Background: The Appalachian population is distinct, not just culturally and geographically but also in its health care needs, facing the most health care disparities in the United States. To meet these unique demands, Appalachian medical centers need an arsenal of analytics and data science tools with the foundation of a centralized data warehouse to transform health care data into actionable clinical interventions. However, this is an especially challenging task given the fragmented state of medical data within Appalachia and the need for integration of other types of data such as environmental, social, and economic with medical data. Objective: This paper aims to present the structure and process of the development of an integrated platform at a midlevel Appalachian academic medical center along with its initial uses. Methods: The Appalachian Informatics Platform was developed by the Appalachian Clinical and Translational Science Institute's Division of Clinical Informatics and consists of 4 major components: a centralized clinical data warehouse, modeling (statistical and machine learning), visualization, and model evaluation. Data from different clinical systems, billing systems, and state- or national-level data sets were integrated into a centralized data warehouse. The platform supports research efforts by enabling curation and analysis of data using the different components, as appropriate. Results: The Appalachian Informatics Platform is functional and has supported several research efforts since its implementation for a variety of purposes, such as increasing knowledge of the pathophysiology of diseases, risk identification, risk prediction, and health care resource utilization research and estimation of the economic impact of diseases. Conclusions: The platform provides an inexpensive yet seamless way to translate clinical and translational research ideas into clinical applications for regions similar to Appalachia that have limited resources and a largely rural population.

Introduction: Metabolic syndrome amplifies the risk of gestational diabetes, preeclampsia, and preterm labor in pregnant women. Similarly, women with substance use disorder have worsened obstetric and birth outcomes. Despite these two conditions being major healthcare disparities in Appalachia, the health outcomes of this cohort have not been studied thus far. This study looks at the health outcomes of this cohort. Method and Results: In this retrospective cohort study, we analyzed 27,955 mothers who delivered at Cabell Huntington Hospital between January 2010 and November 2021. We implemented Chi-square tests to determine the associations and multiple logistic regression methods for comparison after controlling for other factors, and found that MetS, together with SUD, significantly increases the risk as well as the number of pregnancy complications such as gestational diabetes (p-value < 0.001), preeclampsia (p-value < 0.001), premature rupture (p-value < 0.001), preterm labor (p-value < 0.001), and newborn disorder (p-value < 0.001) compared to the women who had none or had either MetS or SUD alone. Conclusion: Women with both metabolic syndrome and substance abuse had worsened pregnancy and neonatal outcomes compared to women with metabolic syndrome or SUD alone. In conclusion, analysis of all the variables is crucial to strategically planning and implementing health interventions that will positively influence the health outcome of the pregnant woman as well as the child.

Classical drug exposure: response studies in clinical pharmacology represent the quintessential prototype for Bench to Bedside-Clinical Translational Research. A fundamental premise of this approach is for a multidisciplinary team of researchers to design and execute complex, in-depth mechanistic studies conducted in relatively small groups of subjects. The infrastructure support for this genre of clinical research is not well-handled by scaling down of infrastructure used for large Phase III clinical trials. We describe a novel, integrated strategy, whose focus is to support and manage a study using an Information Hub, Communication Hub, and Data Hub design. This design is illustrated by an application to a series of varied projects sponsored by Special Clinical Centers of Research in chronic obstructive pulmonary disease at the University of Pittsburgh. In contrast to classical informatics support, it is readily scalable to large studies. Our experience suggests the culture consequences of research group self-empowerment is not only economically efficient but transformative to the research process.

Background/Objectives: Dysfunctions of metabolic syndrome (MetS) have been identified as a significant risk factor for colorectal cancer (CRC). However, current colon cancer guidelines do not classify patients with MetS as high risk, thereby leaving these individuals vulnerable. Consequently, we explored the relationship between MetS, its individual components, and the development of CRC in a cohort of patients with MetS to assess the necessity for CRC screening in these individuals. Methods: This study included patients ages 18 and older that received a service from the Marshall-Health (MH) practice plan, Cabell-Huntington Hospital (CHH), MU/JCESOM’s Edwards Comprehensive Cancer Center (ECCC), or the University of Kentucky HealthCare (UKHC) system between 2010 and 2018. We implemented log-binomial regression models to assess the individual and collective effects of MetS components after adjusting other CRC risk factors. Results: Given that CRC prevalence increases in the older population (aged 65 years and above), and that multiple components of MetS are observed within the same population, we analyzed the concurrent impact of all MetS components on CRC. Log-binomial regression models were implemented to assess the risk of CRC due to MetS components after adjusting other risk factors. Conclusions: We identified specific components that markedly increased CRC risk, suggesting that individuals with these components should be prioritized for early screening. These findings could significantly influence early CRC screening protocols, with the ultimate aim to reduce mortality associated with the disease.

Background: Caring for the growing dementia population with complex health care needs in West Virginia has been challenging due to its large, sizably rural-dwelling geriatric population and limited resource availability. Objective: This paper aims to illustrate the application of an informatics platform to drive dementia research and quality care through a preliminary study of benzodiazepine (BZD) prescription patterns and its effects on health care use by geriatric patients. Methods: The Maier Institute Data Mart, which contains clinical and billing data on patients aged 65 years and older (N=98,970) seen within our clinics and hospital, was created. Relevant variables were analyzed to identify BZD prescription patterns and calculate related charges and emergency department (ED) use. Results: Nearly one-third (4346/13,910, 31.24%) of patients with dementia received at least one BZD prescription, 20% more than those without dementia. More women than men received at least one BZD prescription. On average, patients with dementia and at least one BZD prescription sustained higher charges and visited the ED more often than those without one. Conclusions: The Appalachian Informatics Platform has the potential to enhance dementia care and research through a deeper understanding of dementia, data enrichment, risk identification, and care gap analysis.