Journal:Fueling clinical and translational research in Appalachia: Informatics platform approach

Full article title	Fueling clinical and translational research in Appalachia: Informatics platform approach
Journal	JMIR Medical Informatics
Author(s)	Cecchetti, Alfred A.; Bhardwaj, Niharika; Murughiyan, Usha; Kothakapu, Gouthami; Sundaram, Uma
Author affiliation(s)	Joan C. Edwards School of Medicine at Marshall University
Primary contact	Email: cecchetti at marshall dot edu
Year published	2020
Volume and issue	8(10)
Article #	e17962
DOI	10.2196/17962
ISSN	2291-9694
Distribution license	Creative Commons Attribution 4.0 International
Website	https://medinform.jmir.org/2020/10/e17962/
Download	https://medinform.jmir.org/2020/10/e17962/PDF (PDF)

Abstract

Background: The Appalachian population is distinct, not just culturally and geographically but also in its healthcare 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 healthcare 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 (AIP) was developed by the Appalachian Clinical and Translational Science Institute’s Division of Clinical Informatics and consists of four 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 AIP 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 healthcare 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.

Keywords: Appalachian region, medical informatics, health care disparities, electronic health records, data warehousing, data mining, data visualization, machine learning, data science

Introduction

Background: Unique challenges in Appalachia

Appalachia, with its predominantly rural communities, is known to have one of the worst sets of healthcare outcomes in the United States. This is especially true of southern and central rural Appalachia, which face some of the most severe health disparities in the nation. [1] Over the years, the gap in the overall health between Appalachia and the nation as a whole has continued to grow. [2,3] To close this gap, it is critical to identify the cause of these disparities and direct efforts toward developing necessary interventions to address them.

Such an effort necessitates the adoption of modern technologies such as a centralized research data warehouse to house all data necessary to obtain a comprehensive picture of the health of the Appalachian population before analysis to gain actionable insights can be performed. A centralized data warehouse, once considered strictly a business tool, has evolved into an important instrument for cost containment, tracking of patient outcomes, providing [Clinical decision support system|clinical decision support]] at the point of care, improving prognostic accuracy, and facilitating research. [4] Thus, rural academic medical centers have moved toward implementing data warehouse systems that feed analytical systems for research needs. [5] This entails (1) the integration of data from different types of medical settings (i.e., multi-institutional) such as hospitals, clinics, and specialty centers; (2) linkage of financial data with clinical data, a well-established practice proven to be pivotal to high-quality care and great economic outcomes [6,7]; and (3) integration of other determinants of health such as environmental [8], social [9], and spiritual factors [10] to create longitudinal health records across the care continuum.

However, there are challenges in creating a multi-institutional data warehouse. [11] Electronic health records (EHRs) do not easily interact with one another due to the use of nonstandard terminologies and difficulty in understanding the flow of information. Additionally, significant differences exist between rural and urban health systems. [12-16] Unlike their urban counterparts, healthcare data in Appalachia are typically fragmented, existing in silos within dissimilar databases, registries, data collections, and departmental systems. With innovations in medical technology, the list of data sources continues to grow, producing unprecedented amounts of data from all aspects of care, including diagnosis, medication, procedures, laboratory testing, imaging, and patient self-monitoring. [17-21] To complicate matters, the overall health and health behaviors of Appalachians are strongly affected by Appalachia’s unique culture, geography, and health system issues. [22-24] Consequently, Appalachian academic medical centers face the complex challenge of collecting, organizing, standardizing, and analyzing these enormous quantities of heterogeneous data originating from a wide variety of sources to address the unmet needs of the population they serve.

Why an informatics platform?

Data integration and interoperability have been shown to be key to unlocking these data for data analytics, enabling the development of novel patient management strategies for rural hospitals [25,26] and translational research that leads to new approaches at the bedside for prevention, diagnosis, and treatment of disease, which are essential to improving the health of a population. [27-29] Data analytics, once the domain of the statistician, has now become an equal partner in clinical research and research operations. [30,31] Following the data explosion, data analytics increasingly involves the use of visual analytics tools such as Tableau (Tableau Software Inc.) and Power BI (Microsoft Corp.) to explore data easily and in a self-service fashion and to clearly and effectively communicate complex ideas [32], especially to those members of the medical community who might not have an intimate understanding of the underlying data. Furthermore, machine learning is gaining importance, especially in the area of predictive analytics, to improve the practice of medicine and to infer potentially innovative risk factors. [28,33-35]

However, these applications (e.g., data warehouse, data analytics, statistical analysis, machine learning, visual analytics) are generally uncoordinated without any overarching governance. Thus, we developed an informatics platform—that is, a suite of interconnected, coordinated applications hosted within an operational environment [36]—called the Appalachian Informatics Platform (AIP), in West Virginia (the only state located entirely in Appalachia) that facilitates interoperable access to integrated information, data visualization, and data analytics, thereby functioning as an excellent basis for clinical and translational research to improve health care.

The goal of this study is to describe the structure and process of development of the AIP and demonstrate its value in supporting clinical and translational research.

Methods

The AIP (Figure 1) is composed of four major components: (1) a multi-institutional data storage or clinical data warehouse (CDW); (2) modeling tools (statistical and machine learning); (3) visualization tools; and (4) evaluation tools. Each of these components is described in detail in separate sections.

The CDW forms an integral part of the AIP. It also contains embedded data analytics (modeling and evaluation) and interactive visualization tools (e.g., Tableau [Tableau Software Inc], Power BI [Microsoft Corp]). Together, these enable the analysis of Appalachian health information to speed up the transition of translational research ideas into clinical practice.

The CDW serves as a secure source of quality data for descriptive, diagnostic, predictive, and prescriptive analytics for research and operational needs. The visual analytics tools enable an initial exploratory analysis of the processed data and the interactive presentation of analytical findings for further analysis and review. Depending on the use case, data can be analyzed using statistical modeling via external (e.g., SPSS [IBM Corp], Stata [StataCorp]) or integrated (e.g., [[R (programming language)|R] [R Foundation for Statistical Computing], Python [Python Software Foundation] in SQL [Structured Query Language]) applications or machine learning modeling. The performance of the resulting models was evaluated using appropriate metrics. Once trained and evaluated, machine learning models can be deployed and stored in the CDW for future use if needed. Furthermore, the stored machine learning models can be continuously evaluated and improved as more data are generated.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, grammar, and punctuation. In some cases important information was missing from the references, and that information was added.