The CDU Center for Biomedical Informatics
The Urban Informatics Testbed
Urban, medically underserved communities manifest the highest burden of disease and the most challenged health care infrastructure in the United States. This means that Health Information Technology (HIT)-related improvements in service delivery have the greatest potential to improve the health and quality of life of these populations. However, as little is known about the feasibility, utility, or impact of HIT interventions in urban, medically underserved communities and urban safety-net health systems, the use of HIT to improve care quality merits specific evaluation. Researchers at the center will engage in systematic evaluations of HIT impact through the Urban Informatics Testbed, utilizing qualitative approaches grounded in sociotechnical theories as well as quantitative methods.
A testbed is a platform for experimentation for large development projects, which allows for transparent and replicable testing of scientific theories, computational tools, and new technologies. Two Testbed projects conducted by researchers at the Center are described below.
The CDU Electronic Disease Registry to Improve Chronic Care (CEDRIC)
To meet the challenge of improving health care quality in safety net clinics whose patients have a predominance of chronic diseases such as diabetes, we have developed a new, information system called CEDRIC. CEDRIC is a database-backed, Web-based system for managing chronic diseases in primary care safety net settings. It is implemented in C# and Java and utilizes the MySQL database management system, a robust, open-source database platform that is appropriate for community health centers that may not be able to keep up with the licensing costs of using proprietary database-management systems. The CEDRIC development project takes into account sociotechnical barriers to successful clinical information system implementation and forges academic-community partnerships with safety net clinics that often have a high physician turnover and a large uninsured/underinsured and homeless patient population. CEDRIC is designed to support workflow practices in community health centers, provide alerts and recommendations to physicians based on applicable clinical practice guidelines, and supply point-of-care summary reports regarding clinic encounters. In addition to producing patient-level summary reports that inform care continuity during clinic encounters, CEDRIC also includes functionality that enables disease-specific population-based summaries of outcomes, provider practices, and required patient outreach activities. The first incarnation of the system focuses on diabetes management and has been developed in a partnership between the Center for Biomedical Informatics and clinicians in the Family Medicine Clinic at the Hubert H. Humphrey Comprehensive Health Center in South Los Angeles. View CEDRIC-related publications here.
Diabetic Retinopathy Screening Using Telemedicine in South Los Angeles Safety Net Clinics
African Americans, Hispanic Americans, and the poor are at high risk for visual complications of diabetes. Overall, poor African Americans and Hispanics have the highest risk of vision loss due to diabetes. The most efficient and effective strategy for decreasing visual morbidity due to diabetes in inner-city communities is to increase access to timely surveillance (retinal examination), diagnosis, and treatment. A trial of point-of-service screening for diabetic retinopathy in community clinics using nonmydriatic digital cameras with transmission of digital images for review by board-certified ophthalmologists may increase the rate of retinal examination and improve the quality of eye care for high-risk diabetic patients attending inner-city primary care clinics. This NIH supported project assessed the impact of telemedicine screening on accurate and timely diagnosis of diabetic retinopathy in six of seven inner-city, primary-care clinics collectively termed the Southside Coalition. This effort builds on a previous project at CDU that developed an open-source, web-based telemedicine system for teleophthalmology screening (Wei JC, Valentino DJ, Bell DS, Baker RS. A Web-based telemedicine system for diabetic retinopathy screening using digital fundus photography. Telemed J E Health. 2006 Feb;12(1):50-7) and a qualitative study of patient perceptions of telemedicine (George SM, Hamilton A, Baker R. Pre-experience perceptions about telemedicine among African Americans and Latinos in South Central Los Angeles. Telemed J E Health. 2009 Jul-Aug;15(6):525-30). View publications related to the teleretinal screening project here
Geographical Information Science
Work at the center includes developing linkable data sets and analytical tools that facilitate research into the relationships between local environment and high-risk health behaviors, and health outcomes such as morbidity and mortality. These datasets have been developed so that geographic information on the social and physical environment will be easily linkable to existing individual level health-related data sources such as those produced by the National Center for Health Statistics (NCHS), as well as with similar state- and county-level health-related survey data sources. New data sources are created on a project-by-project basis where geo-coding, GPS surveying,or other data collection is required. In addition to the linkable data sources, a tool set of spatial statistical analysis scripts is under development. This will allow investigators to analyze, for example, how self-reported health behaviors and outcomes are affected by local public opportunities for exercise, nutritional contexts, and geographical accessibility to primary care.
Work in this area includes using data mining and machine learning methods on primary and secondary datasets for assessing predictors and outcomes of chronic diseases/conditions that disproportionately affect patients in the university's service area (e.g., chronic kidney disease). For example, abdominal obesity, dyslipidemia, elevated blood pressure, insulin resistance, prothrombotic state, and proinflammatory state are factors that predict metabolic syndrome in the general population. Data mining/machine learning efforts will assess whether other powerful predictors of metabolic syndrome exist for urban underserved populations.
Sociotechnical factors in biomedical informatics involve a focus on both the technical processes and the social systems operating within the health care environment to improve organizational performance. Because health care involves the tight interrelation of several elements in a system - such as people, tools, organizational routines, documents, etc. - the introduction of a new element such as health information technology can have reverberating consequences throughout the system. To better understand the impact of HIT implementation in such systems, evaluation needs to incorporate multimethod, longitudinal and systematic sociotechnical approaches that are able to capture the contingent and negotiated nature of the ongoing workflow despite the existence of predetermined task descriptions and formal procedures. Attention to sociotechnical factors is particularly important for the successful diffusion and acceptance of HIT in the Charles Drew University service area, given the "digital divide" that prevails among the multicultural and vulnerable patient populations served and the consequent challenges in the health care delivery context for patients, providers and organizations. Multiple methods are utilized in sociotechnical analyses, including quantitative techniques such as surveys, experiments and cost-benefit analyses as well as qualitative techniques such as participant observation, in-depth and focus group interviews and content analyses.
Through the CDU Accelerating eXcellence In translational Science (AXIS) grant and the UCLA CTSI, several new electronic resources have been made available to CDU researchers. These include CDU Profiles, a custom implementation of Profiles Research Networking Software that is designed to help CDU investigators find local and national collaborators, improve their research networks and build their CVs, and REDCap, a secure, web-based electronic data capture system. REDCap uses a MySQL database for data storage and facilitates dissemination and analysis of research study surveys as well as secure management of data from clinical trials, biospecimen repositories, and other clinical and basic science studies.