Working With Large Qualitative Datasets: Key Processes From an Implementation Study in a Complex Clinical Setting

Collecting and analyzing qualitative research with large datasets is inherently complex and often difficult to convey in traditional manuscripts. Contemporary guidance on managing such datasets, especially those with multiple data sources, is limited. Implementation science, which studies and tests methods to promote uptake of evidence-based practices into routine use, often requires complex qualitative data and analysis and faces unique challenges. These challenges include multiple data collection time points and the need for rapid analysis and feedback to sites. In this paper we reflect on the data collection, management, and analysis of over 400 pieces of qualitative data from three different sources (semi-structured interviews, meeting minutes, and open-ended survey responses) as part of a large implementation study. The data discussed are derived from a type 3 Hybrid-design study of H-HOPE (Hospital to Home: Optimizing the Preterm Infant's Environment) in six diverse neonatal intensive care units (NICUs). We first thoroughly outline the methods our team used in collecting and analyzing our large qualitative dataset. We then discuss four key processes that aided our data collection, management, and analysis: structure and consistency, prompt responsive alterations to site specific procedures, an iterative and extensive analysis approach led by case summaries, and utilization of both an initial 'quick' and more in-depth traditional qualitative analysis approaches. Recommendations for future studies working with large qualitative data sets with multiple sources of data are discussed.