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3339 Development of a Competency-based Informatics Course for Translational Researchers
- Ram Gouripeddi, Danielle Groat, Samir E. Abdelrahman, Tom Cheatham, Mollie Cummins, Karen Eilbeck, Bernie LaSalle, Katherine Sward, Julio C. Facelli
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- Journal:
- Journal of Clinical and Translational Science / Volume 3 / Issue s1 / March 2019
- Published online by Cambridge University Press:
- 26 March 2019, pp. 66-67
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- Article
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OBJECTIVES/SPECIFIC AIMS: Translational researchers often require the use of informatics methods in their work. Lack of an understanding of key informatics principles and methods limits the abilities of translational researchers to successfully implement Findable, Accessible, Interoperable, Reusable (FAIR) principles in grant proposal submissions and performed studies. In this study we describe our work in addressing this limitation in the workforce by developing a competency-based, modular course in informatics to meet the needs of diverse translational researchers. METHODS/STUDY POPULATION: We established a Translational Research Informatics Education Collaborative (TRIEC) consisting of faculty at the University of Utah (UU) with different primary expertise in informatics methods, and working in different tiers of the translational spectrum. The TRIEC, in collaboration with the Foundation of Workforce Development of the Utah Center for Clinical and Translational Science (CCTS), gathered informatics needs of early investigators by consolidating requests for informatics services, assistance provided in grant writing, and consultations. We then reviewed existing courses and literature for informatics courses that focused on clinical and translational researchers [3–9]. Using the structure and content of the identified courses, we developed an initial draft of a syllabus for a Translational Research Informatics (TRI) course which included key informatics topics to be covered and learning activities, and iteratively refined it through discussions. The course was approved by the UU Department of Biomedical Informatics, UU Graduate School and the CCTS. RESULTS/ANTICIPATED RESULTS: The TRI course introduces informatics PhD students, clinicians, and public health practitioners who have a demonstrated interest in research, to fundamental principles and tools of informatics. At the completion of the course, students will be able to describe and identify informatics tools and methods relevant to translational research and demonstrate inter-professional collaboration in the development of a research proposal addressing a relevant translational science question that utilizes the state-of-the-art in informatics. TRI covers a diverse set of informatics content presented as modules: genomics and bioinformatics, electronic health records, exposomics, microbiomics, molecular methods, data integration and fusion, metadata management, semantics, software architectures, mobile computing, sensors, recruitment, community engagement, secure computing environments, data mining, machine learning, deep learning, artificial intelligence and data science, open source informatics tools and platforms, research reproducibility, and uncertainty quantification. The teaching methods for TRI include (1) modular didactic learning consisting of presentations and readings and face-to-face discussions of the content, (2) student presentations of informatics literature relevant to their final project, and (3) a final project consisting of the development, critique and chalk talk and formal presentations of informatics methods and/or aims of an National Institutes of Health style K or R grant proposal. For (3), the student presents their translational research proposal concept at the beginning of the course, and works with members of the TRIEC with corresponding expertise. The final course grade is a combination of the final project, paper presentations and class participation. We offered TRI to a first cohort of students in the Fall semester of 2018. DISCUSSION/SIGNIFICANCE OF IMPACT: Translational research informatics is a sub-domain of biomedical informatics that applies and develops informatics theory and methods for translational research. TRI covers a diverse set of informatics topics that are applicable across the translational spectrum. It covers both didactic material and hands-on experience in using the material in grant proposals and research studies. TRI’s course content, teaching methodology and learning activities enable students to initially learn factual informatics knowledge and skills for translational research correspond to the ‘Remember, Understand, and Apply’ levels of the Bloom’s taxonomy [10]. The final project provides opportunity for applying these informatics concepts corresponding to the ‘Analyze, Evaluate, and Create’ levels of the Bloom’s taxonomy [10]. This inter-professional, competency-based, modular course will develop an informatics-enabled workforce trained in using state-of-the-art informatics solutions, increasing the effectiveness of translational science and precision medicine, and promoting FAIR principles in research data management and processes. Future work includes opening the course to all Clinical and Translational Science Award hubs and publishing the course material as a reference book. While student evaluations for the first cohort will be available end of the semester, true evaluation of TRI will be the number of trainees taking the course and successful grant proposal submissions. References: 1. Wilkinson MD, Dumontier M, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016 Mar 15. 2. National Center for Advancing Translational Sciences. Translational Science Spectrum. National Center for Advancing Translational Sciences. 2015 [cited 2018 Nov 15]. Available from: https://ncats.nih.gov/translation/spectrum 3. Hu H, Mural RJ, Liebman MN. Biomedical Informatics in Translational Research. 1 edition. Boston: Artech House; 2008. 264 p. 4. Payne PRO, Embi PJ, Niland J. Foundational biomedical informatics research in the clinical and translational science era: a call to action. J Am Med Inform Assoc JAMIA. 2010;17(6):615–6. 5. Payne PRO, Embi PJ, editors. Translational Informatics: Realizing the Promise of Knowledge-Driven Healthcare. Softcover reprint of the original 1st ed. 2015 edition. Springer; 2016. 196 p. 6. Richesson R, Andrews J, editors. Clinical Research Informatics. 2nd ed. Springer International Publishing; 2019. (Health Informatics). 7. Robertson D, MD GHW, editors. Clinical and Translational Science: Principles of Human Research. 2 edition. Amsterdam: Academic Press; 2017. 808 p. 8. Shen B, Tang H, Jiang X, editors. Translational Biomedical Informatics: A Precision Medicine Perspective. Softcover reprint of the original 1st ed. 2016 edition. S.l.: Springer; 2018. 340 p. 9. Valenta AL, Meagher EA, Tachinardi U, Starren J. Core informatics competencies for clinical and translational scientists: what do our customers and collaborators need to know? J Am Med Inform Assoc. 2016 Jul 1;23(4):835–9. 10. Anderson LW, Krathwohl DR, Airasian PW, Cruikshank KA, Mayer RE, Pintrich PR, Raths J, Wittrock MC. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives, Abridged Edition. 1 edition. New York: Pearson; 2000.
3048 Measuring the Autonomic Nervous System for Translational Research: Identification of Non-invasive Methods
- Danielle Groat, Ram Gouripeddi, Yu Keui Lin, Julio C. Facelli
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- Journal:
- Journal of Clinical and Translational Science / Volume 3 / Issue s1 / March 2019
- Published online by Cambridge University Press:
- 26 March 2019, p. 28
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- Article
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- You have access Access
- Open access
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OBJECTIVES/SPECIFIC AIMS: The objective of this study is to identify and categorize non-invasive measurement methods for autonomic nervous system (ANS) symptoms that develop in hypoglycemic episodes. METHODS/STUDY POPULATION: We first reviewed literature for hypoglycemia symptomology. We then performed a selective literature review of Google Scholar, PubMed and Scopus for an ANS symptom and/or synonyms and the words ‘sensor’ or ‘detection’, e.g. ‘sweat sensor’ and ‘tremor detection’, studies utilizing non-invasive measurements in DM, and datasets of non-invasive measurements in DM. Measurement methods were then organized based on the ANS symptoms and existing metadata models for harmonizing sensors and surveys. RESULTS/ANTICIPATED RESULTS: We identified several measurement methods to for ANS symptoms during hypoglycemic events: thermometer, accelerometer, electrocardiogram (ECG), galvanic skin response (GSR), image processing, infrared imaging, thermal actuator, and ecological momentary assessment (EMA). The stage of implementation varied across the measurement methods from under development, to use in research and clinical settings, and even commercially available consumer products. Measurement methods that could be worn as wrist-band wearables or as film-based epidermal sensors would be capable of automatically gathering data with little to no effort required of the person wearing the device. Image-based methods would require the individual to actively engage in generating a photograph for analysis. In the case of EMA’s, a message containing a question is sent to the individual, often via text message, soliciting short and immediate responses. It is anticipated that one sensor alone would not be sufficient to measure ANS responses to hypoglycemia, but rather several data points would be required. For example, if the GSR was the only signal, sweat in response to vigorous exercise or a warm environment would inject noise into the signal. Including the accelerometer data would allow for the identification of body movement which would indicate exercise, while an ECG signal could confirm the exercise. DISCUSSION/SIGNIFICANCE OF IMPACT: Impaired awareness of hypoglycemia (IAH) is a complication that develops in about 30% of type 1 DM and 10% type 2 DM populations. In individuals with intact awareness of hypoglycemia, the ANS leads to symptoms which includes: shaking, trembling, anxiety, nervousness, palpitation (i.e. change in heart rate and/or function), clamminess, sweating, dry mouth, hunger, pallor (i.e. drop in blood flow and/or skin-surface temperature), and pupil dilation. IAH is defined as the onset of hypoglycemia before the appearance of autonomic warning symptoms. IAH is caused by repeated exposures to low blood glucose levels, which reduces the body’s ability to sense hypoglycemia, and therefore it is difficult for patients to recognize and self-treat. Individuals with IAH are six times more likely to experience severe hypoglycemia, an emergent condition which can lead to unconsciousness, seizure, coma, and death. Clinical investigators are developing interventions that aim to improve awareness of hypoglycemia. Surveys, observations by clinicians, and laboratory tests, often carried out in highly controlled in-patient settings, are currently used to assess the severity of IAH and the ANS’s ability to respond to hypoglycemia. In other disease states, for example heart disease and Parkinson’s disease, electrocardiograms and accelerometers have been used to assess heart function and tremor, respectively. However, there is currently a barrier to examining the efficacy of IAH interventions in real world settings as there are no established objective and non-invasive means to measure ANS symptoms due to hypoglycemia. This work encompasses the first important step necessary to direct translational researchers interested in testing the efficacy of IAH interventions and developing diagnostic tools for IAH in real-world studies outside the clinic. Next steps include evaluating these sensors and specifying EMA surveys, designing studies, and integration and assimilation of these data streams to identify true events of IAH by leveraging informatics platform such as the Utah PRISMS Informatics Ecosystem. Investigators would then be able to conduct studies that aim to develop and validate models that take sensor and EMA data as the input to detect and assess the severity of IAH.