Mataroria P Lyndon1, Michael P Cassidy2, Leo Anthony Celi3, Luk Hendrik4, Yoon Jeon Kim5, Nicholas Gomez6, Nathaniel Baum7, Lucas Bulgarelli8, Kenneth E Paik9, Alon Dagan10. 1. Centre for Medical and Health Sciences Education, The University of Auckland, Building 599, Auckland Hospital Support Building, 2 Park Rd, Grafton, Auckland, New Zealand; MIT Critical Data, Building E25-505, 745 Carleton Street, Cambridge, MA 02139, United States. Electronic address: mataroria.lyndon@auckland.ac.nz. 2. TERC, STEM Education Evaluation Center, 2067 Massachusetts Avenue, Cambridge, MA 02140, United States. Electronic address: michael_cassidy@terc.edu. 3. MIT Critical Data, Building E25-505, 745 Carleton Street, Cambridge, MA 02139, United States; Harvard Medical School 25 Shattuck Street Boston, MA 02115, USA. Electronic address: lceli@mit.edu. 4. JASON Learning, 44983 Knoll Square, Ashburn, VA 20147, United States. Electronic address: luk@jason.org. 5. MIT Teaching Systems Lab, Massachusetts Institute of Technology, 600 Technology Square Cambridge, MA 02139, United States. Electronic address: yjk7@mit.edu. 6. MIT Teaching Systems Lab, Massachusetts Institute of Technology, 600 Technology Square Cambridge, MA 02139, United States. Electronic address: nico94@mit.edu. 7. Mass Mentoring Partnership, 75 Kneeland St, 11th Floor, Boston, MA 02111, United States. Electronic address: nbaum11@gmail.com. 8. Hospital Israelita Albert Einstein Big Data Analytics, Av. Albert Einstein, 627/701 Morumbi, São Paulo, 05652-900, Brazil. Electronic address: lucas.bulgarelli@einstein.br. 9. MIT Critical Data, Building E25-505, 745 Carleton Street, Cambridge, MA 02139, United States. Electronic address: kepaik@mit.edu. 10. MIT Critical Data, Building E25-505, 745 Carleton Street, Cambridge, MA 02139, United States; Beth Israel Deaconess Medical Center Department of Emergency Medicine, 1 Deaconess Rd, Boston, MA 02215, United States. Electronic address: adagan@bidmc.harvard.edu.
Abstract
OBJECTIVE: Machine learning in healthcare, and innovative healthcare technology in general, require complex interactions within multidisciplinary teams. Healthcare hackathons are being increasingly used as a model for cross-disciplinary collaboration and learning. The aim of this study is to explore high school student learning experiences during a healthcare hackathon. By optimizing their learning experiences, we hope to prepare a future workforce that can bridge technical and health fields and work seamlessly across disciplines. METHODS: A qualitative exploratory study utilizing focus group interviews was conducted. Eight high school students from the hackathon were invited to participate in this study through convenience sampling Participating students (n = 8) were allocated into three focus groups. Semi structured interviews were completed, and transcripts evaluated using inductive thematic analysis. FINDINGS: Through the structured analysis of focus group transcripts three major themes emerged from the data: (1) Collaboration, (2) Transferable knowledge and skills, and (3) Expectations about hackathons. These themes highlight strengths and potential barriers when bringing this multidisciplinary approach to high school students and the healthcare community. CONCLUSION: This study found that students were empowered by the interdisciplinary experience during a hackathon and felt that the knowledge and skills gained could be applied in real world settings. However, addressing student expectations of hackathons prior to the event is an area for improvement. These findings have implications for future hackathons and can spur further research into using the hackathon model as an educational experience for learners of all ages.
OBJECTIVE: Machine learning in healthcare, and innovative healthcare technology in general, require complex interactions within multidisciplinary teams. Healthcare hackathons are being increasingly used as a model for cross-disciplinary collaboration and learning. The aim of this study is to explore high school student learning experiences during a healthcare hackathon. By optimizing their learning experiences, we hope to prepare a future workforce that can bridge technical and health fields and work seamlessly across disciplines. METHODS: A qualitative exploratory study utilizing focus group interviews was conducted. Eight high school students from the hackathon were invited to participate in this study through convenience sampling Participating students (n = 8) were allocated into three focus groups. Semi structured interviews were completed, and transcripts evaluated using inductive thematic analysis. FINDINGS: Through the structured analysis of focus group transcripts three major themes emerged from the data: (1) Collaboration, (2) Transferable knowledge and skills, and (3) Expectations about hackathons. These themes highlight strengths and potential barriers when bringing this multidisciplinary approach to high school students and the healthcare community. CONCLUSION: This study found that students were empowered by the interdisciplinary experience during a hackathon and felt that the knowledge and skills gained could be applied in real world settings. However, addressing student expectations of hackathons prior to the event is an area for improvement. These findings have implications for future hackathons and can spur further research into using the hackathon model as an educational experience for learners of all ages.
Authors: Poppy L McLeod; Quinn W Cunningham; Deborah DiazGranados; Gabi Dodoiu; Seth Kaplan; Joann Keyton; Nicole Larson; Chelsea LeNoble; Stephan U Marsch; Thomas A O'Neill; Sarah Henrickson Parker; Norbert K Semmer; Marissa Shuffler; Lillian Su; Franziska Tschan; Mary Waller; Yumei Wang Journal: Health Care Manage Rev Date: 2021 Oct-Dec 01