COVID-19 Data Utilization in North Carolina: Qualitative Analysis of Stakeholder Experiences.

BACKGROUND: As the world faced the pandemic caused by the novel coronavirus disease 2019 (COVID-19), medical professionals, technologists, community leaders, and policy makers sought to understand how best to leverage data for public health surveillance and community education. With this complex public health problem, North Carolinians relied on data from state, federal, and global health organizations to increase their understanding of the pandemic and guide decision-making. OBJECTIVE: We aimed to describe the role that stakeholders involved in COVID-19-related data played in managing the pandemic in North Carolina. The study investigated the processes used by organizations throughout the state in using, collecting, and reporting COVID-19 data. METHODS: We used an exploratory qualitative study design to investigate North Carolina's COVID-19 data collection efforts. To better understand these processes, key informant interviews were conducted with employees from organizations that collected COVID-19 data across the state. We developed an interview guide, and open-ended semistructured interviews were conducted during the period from June through November 2020. Interviews lasted between 30 and 45 minutes and were conducted by data scientists by videoconference. Data were subsequently analyzed using qualitative data analysis software. RESULTS: Results indicated that electronic health records were primary sources of COVID-19 data. Often, data were also used to create dashboards to inform the public or other health professionals, to aid in decision-making, or for reporting purposes. Cross-sector collaboration was cited as a major success. Consistency among metrics and data definitions, data collection processes, and contact tracing were cited as challenges. CONCLUSIONS: Findings suggest that, during future outbreaks, organizations across regions could benefit from data centralization and data governance. Data should be publicly accessible and in a user-friendly format. Additionally, established cross-sector collaboration networks are demonstrably beneficial for public health professionals across the state as these established relationships facilitate a rapid response to evolving public health challenges.

Scientific Training in the Era of Big Data: A New Pedagogy for Graduate Education.

The era of "big data" has radically altered the way scientific research is conducted and new knowledge is discovered. Indeed, the scientific method is rapidly being complemented and even replaced in some fields by data-driven approaches to knowledge discovery. This paradigm shift is sometimes referred to as the "fourth paradigm" of data-intensive and data-enabled scientific discovery. Interdisciplinary research with a hard emphasis on translational outcomes is becoming the norm in all large-scale scientific endeavors. Yet, graduate education remains largely focused on individual achievement within a single scientific domain, with little training in team-based, interdisciplinary data-oriented approaches designed to translate scientific data into new solutions to today's critical challenges. In this article, we propose a new pedagogy for graduate education: data-centered learning for the domain-data scientist. Our approach is based on four tenets: (1) Graduate training must incorporate interdisciplinary training that couples the domain sciences with data science. (2) Graduate training must prepare students for work in data-enabled research teams. (3) Graduate training must include education in teaming and leadership skills for the data scientist. (4) Graduate training must provide experiential training through academic/industry practicums and internships. We emphasize that this approach is distinct from today's graduate training, which offers training in either data science or a domain science (e.g., biology, sociology, political science, economics, and medicine), but does not integrate the two within a single curriculum designed to prepare the next generation of domain-data scientists. We are in the process of implementing the proposed pedagogy through the development of a new graduate curriculum based on the above four tenets, and we describe herein our strategy, progress, and lessons learned. While our pedagogy was developed in the context of graduate education, the general approach of data-centered learning can and should be applied to students and professionals at any stage of their education, including at the K-12, undergraduate, graduate, and professional levels. We believe that the time is right to embed data-centered learning within our educational system and, thus, generate the talent required to fully harness the potential of big data.