Hypothesis Generation Using Network Structures on Community Health Center Cancer-Screening Performance

RESEARCH OBJECTIVES: Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. METHODS: To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. RESULTS: This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments

A Review on the Applications of Crowdsourcing in Human Pathology

The advent of the digital pathology has introduced new avenues of diagnostic medicine. Among them, crowdsourcing has attracted researchers' attention in the recent years, allowing them to engage thousands of untrained individuals in research and diagnosis. While there exist several articles in this regard, prior works have not collectively documented them. We, therefore, aim to review the applications of crowdsourcing in human pathology in a semi-systematic manner. We firstly, introduce a novel method to do a systematic search of the literature. Utilizing this method, we, then, collect hundreds of articles and screen them against a pre-defined set of criteria. Furthermore, we crowdsource part of the screening process, to examine another potential application of crowdsourcing. Finally, we review the selected articles and characterize the prior uses of crowdsourcing in pathology

State of Health Equity Movement, 2011 Update Part B: Catalog of Activities DRA Project Report No. 11-02

State of Health Equity Movement, 2011 Update Part B: Catalog of Activities DRA Project Report No. 11-0

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

AI in drug discovery and its clinical relevance

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.  Other InformationPublished in:HeliyonLicense: https://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://doi.org/10.1016/j.heliyon.2023.e17575 </p

ORED Communicator - February 2015

The February 2015 issue of the Office of Research and Economic Development newsletter.https://digitalcommons.fiu.edu/research_newsletter/1006/thumbnail.jp

Investing in The Health and Well-Being of Young Adults

This report was prepared to assist federal, state, and local policy makers and program leaders, as well as employers, nonprofit organizations, and other community partners, in developing and enhancing policies and programs to improve young adults' health, safety, and well-being. The report also suggests priorities for research to inform policy and programs for young adults.Young adulthood - ages approximately 18 to 26 - is a critical period of development with long-lasting implications for a person's economic security, health and well-being. Young adults are key contributors to the nation's workforce and military services and, since many are parents, to the healthy development of the next generation. Although 'millennials' have received attention in the popular media in recent years, young adults are too rarely treated as a distinct population in policy, programs, and research. Instead, they are often grouped with adolescents or, more often, with all adults. Currently, the nation is experiencing economic restructuring, widening inequality, a rapidly rising ratio of older adults, and an increasingly diverse population. The possible transformative effects of these features make focus on young adults especially important. A systematic approach to understanding and responding to the unique circumstances and needs of today's young adults can help to pave the way to a more productive and equitable tomorrow for young adults in particular and our society at large