Delaware journal of public health最新文献

Recent Developments in United States Vaccine Policy: A Narrative Review. 美国疫苗政策的最新发展：述评。

Delaware journal of public health Pub Date : 2026-03-31 eCollection Date: 2026-03-01 DOI: 10.32481/djph.2026.03.16

Suhani Bhatt, Katherine Smith

引用次数: 0

A Blueprint for Partnership between AI and MD. 人工智能与医学医学合作蓝图

Delaware journal of public health Pub Date : 2026-03-31 eCollection Date: 2026-03-01 DOI: 10.32481/djph.2026.03.07

Thomas Schwaab, Patrick Callahan

{"title":"A Blueprint for Partnership between AI and MD.","authors":"Thomas Schwaab, Patrick Callahan","doi":"10.32481/djph.2026.03.07","DOIUrl":"https://doi.org/10.32481/djph.2026.03.07","url":null,"abstract":"Healthcare delivery is experiencing a digital inflection point. Despite widespread adoption of electronic health records (EHRs) and expanding diagnostic technologies, clinicians increasingly report administrative overload, fragmented information systems, and reduced time for direct patient care. Data volume has increased, but clarity has not. This commentary proposes a four-pillar framework for transforming healthcare data from a source of cognitive burden into a driver of clinical, operational, and financial value. The framework includes: (1) early detection of clinical deterioration through AI-enabled analytics; (2) proactive operational adjustments using predictive capacity modeling; (3) population-level predictive capability to prevent avoidable hospitalizations; and (4) operational efficiency through automation of documentation and coding workflows. Rather than replacing physicians, artificial intelligence systems should function as intelligent assistants that synthesize data, reduce clerical burden, and support clinical judgment. We also discuss how integration of multi-omics data may further enhance early detection and personalized care. Moving from data fragmentation to actionable insight is not solely a technology challenge. It is a workforce sustainability issue and a public health priority.","PeriodicalId":72774,"journal":{"name":"Delaware journal of public health","volume":"12 1","pages":"28-30"},"PeriodicalIF":0.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147629304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model-Informed Drug Development: Addressing the Critical Need for Training in the Promising New Field. 基于模型的药物开发：解决有前途的新领域培训的迫切需要。

Delaware journal of public health Pub Date : 2026-03-31 eCollection Date: 2026-03-01 DOI: 10.32481/djph.2026.03.11

Yasaman Moghadamnia, Ryan Zurakowski, Mohammad Aminul Islam

{"title":"Model-Informed Drug Development: Addressing the Critical Need for Training in the Promising New Field.","authors":"Yasaman Moghadamnia, Ryan Zurakowski, Mohammad Aminul Islam","doi":"10.32481/djph.2026.03.11","DOIUrl":"https://doi.org/10.32481/djph.2026.03.11","url":null,"abstract":"The pharmaceutical industry faces a major challenge in drug discovery and development, with overall success rates of only 10-20%, often due to reductionist approaches that fail to account for complex biological networks. To address this challenge, industry and regulatory agencies, including the FDA, are increasingly adopting Model-Informed Drug Development (MIDD) and Quantitative Systems Pharmacology (QSP) to improve dose optimization, trial design, and decision-making throughout the drug development pipeline. At the same time, pharmaceutical investment in the United States, particularly in the greater Delaware region, is rapidly expanding, increasing the demand for a highly skilled workforce trained in advanced modeling and simulation. However, the widespread adoption of MIDD and QSP methodologies is hindered by a shortage of trained scientists, as traditional Biomedical Engineering curricula often lack the advanced mathematical and computational modeling preparation required by industry. To address this gap, the Biomedical Engineering department at the University of Delaware has integrated MIDD principles into its graduate curriculum and launched the nation's first Master's program dedicated to QSP, providing structured, industry-aligned training to prepare students for careers in the pharmaceutical and biotechnology industries.","PeriodicalId":72774,"journal":{"name":"Delaware journal of public health","volume":"12 1","pages":"68-71"},"PeriodicalIF":0.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147629312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Snapshot of Diabetes Risk, Risk Awareness, and Lifestyle Change Factors in Older Adults Attending Delaware Senior Centers. 特拉华州老年中心老年人糖尿病风险、风险意识和生活方式改变因素的快照。

Delaware journal of public health Pub Date : 2026-03-31 eCollection Date: 2026-03-01 DOI: 10.32481/djph.2026.03.13

Laurie Ruggiero, Elizabeth Orsega-Smith

{"title":"Snapshot of Diabetes Risk, Risk Awareness, and Lifestyle Change Factors in Older Adults Attending Delaware Senior Centers.","authors":"Laurie Ruggiero, Elizabeth Orsega-Smith","doi":"10.32481/djph.2026.03.13","DOIUrl":"https://doi.org/10.32481/djph.2026.03.13","url":null,"abstract":"Diabetes prevalence increases with age. Senior centers offer an opportunity to reach community-dwelling older adults to educate them about diabetes and its prevention. Objective. The objective of the study was to examine diabetes/pre-diabetes occurrence, risk factors, and awareness, and lifestyle behaviors; and compare lifestyle behaviors in three diabetes risk-related subgroups (diabetes, lower risk; higher risk) in older adults attending senior centers. Methods. A single occasion cross-sectional self-report survey was conducted at two Delaware senior centers. A total of 159 individuals participated in the survey. Results. Demographic characteristics were: 76.08 years old on average (SD = 7.89); 77.4% female; 1.9% Hispanic/Latino/Latinx; 84.2 White, and 13.3% Black/African American. Of this sample, 20.0% self-reported a diabetes diagnosis, 66.3% without known diabetes may have increased risk, and 29.8% were aware of their diabetes risk. Furthermore, more than half reported a lack of knowledge about pre-diabetes. For lifestyle behaviors, 73% reported being in the action/maintenance stages of change for physical activity, 53%-72% across areas of healthy eating, and 93% were nonsmokers. No significant differences were found between risk groups for these lifestyle areas. Conclusions/Policy Implications. These findings suggest potential gaps in older adults' awareness of diabetes risk and opportunities for promoting healthy lifestyle behaviors. Senior centers offer a convenient opportunity to reach older adults, to offer tailored approaches to address gaps in their awareness of pre-diabetes and diabetes risk, and to link individuals with current senior center, state, and other programs to further support diabetes prevention in older adults.","PeriodicalId":72774,"journal":{"name":"Delaware journal of public health","volume":"12 1","pages":"98-104"},"PeriodicalIF":0.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147629330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recent Advances in Modeling and Prediction of Blood Glucose in Type 1 Diabetes. 1型糖尿病血糖模型与预测的最新进展。

Delaware journal of public health Pub Date : 2026-03-31 eCollection Date: 2026-03-01 DOI: 10.32481/djph.2026.03.09

Yixiang Deng, Yiwei Kong, Xuechun Wang, He Li

{"title":"Recent Advances in Modeling and Prediction of Blood Glucose in Type 1 Diabetes.","authors":"Yixiang Deng, Yiwei Kong, Xuechun Wang, He Li","doi":"10.32481/djph.2026.03.09","DOIUrl":"https://doi.org/10.32481/djph.2026.03.09","url":null,"abstract":"Accurate prediction and control of blood glucose levels are essential for the management of type 1 diabetes, where patients rely on exogenous insulin and are vulnerable to both hypoglycemia and hyperglycemia. The widespread adoption of continuous glucose monitoring systems, insulin pumps, and wearable devices has generated large volumes of physiological and behavioral data, creating new opportunities for computational modeling and intelligent decision support. This review surveys recent advances in glucose prediction and control models, with a primary focus on type 1 diabetes. We examine three major classes of approaches: mechanistic models based on physiological principles, data-driven machine learning methods, and hybrid or biology-informed frameworks that integrate mechanistic knowledge with learning-based techniques. We also discuss the growing role of multimodal data, deep learning architectures, and reinforcement learning for automated insulin dosing and adaptive control in artificial pancreas systems. Despite significant progress, important challenges remain, including handling noisy and heterogeneous data, improving predictive reliability and uncertainty quantification, and enabling real-time deployment on resource-constrained medical devices. Emerging strategies such as edge computing, efficient model design, and hardware-algorithm co-optimization may help bridge this gap. Continued progress will require interdisciplinary collaboration, standardized evaluation on public datasets, and rigorous clinical validation to translate emerging modeling approaches into practical tools that improve patient outcomes.","PeriodicalId":72774,"journal":{"name":"Delaware journal of public health","volume":"12 1","pages":"46-53"},"PeriodicalIF":0.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147629333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harnessing AI for Transformative Healthcare: Proceedings and Strategic Roadmap from AI4Health Industry Day 2026 in Delaware. 利用人工智能实现医疗变革：特拉华州AI4Health Industry Day 2026的会议记录和战略路线图。

Delaware journal of public health Pub Date : 2026-03-31 eCollection Date: 2026-03-01 DOI: 10.32481/djph.2026.03.17

Celia Payen, Xi Peng, Weisong Shi, Patrick Callahan

{"title":"Harnessing AI for Transformative Healthcare: Proceedings and Strategic Roadmap from AI4Health Industry Day 2026 in Delaware.","authors":"Celia Payen, Xi Peng, Weisong Shi, Patrick Callahan","doi":"10.32481/djph.2026.03.17","DOIUrl":"https://doi.org/10.32481/djph.2026.03.17","url":null,"abstract":"Artificial intelligence (AI) is reshaping healthcare, offering new capabilities to improve specialty care delivery, reduce administrative burden, enhance operational efficiency, and accelerate biomedical discovery. Yet implementation remains constrained by workforce shortages, fragmented data infrastructure, governance requirements, and the need for responsible deployment aligned with patient-centered outcomes. AI4Health Industry Day 2026 convened 50-60 stakeholders from across Delaware's healthcare & Innovation ecosystem, including ChristianaCare, the Delaware Department of Health and Social Services (DHSS), the University of Delaware, NVIDIA, IBM, and emerging startups,to examine the current state of healthcare AI and identify pathways for scalable impact. This proceeding report synthesizes key themes spanning workforce analytics, robotics-enabled care operations, privacy-preserving machine learning, knowledge graph-driven discovery, and AI-accelerated gene editing. Panel discussions emphasized Delaware's Rural Health Transformation efforts and the importance of aligning innovation with access, cost, and equity priorities. We conclude with a strategic roadmap positioning Delaware as an emerging hub for responsible AI deployment in specialty care and public health.","PeriodicalId":72774,"journal":{"name":"Delaware journal of public health","volume":"12 1","pages":"122-126"},"PeriodicalIF":0.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048759/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147629357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Maria in 2035: Delaware as a Living Laboratory for AI-Enabled Public Health. 2035年的玛丽亚：特拉华州作为人工智能公共卫生的活实验室。

Delaware journal of public health Pub Date : 2026-03-31 eCollection Date: 2026-03-01 DOI: 10.32481/djph.2026.03.15

Neil G Hockstein, Patrick J Callahan

引用次数: 0

When Collaborative Robots Meet the Bedside: Nurses Informing Emerging Artificial Intelligence Technology. 当协作机器人来到床边：护士告知新兴的人工智能技术。

Delaware journal of public health Pub Date : 2026-03-31 eCollection Date: 2026-03-01 DOI: 10.32481/djph.2026.03.04

Susan D Smith, Danielle Weber

{"title":"When Collaborative Robots Meet the Bedside: Nurses Informing Emerging Artificial Intelligence Technology.","authors":"Susan D Smith, Danielle Weber","doi":"10.32481/djph.2026.03.04","DOIUrl":"https://doi.org/10.32481/djph.2026.03.04","url":null,"abstract":"Across the United States, clinicians working in the acute care hospital settings continue to face persistent workforce shortages. As artificial intelligence (AI) becomes increasingly integrated into healthcare delivery, it is critical to explore technology-enabled solutions that reduce non-clinical workload without compromising care. At ChristianaCare, a nurse-led team of executive and clinical leaders, researchers, and informaticians, launched a three-year, grant funded collaborative robot (cobot) pilot program starting in 2022 to evaluate whether cobots could offload repetitive, time-consuming non-clinical tasks from staff. At the conclusion of the grant period in 2025, the findings demonstrated operational value in select high-volume workflows, such as non-urgent medication and equipment deliveries. However, the pilot also revealed that current cobot capabilities were unable to meaningfully augment nurses' complex and often time-sensitive patient care workflows. These findings underscore the need for engineers and scientists to partner closely with nurses and frontline hospital staff throughout the design and implementation processes to ensure AI powered cobots deliver high impact, workforce-supporting solutions. Additionally, healthcare leaders should not underestimate their role in facilitating these collaborations and removing organizational barriers that could influence operational success.","PeriodicalId":72774,"journal":{"name":"Delaware journal of public health","volume":"12 1","pages":"10-11"},"PeriodicalIF":0.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147629411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bias Patterns in the Application of LLMs for Clinical Decision Support: A Comprehensive Study. 法学硕士应用于临床决策支持的偏倚模式：一项综合研究。

Delaware journal of public health Pub Date : 2026-03-31 eCollection Date: 2026-03-01 DOI: 10.32481/djph.2026.03.10

Raphael Poulain, Farzana Islam Adiba, Hamed Fayyaz, Rahmatollah Beheshti

{"title":"Bias Patterns in the Application of LLMs for Clinical Decision Support: A Comprehensive Study.","authors":"Raphael Poulain, Farzana Islam Adiba, Hamed Fayyaz, Rahmatollah Beheshti","doi":"10.32481/djph.2026.03.10","DOIUrl":"https://doi.org/10.32481/djph.2026.03.10","url":null,"abstract":"Objectives: To investigate the extent to which Large Language Models (LLMs) exhibit social bias based on protected patient attributes and to determine how design choices, such as architecture and prompting strategies, influence these observed biases in clinical decision support.Methods: We evaluated eight popular LLMs, including general-purpose and clinically trained models, across three standardized question-answering datasets using clinical vignettes. We employed red-teaming strategies to analyze the impact of demographics on LLM outputs and compared various prompting techniques, including Zero-shot and Chain of Thought.Results: Our experiments reveal various disparities across protected groups. Notably, larger models were not necessarily less biased, and medical fine-tuning did not consistently outperform general-purpose models. Furthermore, specific prompt phrasing significantly influenced bias patterns, whereas reflection-type approaches like Chain of Thought effectively reduced biased outcomes.Conclusions: LLMs demonstrate significant social biases in clinical scenarios that are influenced by model architecture and prompt engineering. These findings highlight the critical need for rigorous evaluation and enhancement of LLMs before their integration into clinical decision support systems. Consistent with prior studies, we call for additional scrutiny to ensure equity in AI-driven healthcare applications. All code and data are available at https://github.com/healthylaife/FairCDSLLM.","PeriodicalId":72774,"journal":{"name":"Delaware journal of public health","volume":"12 1","pages":"54-67"},"PeriodicalIF":0.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147629318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From the Guest Editors: Artificial Intelligence and Big Data in the Health Sciences. 特邀编辑：健康科学中的人工智能和大数据。

Delaware journal of public health Pub Date : 2026-03-31 eCollection Date: 2026-03-01 DOI: 10.32481/djph.2026.03.02

Weisong Shi, Yixiang Deng

引用次数: 0