Research

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.

• Evidence for Precision Medicine and Precision Public Health Practice
• Facilitating Research and Patient Monitoring with Digital Devices
• Genomic Clinical Decision Support
• Collaborative Research

Definitions

• 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:

• Nguyen MH, Sedoc J, Taylor CO. Usability, Engagement, and Report Usefulness of Chatbot-Based Family Health History Data Collection: Mixed Methods Analysis. J Med Internet Res. 2024 Sep 30:26:e55164. [link]
• Rattsev I, Stearns V, Blackford AL, Hertz DL, Smith KL, Rae JM, Taylor CO. Incorporation of Emergent Symptoms and Genetic Covariates Improves Prediction of Aromatase Inhibitor Therapy Discontinuation. JAMIA Open. 2024 Apr 1;7(1):ooae006. [link]
• Flaks-Manov N, Bai J, Zhang C, Malpani A, Ray SC, Taylor CO. Assessing Associations Between COVID-19 Symptomology and Adverse Outcomes after Piloting Crowdsourced Data Collection: A Cross-Sectional Survey Study. JMIR Formative Research. Vol 6, No 12 (2022). [link]
• 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]

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Facilitating Research and Patient Monitoring with Digital Devices

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

Selected publications:

• Soley N, Speed TJ, Xie A, Taylor CO. Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data. Appl Clin Inform . 2024 May;15(3):569-582. [link]
• Lu Yuzhi, Green AR, Quiles R, Taylor CO. An Automated Strategy to Calculate Medication Regimen Complexity. AMIA Annu Symp Proc. 2023; 2023: 1077–1086. [link]
• Soley N, Song S, Flaks-Manov N, Taylor CO. Risk for Poor Post-Operative Quality of Life Among Wearable Use Subgroups in an All of Us Research Cohort. Pac Symp Biocomput. 2023; 28: 31-42. [link]
• Gorman K, Rattsev I, Lu L, Taylor CO. An Interactive Visualization Tool for Medication (Re) fill Adherence: A Case Study of Pharmacy Claims-derived Adherence Measures in Asthmatics. In 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI) 2022 Jun 11 (pp. 239-244). IEEE. [link]
• 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]

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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:

• Coffen-Burke J, Yang K, Lkhagvajav Z, Lu L, Wang N, Segbefia TD, Taylor CO. Designing Software for Genomic Medicine Service Leaders Seeking to Engage Implementation Partners. In2023 IEEE 11th International Conference on Healthcare Informatics (ICHI) 2023 Jun 26 (pp. 398-406). IEEE. [link]
• Soley N, Klein A, Taylor CO, Nguyen M, Ewachiw G, Shah H, Bodurtha J. Using a Chatbot to Provide Genetic Education to Pancreatic Cancer Patients. AMIA Jt Summits Transl Sci Proc. 2023; 2023: 497–504. [link]
• 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. 2021; 5(02): 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]

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