3144 Exploring communication and collaboration at the Mount Sinai Health Hackathon: a social network analysis

Abstract

OBJECTIVES/SPECIFIC AIMS: The study aims to (1) investigate the structural patterns of professional communication that exist at the Mount Sinai Health Hackathon (2) explore if and how the professional networks of the participants change after engaging in the Mount Sinai Health Hackathon (3) explore any associations between the characteristics of participants’ professional networks and successful innovation development. METHODS/STUDY POPULATION: The recruitment pool consists of all 78 Mount Sinai Health Hackathon 2018 participants. Characteristics of the social network of Health Hackathon participants are assessed via an SNA data collection instrument at three time points: T1 directly before the Health Hackathon event, T2 directly after the event, T3 six months post-event. The Icahn School of Medicine at Mount Sinai Institutional Review Board approved this study as exempt. In order to explore patterns of communication between Health Hackathon participants during event, whole network data is collected at T2. Participants are provide with a roster of Mount Sinai Health hackathon participant names and asked to report the nature, frequency and perceived importance of their interaction with each of the other participants over the duration of the 48 hour event. In order capture any network change in the wider professional networks of the individual participants, known as “ego networks”, participants are asked to complete an SNA ego network survey at time points T1, T2 and T3. Open ended questions asked participants to report up to 20 people they consider being most important to them in their professional network and record the professional background of each person, the nature of the communication and the importance of each person to their success. Finally, at T3 participants are also asked to report on their project success (determined by businesses formed, filed provisional patents, financial income generation). This will be reviewed in relation to their social network data, to see if there is any relationship between the two. Data is analyzed using the specialized SNA software, UCiNET, which creates network sociograms to visualize network data. Descriptive statistics are used to report individual-level characteristics of respondents.RESULTS/ANTICIPATED RESULTS: To describe the structural patterns of communication at each time point, the following network-level indices are calculated: density (a measure of network cohesion), degree centrality (how many connections the individual has), betweenness centrality (whether the individual provides connections to other people in a network) and closeness centrality (how close the individual is to other people in the network). Network sociograms are generated for each time point to provide a visualization of the network. To explore the hypothesis that participating in the Mount Sinai Health Hackathon increases diversity of a professional network, analysis will focus on whether and how network-level indices change pre- and post- Hackathon. It will also explore any association betw...

The Future of Hackathon Research and Practice

“…investigated the usage of software repositories [59,60,66]. Other researchers have also included social network analysis [31]. Methods such as sensor based analytics and tools like smart badges which could foster the investigation of hackathons at a large scale and which have been successfully used in an educational context have not been utilized to study hackathons extensively yet [72], with only very few exceptions like [54].…”