Data-driven, cross-disciplinary collaboration: lessons learned at the largest academic health center in Latin America during the COVID-19 pandemic

Abstract

Introduction: The COVID-19 pandemic has prompted global research efforts to reduce infection impact, highlighting the potential of cross-disciplinary collaboration to enhance research quality and efficiency.

Methods: At the FMUSP-HC academic health system, we implemented innovative flow management routines for collecting, organizing and analyzing demographic data, COVID-related data and biological materials from over 4,500 patients with confirmed SARS-CoV-2 infection hospitalized from 2020 to 2022. This strategy was mainly planned in three areas: organizing a database with data from the hospitalizations; setting-up a multidisciplinary taskforce to conduct follow-up assessments after discharge; and organizing a biobank. Additionally, a COVID-19 curated collection was created within the institutional digital library of academic papers to map the research output.

Results: Over the course of the experience, the possible benefits and challenges of this type of research support approach were identified and discussed, leading to a set of recommended strategies to enhance collaboration within the research institution. Demographic and clinical data from COVID-19 hospitalizations were compiled in a database including adults and a minority of children and adolescents with laboratory confirmed COVID-19, covering 2020-2022, with approximately 350 fields per patient. To date, this database has been used in 16 published studies. Additionally, we assessed 700 adults 6 to 11 months after hospitalization through comprehensive, multidisciplinary in-person evaluations; this database, comprising around 2000 fields per subject, was used in 15 publications. Furthermore, thousands of blood samples collected during the acute phase and follow-up assessments remain stored for future investigations. To date, more than 3,700 aliquots have been used in ongoing research investigating various aspects of COVID-19. Lastly, the mapping of the overall research output revealed that between 2020 and 2022 our academic system produced 1,394 scientific articles on COVID-19.

Discussion: Research is a crucial component of an effective epidemic response, and the preparation process should include a well-defined plan for organizing and sharing resources. The initiatives described in the present paper were successful in our aim to foster large-scale research in our institution. Although a single model may not be appropriate for all contexts, cross-disciplinary collaboration and open data sharing should make health research systems more efficient to generate the best evidence.

Keywords: COVID-19; cross-disciplinarity; data management; data science; health data analysis; research collaboration; research data; research management.

Figure 1

Flow-chart of HC-FMUSP research-managing structure to foster large-scale cross-disciplinary collaborative research studies on COVID-19.

Figure 2

Data curation processes involved in the development of HC-FMUSP COVID-19 institutional databases. EHR, electronic health record; SQL, Structured Query Language; CSV, Comma-separated values; CRF, case report form.

Figure 3

Flowchart of the multidisciplinary follow-up assessment program cohort. ^aExclusion criteria: previous diagnosis of dementia or end-stage cancer, pregnant or postpartum patients, patients living in nursing homes or long-term care facilities or insufficient physical mobility to leave home. ^bParticipants who consented with remote assessments but declined the invitation to attend in-person appointments due to health concerns. Despite the implementation of measures to mitigate exposure risk during follow-up evaluations, there was still a level of heightened health apprehension, since the multidisciplinary follow-up assessments occurred between October 2020 and January 2021, when the pandemic was still at its peak in São Paulo.