Data Science, Biomedical Informatics, and the OHSU Department of Medical Informatics & Clinical Epidemiology

(The following is reposted from Health, Data, Information and Action, the blog of the Oregon Health & Science University Department of Medical Informatics & Clinical Epidemiology.)

Data Science is a broad field that intersects many other fields within and outside of biomedicine and health, including biomedical informatics. Data science is certainly an important component of research and educational programs in the OHSU Department of Medical Informatics & Clinical Epidemiology (DMICE).

What exactly is data science? There are many methods, but one consensus is, “the multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured” [1].

The definition of data science is somewhat different from the definition of biomedical informatics, which is “the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health” [2].

Clearly there is overlap as well as complementarity. As noted by Payne et al., biomedical informatics deals with a broader spectrum of data and information tasks, focused not only on what is learned from data but also how that is applied in a broader sociotechnical context [2].

Many DMICE research programs focus on aspects of Data Science:

• Re-use of data from EHR (William Hersh, Aaron Cohen, Steven Bedrick) – leveraging data in EHR to identify patients as candidates for research studies and signals for rare diseases (porphyria) [3]
• Documenting genomic variation in leukemia (Shannon McWeeney) – allowing for repurposing of drugs [4]
• Quality of data for clinical care and research (Nicole Weiskopf) – methods for insuring completeness and comprehensiveness of data for use in research, quality measurement, and other tasks [5]
• Urinary microbiome in health and disease (Lisa Karstens) – identifying role of microbiome and how its genetics can be leveraged for diagnosis and treatment [6]
• Use of ambient data to detect and manage clinician strain – Dana Womack, Paul Gorman [7]

DMICE educational programs include Data Science in many of their courses. Our Bioinformatics & Computational Biomedicine (BCB) major includes:

• Data Harmonization and Standards for Translational Research - BMI 533/633 (Instructors: Melissa Haendel, Ph.D., Ted Laderas, Ph.D., Christina Zheng, Ph.D.)
• Management and Processing of Large Scale Data –BMI 535/635 (Instructors: Michael Mooney, Ph.D., Christina Zheng, Ph.D.)
• Computational Genetics -BMI 559/659 (Instructor: Shannon McWeeney, Ph.D.)
• Bioinformatics Programming and Scripting - BMI 565/656 (Instructor: Michael Mooney, Ph.D.)
• Network Science and Biology- BMI 567/667 (Instructor: Guanming Wu, Ph.D.)
• Data Analytics –BMI 569/669 (Instructors: Brian Sikora, Delilah Moore, Ted Laderas, Ph.D.)

Our Health & Clinical Informatics (HCIN) major includes:

• Introduction to Biomedical and Health Informatics - BMI 510/610 (Instructor: William Hersh, M.D.)
• Analytics for Healthcare - BMI 524/624 (Instructors: Abhijit Pandit, M.B.A., Tracy Edinger, N.D.)
• Clinical Research Informatics- BMI 523/623 (Instructor: Nicole Weiskopf, Ph.D., Robert Schuff)

We also have developed ample instructional materials in Data Science for other learners:

• R Bootcamp (Instructors: Ted Laderas, Ph.D. and Jessica Minnier, Ph.D.)
• Clinical Data Wrangling Workshop (Instructor: Ted Laderas, Ph.D.)
• OHSU Big Data to Knowledge (BD2K) Open Educational Resources (OERs)

References

1. Donoho, D (2017). 50 years of Data Science. Journal of Computational and Graphical Statistics. 26: 745-766. https://dl.dropboxusercontent.com/u/23421017/50YearsDataScience.pdf.
2. Payne, PRO, Bernstam, EV, et al. (2018). Biomedical informatics meets data science: current state and future directions for interaction. JAMIA Open. 1: 136-141. https://academic.oup.com/jamiaopen/article/1/2/136/5068667.
3. Wu, S, Liu, S, et al. (2017). Intra-institutional EHR collections for patient-level information retrieval. Journal of the American Society for Information Science & Technology. 68: 2636-2648.
4. Tyner, JW, Tognon, CE, et al. (2018). Functional genomic landscape of acute myeloid leukaemia. Nature. 562: 526-531.
4. Weiskopf, NG, Bakken, S, et al. (2017). A data quality assessment guideline for electronic health record data reuse. eGEMS. 5(1): 14.
6. Karstens, L, Asquith, M, et al. (2016). Does the urinary microbiome play a role in urgency urinary incontinence and its severity? Frontiers in Cellular and Infection Microbiology. 6:78. https://doi.org/10.3389/fcimb.2016.00078.
7. Womack, D. (2018). Subtle cues: Qualitative elicitation of signs of strain in the hospital workplace. PhD Dissertation, Oregon Health & Science University.