Our research draws from biomedical informatics and the related field of biomedical data science, to address the challenge of how to incorporate technology and digital approaches into translational research, precision medicine, and precision public health practices.


  • Biomedical Informatics, Data Science, and Implementation. Biomedical Informatics can be defined as the analysis, management and use of knowledge, information and data in the domain of biomedicine and health. (Kulikowski et al. JAMIA 2012). Biomedical informatics and the related subfield of biomedical data science is concerned with discoveries using primarily health data. (Brennan et al. JAMIA 2017). Biomedical informatics often involves the implementation of tools, resources, and organizational models that transform translational research (moving theory into practice and concepts into reality). (Description used by 2019 AMIA Informatics Summit Implementation Track).
  • Translational research may be conceptualized as four phases revolving around the development of evidence-based guidelines (T1-T4). T1 research seeks to move a basic discovery into a candidate health application (e.g., genetic test/intervention); T2 research assesses the value of a health application leading to the development of evidence-based guidelines; T3 research attempts to move evidence-based guidelines into health practice; and T4 research seeks to evaluate the "real world" health outcomes of a health application in practice. (Khoury et al. Genet Med 2007). Translational research can lead to actionable health discoveries that provide relevant information for precision medicine and precision public health.
  • Precision medicine “is the practice of clinical decision-making such that the decisions maximize the outcomes that the patient most cares about and minimize those that the patient fears the most, on the basis of as much knowledge about the individual’s state as is available.” (Pauker SG et al. NEJM. 1987). This definition of precision medicine captures a focus on the outcomes of care, the central role of the patient in defining important outcomes, and the inclusion of a broad personal data. (Williams MS. In: Personalized and Precision Medicine Informatics. Springer. 2020).
  • Precision public health involves collecting and using more accurate data on genes, exposures, behaviors, and other social/economic health determinants in order to enhance public health action that can lead to improved health and reduced health disparities in subpopulations. (Khoury et al. Am J Prev Med. 2016).

Evidence for Precision Medicine and Precision Public Health Practice

Projects that seek to leverage EHR-derived data and other data sources to identify practice patterns and behaviors relevant to health outcomes. Our goal is to generate evidence to guide precision medicine and precision public health practices in areas such as case management after the return of genomic test results, chronic disease management, drug treatment selection, and postpartum care optimization.

Selected Publications:

  • Rattsev I, Flaks-Manov N, Jelin AC, Bai J, Taylor CO. Recurrent preterm birth risk assessment for two delivery subtypes: A multivariable analysis. Journal of the American Medical Informatics Association. 2021;ocab184. [link]
  • Roe KD, Jawa V, Zhang X, Chute CG, Epstein JA, Matelsky J, Shpitser I, Taylor CO. Feature engineering with clinical expert knowledge: a case study assessment of machine learning model complexity and performance. PloS one. 2020 Apr 23;15(4):e0231300. [link]
  • Taylor CO, Lemke KW, Richards TM, Roe KD, He T, Arruda-Olson A, Carrell D, Denny JC, Hripcsak G, Kiryluk K, Kullo I, Larson EB, Peissig P, Walton N, Wei W, Ye Z, Chute CG, Weiner JP. Comorbidity Characterization Among eMERGE Institutions: A Pilot Evaluation of the Johns Hopkins ACG System. In AMIA Summits on Translational Science Proceedings, vol. 2019. [link]
  • Liang OS, Sheffield J, Taylor CO. Detecting Patterns of Prescription Drug Use During Pregnancy and Lactation with Visualization Techniques. In AMIA Summits on Translational Science Proceedings, vol. 2019. [link]
  • Chowdhuri S, McCrea S, Demner-Fushman D, Taylor CO. Extracting Biomedical Terms from Postpartum Depression Online Health Communities. In AMIA Summits on Translational Science Proceedings, vol. 2019.[link]

Facilitating Translational Research with Digital Devices

Characterizing and designing services to enable participation and engagement in research using digital devices. Our goal is to provide tools to help researchers make use of emerging technologies such as wearable monitoring to enable deep phenotyping, while keeping study participants at the center of innovation.

Selected publications:

  • Taylor CO, Flaks-Manov N, Remesh S, Choe EK. Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study. Journal of medical Internet research. 2021, 23(10), e19789.[link]
  • Taylor CO, Flaks-Manov N, Crew KD, Weng C, Connolly J, Chute C, Ford D, Lehmann H, Rahm A, Kullo I, Caraballo P, Kitchner T, Lynch J, Cobb B, Holm I, Mathews D. Preferences for updates on general research results. A survey of participants in genomics research at two institutions. J. Pers. Med. 2021, 11(5), 399.[link]
  • Overby CL, Maloney KA, Alestock TD, Chavez J, Berman D, Sharaf RM, Fitzgerald T, Kim EY, Palmer K, Shuldiner AR, Mitchell BD. Prioritizing Approaches to Engage Community Members and Build Trust in Biobanks: A Survey of Attitudes and Opinions of Adults within Outpatient Practices at the University of Maryland. J. Pers. Med. 2015 Sep; 5(3): 264–279. [link]

Genomic Clinical Decision Support

Approaches to design and evaluate genomic clinical decision support leveraging local health IT infrastructure. We also have contributed to the NHGRI-funded eMERGE Network EHR Integration workgroup that is developing methods and best practices for incorporating patient’s genomic data into the EHR and assessing the usability of those data by physicians and patients (see Projects).

Selected publications:

  • Taylor CO, Rasmussen LV, Rasmussen-Torvik LJ, Prows CA, Dorr DA, Samal L, Aronson S. Facilitating Genetics Aware Clinical Decision Support: Putting the eMERGE Infrastructure into Practice. ACI Open, 5(02), pp.e54-e58.. [link]
  • Murugan M, Babb L, Taylor CO, Rasmussen L, Freimuth RR, Venner E, Yan F, Yi V, Granite S, Zouk H, Aronson SJ, Power K, Fedotov A, Crosslin D, Fasel D, Jarvik G, Hakonarson H, Bangash H, Kullo I, Connolly J, Nestor J, Caraballo P, Wei W, Wiley K, Rehm H, Gibbs R. Genomic Considerations for FHIR: eMERGE Implementation Lessons. J Biomed Inform. 2021 June;118:103795. [link]
  • Rassmussen LV, Connolly JJ, Del Fiol G, Freimuth RR, Pet DB, Peterson JF, Shirts BH, Starren JB, Williams MS, Walton N, Taylor CO. Infobuttons for Genomic Medicine: Requirements and Barriers. Appl Clin Inform. 2021; 12(02): 383-390. [link]
  • Aronson S, Babb L, Ames D, Gibbs RA, Venner E, Connelly JJ, Marsolo K, Weng C, Williams MS, Hartzler AL, Liang WH, Ralston JD, Devine EB, Murphy S, Chute CC, Caraballo PJ, Kullo IJ, Freimulth RR, Rasmussen LV, Wehbe FH, Peterson JF, Robinson JR, Wiley K, Taylor CO. Empowering Genomic Medicine by Establishing Critical Sequencing Result Data Flows: The eMERGE Example. J Am Med Inform Assoc. 2018 Oct 1;25(10):1375-1381.[link]
  • Overby CL, Thompkins P, Lehmann H, Chute CG, Sheffield JS. Genetics-informed Drug Dosing Guidance in Pregnant Women: A Needs Assessment with Obstetric Healthcare Providers at Johns Hopkins. AMIA Annu Symp Proc. 2018 Apr 16;2017:1342-1351. eCollection 2017.[slides] [link]
  • Cutting E, Banchero M, Beitelshees AL, Cimino JJ, Del Fiol G, Gurses AP, Hoffman MA, Jeng LJ, Kawamoto K, Kelemen M, Pincus HA, Shuldiner AR, Williams MS, Overby CL. User-centered design of multi-gene sequencing panel reports for clinicians. Journal of Biomedical Informatics. 2016 Oct 31;63:1-10.[link]
  • Overby CL, Devine EB, Abernethy N, McCune JS, Tarczy-Hornoch P. Making pharmacogenomic-based prescribing alerts more effective: a scenario-based pilot study with physicians. Journal of biomedical informatics. 2015 Jun 30;55:249-59. [link]
  • Overby CL, Tarczy-Hornoch P, Hoath JI, Kalet IJ, Veenstra DL. Feasibility of incorporating genomic knowledge into electronic medical records for pharmacogenomic clinical decision support. BMC bioinformatics. 2010 Oct 28;11(9):S10.[link]

Collaborative Research

Our efforts are often interdisciplinary and require expertise in a number of methodological areas, thus we have several collaborations with other researchers and teams.

Collaborators on Funded Projects:

  • Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Research Diversity (AIM-AHEAD)
  • Randomized clinical trial of the sequence of genetic counseling and testing to optimize efficiency, patient empowerment and engagement, and medical adherence for diverse genetic testing indications
    • Cynthia A James, PhD (PI of [NIH NHGRI R01 HG11902]); Carolyn Applegate, MGC; Brittney Murray, MS, CGC & team
  • Implementing the Genomic Data Science Analysis, Visualization, and Informatics Lab-Space (AnVIL)
    • Michael Schatz, PhD (contact PI on AnVIL grant [NIH NHGRI U24HG010263])
  • Will the Doctor “See You” Now? A RCT of Video vs. Telephone Primary Care Visits @Johns Hopkins University
    • Jeremy Epstein, MD
  • Patient Readiness for Personalized Assessment Program @Johns Hopkins University
    • Joann Bodurtha, MD & Alison Klein, PhD
  • Clinial Pharmacogenomics and Pharmacometrics Program @Johns Hopkins University
    • Craig Hendrix, MD; Ethel Weld, MD, PhD; & Jim Stevenson, MS, PharmD
  • Online Mendelian Inheritance in Man®
    • Ada Hamosh, MD, MPH (PI of (OMIM®) grant [NIH NHGRI U41 HG006627]); Howard Levy, MD, PhD & team
  • Johns Hopkins Insititute for Clinical and Translational Research