Research

Faculty members of the CDU Center for Biomedical Informatics are recognized nationally and internationally for their research in a number of areas in this broad discipline.

Machine Learning

Machine learning involves the use of statistical theory and mathematics to create models that capture existing patterns in data and to utilize these patterns to solve new problems. Since it offers the ability to provide diagnostic and prognostic predictions that can help health workers as they make clinical decisions, machine learning plays an increasingly important role in Biomedical Informatics. Work at CDU in this area includes machine learning methods applied to 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, diabetic retinopathy).

Sociotechnical Studies

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 (HIT) 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.

Health Information Technology Standards

The Center provides leadership in standards development organizations such as Health Level Seven International as well as foundational research that helps support the development of standards for data representation and clinical decision support.

Computer-Based Clinical Decision Support

Members of the Center have engaged in extensive research activities in the domain of Clinical Decision Support (CDS) and provide leadership at the national and international level in this field. For example, the CDU Electronic Disease Registry to Improve Chronic Care (CEDRIC) project brought together community clinicians and members of the Center to create a registry to support clinical practice and provide CDS in the care of patients who suffer from diabetes mellitus. In addition, members of the Center lead efforts to develop health information technology standards to facilitate implementation of CDS and support knowledge sharing that can help reduce health disparities.

Selected Publications 

  1. Ogunyemi OI, George S, Mukherjee S, Gandhi M, Jenders RA. Centering “TechQuity” through Biomedical Informatics Centers at Minority-Serving Academic Institutions Providing Informatics Solutions for Urban Safety-Net Settings. J Health Care Poor Underserved, 2021 May; 32(2 Suppl): 300-317. [Full text]
  2. Ogunyemi OI, Gandhi M, Lee M, Teklehaimanot S, Daskivich LP, Hindman D, Lopez K, Taira R. Detecting Diabetic Retinopathy through Machine Learning on Electronic Health Record Data from an Urban, Safety Net Healthcare System. JAMIA Open. 2021 August 19;4(3):1 – 10. [Full text]
  3. Strasberg HR, Rhodes B, Del Fiol G, Jenders RA, Haug P, Kawamoto K.  Contemporary clinical decision support standards using Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR).  J Am Med Inform Assoc 2021; 28(8):1796-1806.
  4. Soares A, Jenders RA, Harrison R, Schilling L.  A comparison of Arden Syntax and the Clinical Quality Language.  Appl Clin Inform 2021;12:495–506.
  5. Novak L, George S, Wallston K, Vaughn Y, Israel T, Joosten Y, Williams N, Richmond A, Wilkins C. Patient stories can make a difference in patient-centered research design. Journal of Patient Experience. 2021. In press.
  6. George S,  Silva L, Nguyen N, Llamas M, Ramos I, Joe J, Mendez J, Salazar R, Tehan J, Vasquez T, Nealy S, Balcazar H. “The Development of a Novel, Standards-based Curriculum for Community Facing Clinic-based Community Health Workers” in Frontiers in Public Health, Public Health Education and Promotion  Special Issue “Community Health Workers Practice from Recruitment to Integration”  2021 Jun 7;9:663492. doi: 10.3389/fpubh.2021.663492. [Full text] 
  7. Houston E, Fadardi JS, Harawa NT, Argueta C, Mukherjee S. Individualized Web-Based Attention Training With Evidence-Based Counseling to Address HIV Treatment Adherence and Psychological Distress: Exploratory Cohort Study. JMIR Ment Health. 2021 Jan 28; 8(1):e18328. [Full text]
  8. Edwards GG, Reback CJ, Cunningham WE, Hilliard CL, McWells C, Mukherjee S, Weiss RE, Harawa NT. Mobile-Enhanced Prevention Support Study for Men Who Have Sex With Men and Transgender Women Leaving Jail: Protocol for a Randomized Controlled Trial. JMIR Res Protoc. 2020 Sep 22; 9(9):e18106 [Full text]
  9. Ogunyemi OI, Gandhi M, Tayek C. Predictive Models for Diabetic Retinopathy from Non-Image Teleretinal Screening Data. AMIA Jt Summits Transl Sci Proc. 2019;2019:472-7. [Full text]
  10. Houston E, Argueta C, Lacey J, Mukherjee S, Breda-Hill K. Reaching Mental Health Research Participants with Multiple Stigmas; A Description of Strategies Used in a Depression Intervention Study for YMSM of Color with HIV. Advanced Journal of Social Science. 2018;3(1):1-7. [Full text]
  11. Jenders RA, Adlassnig KP, Fehre K, Haug P. Evolution of the Arden Syntax: Key Technical Issues from the Standards Development Organization Perspective. Artif Intell Med. 2018 Nov;92:10-4. [Full text]
  12. George SM, Hayes EM, Fish A, Daskivich LP, Ogunyemi OI. Understanding the knowledge gap experienced by US safety net patients in teleretinal screening. AMIA Annual Symposium Proceedings. 2016;2016:590-599. [Full text]
  13. Ogunyemi O, Kermah D. Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records. AMIA  Annual Symposium proceedings / AMIA Symposium. 2015:983-90. [Full text]
  14. George S, Duran N, Norris K. A systematic review of barriers and facilitators to minority research participation among African Americans, Latinos, Asian Americans, and Pacific Islanders. Am J Public Health. 2014 Feb;104(2):e16-31. [Full text]
  15. Ogunyemi OI, Meeker D, Kim HE, Ashish N, Farzaneh S, Boxwala A. Identifying appropriate reference data models for comparative effectiveness research (CER) studies based on data from clinical information systems. Med Care. 2013 Aug;51(8 Suppl 3):S45-52. [Full text]
  16. Ogunyemi O, George S, Patty L, Teklehaimanot S, Baker R. Teleretinal screening for diabetic retinopathy in six Los Angeles urban safety-net clinics: final study results. AMIA  Annual Symposium proceedings / AMIA Symposium. 2013;2013:1082-8. [Full text]
  17. Ogunyemi O, Moran E, Patty Daskivich L, George S, Teklehaimanot S, Ilapakurthi R, et al. Autonomy versus automation: perceptions of nonmydriatic camera choice for teleretinal screening in an urban safety net clinic. Telemedicine journal and e-health : the official journal of the American Telemedicine Association. 2013 Aug;19(8):591-6. [Full text]
  18. Ogunyemi O, Teklehaimanot S, Patty L, Moran E, George S. Evaluating predictive modeling’s potential to improve teleretinal screening participation in urban safety net clinics. Studies in health technology and informatics. 2013;192:162-5.
  19. George S, Hindman D, Chizobam Ani, Mukherjee S, Ilapakurthi R, Verma M, et al. Lessons learned in the development of a clinical information system in an urban underserved community health clinic: A community partnered participatory research (CPPR) approach. International Public Health Journal. 2013;5(1):79.
  20. George S, Garth B, Fish A, Baker R. Factors shaping effective utilization of health information technology in urban safety-net clinics. Health informatics journal. 2013;19(3):183-97. [Full text]
  21. George S, Moran E, Duran N, Jenders RA. Using Animation as an Information Tool to Advance Health Research Literacy among Minority Participants. AMIA Annual Symposium Proceedings; 2013; 2013: 475-484. [Full text]
  22. George S, Hamilton A, Baker RS. How do low-income urban African Americans and Latinos feel about telemedicine? A diffusion of innovation analysis. International journal of telemedicine and applications. 2012;2012. [Full text]
  23.  George S, Phillips R, McDavitt B, Adams W, Mutchler MG. The cellular generation and a new risk environment: implications for texting-based sexual health promotion interventions among minority young men who have sex with men. AMIA Annual Symposium Proceedings; 2012; 2012: 247-256. [Full text]
  24. Liu LS, Hirano SH, Tentori M, Cheng KG, George S, Park SY, et al., editors. Improving communication and social support for caregivers of high-risk infants through mobile technologies. Proceedings of the ACM 2011 conference on Computer supported cooperative work; 2011.
  25. Ogunyemi O, Terrien E, Eccles A, Patty L, George S, Fish A, et al. Teleretinal screening for diabetic retinopathy in six Los Angeles urban safety-net clinics: initial findings. AMIA Annual Symposium proceedings / AMIA Symposium. 2011;2011:1027-35. [Full text]
  26. Fish A, George S, Terrien E, Eccles A, Baker R, Ogunyemi O. Workflow concerns and workarounds of readers in an urban safety net teleretinal screening study. AMIA  Annual Symposium proceedings / AMIA Symposium. 2011;2011:417-26. [Full text]
  27. Ogunyemi O, Mukherjee S, Ani C, Hindman D, George S, Ilapakurthi R, et al. CEDRIC: a computerized chronic disease management system for urban, safety net clinics. Studies in health technology and informatics. 2010;160(Pt 1):208-12. [Full text]
  28. George SM, Hamilton A, Baker R. Pre-experience perceptions about telemedicine among African Americans and Latinos in South Central Los Angeles. Telemedicine and e-Health. 2009;15(6):525-30. [Full text]

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