Harmonizing Qualitative Data Across Multiple Health Systems to Identify Quality Improvement Interventions: A Methodological Framework Using PROSPR II Cervical Research Center Data as Exemplar

Abstract

Background: Heterogeneity in healthcare systems’ organizational structures, policies and decisions influences practice implementation and care delivery. While quantitative data harmonization has been used to compare outcomes, few have conducted cross-site qualitative inquiry of healthcare delivery; thus, little is known about how to harmonize qualitative data across multiple settings. Objective: We illustrate a methodological approach for a theory-driven qualitative data harmonization process for the PROSPR II Cervical Research Center, a large multi-site, mixedmethods study evaluating cervical cancer screening across three diverse healthcare settings. Methods: We compared three geographically, socio-demographically, and structurally diverse healthcare systems using a multi-modal qualitative data collection strategy. We grounded our sampling strategy in a cervical cancer screening process model, then tailored it for system-specific differences (e.g., clinic staffing structure and individual roles). Data collection tools included domains corresponding to shared research objectives (e.g., abnormal follow-up) while accommodating local context. Analysis drew on operational domains from the screening process model and constructs from the Consolidated Framework for Implementation Research and Normalization Process Theory. Results: Exemplars demonstrate how data harmonization revealed insights suggesting opportunities to improve clinical processes across healthcare systems. Discussion: This analysis advances the application of qualitative methods in implementation science, where assessing context is key to responding to organizational challenges and shaping implementation strategies across multiple health systems. We demonstrate how systematically collecting, analyzing and harmonizing qualitative data elucidates the impact of process factors and accelerates efforts to identify opportunities for quality improvement interventions.