Felipe Maia de Toledo Piza1, Leo Anthony Celi2, Rodrigo Octavio Deliberato3, Lucas Bulgarelli4, Fabricio Rodrigues Torres de Carvalho5, Roberto Rabello Filho6, Miguel Angel Armengol de La Hoz7, Jennifer Cohn Kesselheim8. 1. Critical Care Department, Hospital Israelita Albert Einstein, São Paulo, Brazil; Medical Education Department, Harvard Medical School, Boston, USA. Electronic address: felipe.piza@einstein.br. 2. Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, MA, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA. 3. Critical Care Department, Hospital Israelita Albert Einstein, São Paulo, Brazil; Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, MA, USA; Big Data Analytics Department, Hospital Israelita Albert Einstein, São Paulo, Brazil. 4. Big Data Analytics Department, Hospital Israelita Albert Einstein, São Paulo, Brazil. 5. Critical Care Department, Hospital Israelita Albert Einstein, São Paulo, Brazil; Intensive Care Unit, AC Camargo Cancer Center, São Paulo, Brazil. 6. Critical Care Department, Hospital Israelita Albert Einstein, São Paulo, Brazil. 7. Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA; Grupo de Bioingenieria y Telemedicina, Universidad Politecnica de Madrid, Madrid, Spain; Unidad de Innovación, Instituto de Investigación Sanitaria San Carlos, Hospital Clínico San Carlos, Madrid, Spain. 8. Medical Education Department, Harvard Medical School, Boston, USA; Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA.
Abstract
BACKGROUND: Datathons are increasingly organized in the healthcare field. The goal is to assemble people with different backgrounds to work together as a team and engage in clinically relevant research or develop algorithms using health-related datasets. Criteria to assess the return of investment on such events have traditionally included publications produced, patents for prediction, classification, image recognition and other types of software, and start-up companies around the application of machine learning in healthcare. Previous studies have not evaluated whether a datathon can promote affective learning and effective teamwork. METHODS: Fifty participants of a health datathon event in São Paulo, Brazil at Hospital Israelita Albert Einstein (HIAE) were divided into 8 groups. A survey with 25 questions, using the Affective Learning Scale and Team-Review Questionnaire, was administered to assess team effectiveness and affective learning during the event. Multivariate regression models and Pearson's correlation tests were performed to evaluate the effect of affective learning on teamwork. RESULTS: Majority of the participants were male 76% (37/49); 32% (16/49) were physicians. The mean score for learning (scale from 1 to 10) was 8.38, while that for relevance of the perceived teamwork was 1.20 (scale from 1 to 5; "1" means most relevant). Pearson's correlation between the learning score and perception of teamwork showed moderate association (r = 0.36, p = 0.009). Five learning and 10 teamwork variables were on average positively graded in the event. The final regression model includes all learning and teamwork variables. Effective leadership was strongly correlated with affective learning (β = -0.27, p < 0.01, R2 = 75%). Effective leadership, team accomplishment, criticism, individual development and creativity were the variables significantly associated with higher levels of affective learning. CONCLUSION: It is feasible to enhance affective knowledge and the skill to work in a team during a datathon. We found that teamwork is associated with higher affective learning from participants' perspectives. Effective leadership is essential for teamwork and is a significant predictor of learning.
BACKGROUND: Datathons are increasingly organized in the healthcare field. The goal is to assemble people with different backgrounds to work together as a team and engage in clinically relevant research or develop algorithms using health-related datasets. Criteria to assess the return of investment on such events have traditionally included publications produced, patents for prediction, classification, image recognition and other types of software, and start-up companies around the application of machine learning in healthcare. Previous studies have not evaluated whether a datathon can promote affective learning and effective teamwork. METHODS: Fifty participants of a health datathon event in São Paulo, Brazil at Hospital Israelita Albert Einstein (HIAE) were divided into 8 groups. A survey with 25 questions, using the Affective Learning Scale and Team-Review Questionnaire, was administered to assess team effectiveness and affective learning during the event. Multivariate regression models and Pearson's correlation tests were performed to evaluate the effect of affective learning on teamwork. RESULTS: Majority of the participants were male 76% (37/49); 32% (16/49) were physicians. The mean score for learning (scale from 1 to 10) was 8.38, while that for relevance of the perceived teamwork was 1.20 (scale from 1 to 5; "1" means most relevant). Pearson's correlation between the learning score and perception of teamwork showed moderate association (r = 0.36, p = 0.009). Five learning and 10 teamwork variables were on average positively graded in the event. The final regression model includes all learning and teamwork variables. Effective leadership was strongly correlated with affective learning (β = -0.27, p < 0.01, R2 = 75%). Effective leadership, team accomplishment, criticism, individual development and creativity were the variables significantly associated with higher levels of affective learning. CONCLUSION: It is feasible to enhance affective knowledge and the skill to work in a team during a datathon. We found that teamwork is associated with higher affective learning from participants' perspectives. Effective leadership is essential for teamwork and is a significant predictor of learning.