, which exacerbate DNA damage. TGFβ1 has been shown to regulate ataxia-telangiectasia mutated (ATM) signaling and DNA repair in other cell types. This study investigates the DNA damage response (DDR) during APAP-induced liver injury, focusing on ATM-mediated regulation of TGFβ1 signaling. APAP administration in vitro and in vivo resulted in DNA damage, increased ATM signaling, accumulation of γH2AX, and activation of phosphorylated ataxia telangiectasia mutated (pATM) and phosphorylated checkpoint kinase 2 (pChk2). Pretreatment with an ATM inhibitor, KU55933, attenuated APAP-induced hepatocyte damage and …

Augmenting Large Language Models To Predict Social Determinants Of Mental Health In Opioid Use Disorder Using Patient Clinical Notes, Madhavi Pagare, Deva Sai Kumar Bheesetti, Inyene E. Essien-Aleksi, Mohammad Arif Ul Alam

Health Sciences Faculty Publications

Objective

Identifying social determinants of mental health (SDOMH) in patients with opioid use disorder (OUD) is crucial for estimating risk and enabling early intervention. Extracting such data from unstructured clinical notes is challenging due to annotation complexity and requires advanced natural language processing (NLP) techniques. We propose the Human-in-the-Loop Large Language Model Interaction for Annotation (HLLIA) framework, combined with a Multilevel Hierarchical Clinical-Longformer Embedding (MHCLE) algorithm, to annotate and predict SDOMH variables. Materials and Methods

We utilized 2636 annotated discharge summaries from the Medical Information Mart for Intensive Care (MIMIC-IV) dataset. High-quality annotations were ensured via a human-in-the-loop approach, refined …

A Study On The Influence Of Entity Internet Hospital Platforms And Health Literacy Facilitation On Postoperative Patients’ Platform Usage, Hong Ye

Dissertations and Theses Collection (Open Access)

Entity Internet Hospital (EIH) is emerging online medical service platforms in China. Currently, while EIHs in China are developing rapidly, their usage rates remain low. However, postoperative patients have substantial follow-up consultation needs. Consequently, incentivizing postoperative patients to use EIH from the demand side is a highly challenging proposition in the current era. Grounded in Technology Acceptance Model and Health Belief Model, this study examined the impacts of platform and health literacy facilitation on platform usage (including intention and frequency) among 631 postoperative patients with cardiovascular disease from four medical institutions in Wuhan, and explored the moderating role of literacy …

December 2025, Swosu Rural Health Center

RURAL ROCKS

• FDA MedWatch - 20 mEq Potassium Chloride Injection by Otsuka ICU Medical
• Plan to expand Medicare Coverage of GLP-1s
• Pharmacist Interventions in The US Oncology Network Cut Cancer Care Costs by Nearly $9 Million