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426 Datathon Revisited: Implementation of Lesson Learned
OBJECTIVES/GOALS: In 2020, Baylor College of Medicine held a datathon to introduce a data warehouse, identify its capabilities/limitations, foster collaborations, and engage trainees. The event was held again in 2022, and lessons learned (e.g., tools for data self-service or team communication) were...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10129517/ http://dx.doi.org/10.1017/cts.2023.458 |
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author | Zimolzak, Andrew J. Sippel, Katherine Davila, Jessica A. DeBakey, Michael E. Punugoti, Vamshi Klotman, Paul E. Petersen, Laura A. DeBakey, Michael E. Liao, Gloria Leiber, Lee Amos, Christopher I. |
author_facet | Zimolzak, Andrew J. Sippel, Katherine Davila, Jessica A. DeBakey, Michael E. Punugoti, Vamshi Klotman, Paul E. Petersen, Laura A. DeBakey, Michael E. Liao, Gloria Leiber, Lee Amos, Christopher I. |
author_sort | Zimolzak, Andrew J. |
collection | PubMed |
description | OBJECTIVES/GOALS: In 2020, Baylor College of Medicine held a datathon to introduce a data warehouse, identify its capabilities/limitations, foster collaborations, and engage trainees. The event was held again in 2022, and lessons learned (e.g., tools for data self-service or team communication) were applied. METHODS/STUDY POPULATION: Senior faculty reviewed proposals with an emphasis on feasibility, impact, and relevance to quality improvement or population health. Selected teams worked with Information Technology (IT) for 2 months and presented findings at a 1-day event. Surveys were administered to participants before and after the event to evaluate their background, team characteristics, collaborations, knowledge before and after the datathon, perceived value of the datathon, and plans for future work. Descriptive statistics of respondents’ self-reports were tabulated. RESULTS/ANTICIPATED RESULTS: In 2022, 19 of 36 projects were accepted (13/33 in 2020). At both events, most projects studied quality improvement or clinical outcomes. Of 82 participants in 2022, 54 completed surveys. In 2022, 72% had no datathon experience (48% in 2020). Median effort was 10 person-hours; median IT time was 20% (20 and 10%, in 2020). Seven respondents finished and 21 partially finished their projects (1 and 11, in 2020); 92% made new collaborations (91% in 2020). Respondents strongly agreed that: the experience was valuable (n=28), they would participate in future datathons (n=30), and they would use the warehouse for future work (n=25). Twenty-seven have planned abstracts; 25 have planned manuscripts. DISCUSSION/SIGNIFICANCE: The 2022 datathon had more participants with less experience, potentially due to improved promotion and training opportunities. Fewer person-hours and a higher percentage of IT time were required as compared to 2020, and more projects were completed, possibly due to increased IT efficiency. |
format | Online Article Text |
id | pubmed-10129517 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-101295172023-04-26 426 Datathon Revisited: Implementation of Lesson Learned Zimolzak, Andrew J. Sippel, Katherine Davila, Jessica A. DeBakey, Michael E. Punugoti, Vamshi Klotman, Paul E. Petersen, Laura A. DeBakey, Michael E. Liao, Gloria Leiber, Lee Amos, Christopher I. J Clin Transl Sci Team Science OBJECTIVES/GOALS: In 2020, Baylor College of Medicine held a datathon to introduce a data warehouse, identify its capabilities/limitations, foster collaborations, and engage trainees. The event was held again in 2022, and lessons learned (e.g., tools for data self-service or team communication) were applied. METHODS/STUDY POPULATION: Senior faculty reviewed proposals with an emphasis on feasibility, impact, and relevance to quality improvement or population health. Selected teams worked with Information Technology (IT) for 2 months and presented findings at a 1-day event. Surveys were administered to participants before and after the event to evaluate their background, team characteristics, collaborations, knowledge before and after the datathon, perceived value of the datathon, and plans for future work. Descriptive statistics of respondents’ self-reports were tabulated. RESULTS/ANTICIPATED RESULTS: In 2022, 19 of 36 projects were accepted (13/33 in 2020). At both events, most projects studied quality improvement or clinical outcomes. Of 82 participants in 2022, 54 completed surveys. In 2022, 72% had no datathon experience (48% in 2020). Median effort was 10 person-hours; median IT time was 20% (20 and 10%, in 2020). Seven respondents finished and 21 partially finished their projects (1 and 11, in 2020); 92% made new collaborations (91% in 2020). Respondents strongly agreed that: the experience was valuable (n=28), they would participate in future datathons (n=30), and they would use the warehouse for future work (n=25). Twenty-seven have planned abstracts; 25 have planned manuscripts. DISCUSSION/SIGNIFICANCE: The 2022 datathon had more participants with less experience, potentially due to improved promotion and training opportunities. Fewer person-hours and a higher percentage of IT time were required as compared to 2020, and more projects were completed, possibly due to increased IT efficiency. Cambridge University Press 2023-04-24 /pmc/articles/PMC10129517/ http://dx.doi.org/10.1017/cts.2023.458 Text en © The Association for Clinical and Translational Science 2023 https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work. |
spellingShingle | Team Science Zimolzak, Andrew J. Sippel, Katherine Davila, Jessica A. DeBakey, Michael E. Punugoti, Vamshi Klotman, Paul E. Petersen, Laura A. DeBakey, Michael E. Liao, Gloria Leiber, Lee Amos, Christopher I. 426 Datathon Revisited: Implementation of Lesson Learned |
title | 426 Datathon Revisited: Implementation of Lesson Learned |
title_full | 426 Datathon Revisited: Implementation of Lesson Learned |
title_fullStr | 426 Datathon Revisited: Implementation of Lesson Learned |
title_full_unstemmed | 426 Datathon Revisited: Implementation of Lesson Learned |
title_short | 426 Datathon Revisited: Implementation of Lesson Learned |
title_sort | 426 datathon revisited: implementation of lesson learned |
topic | Team Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10129517/ http://dx.doi.org/10.1017/cts.2023.458 |
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