OBJECTIVES: Exploration of meaningful change (MC) is critical for interpretation of scores for patient-reported outcome measures (PROMs) and determining the benefit of medical interventions with respect to how patients feel and function. In addition to established statistical approaches, qualitative techniques (e.g., interviews) for exploring patient perspectives on changes in symptoms and health-related quality of life (HRQoL) measured by PROMs can be valuable for informing definitions of MC. Established approaches for visualizing quantitative MC data are available (e.g., distribution functions), but limited consensus or guidance exists for illustration of qualitative MC data. This evidence review will explore approaches for visualizing qualitative MC, outlining their strengths and limitations.

METHODS: A targeted literature review of key bibliographic databases and conference proceedings (Embase, APA PsycINFO and Ovid MEDLINE®) was conducted. Searches utilized key words to identify references related to qualitative MC research (e.g., ‘meaningful improvement’, ‘interview’). Identified records were screened according to pre-specified criteria. Information regarding the representation of MC results was extracted, alongside details such as PROM type (e.g., diary, global scale) and response scale.

RESULTS: The targeted search yielded a total of n=666 search results. Following abstract screening, n=23 articles were selected for full review. Graphical visualizations of qualitative MC data were found to be limited, with most researchers presenting written summaries or tables. Drawing from prior approaches, we outline key considerations for qualitative MC visualization. Examples provided account for study variability including sample size, data type (e.g. continuous total score, ordinal item score), and type of questioning.

CONCLUSIONS: The value of qualitative MC data for understanding treatment benefit from the patient perspective is increasingly recognized. To facilitate communication and maximize impact of MC research findings to diverse stakeholders (including regulators, payers, clinicians and patients), researchers should critically consider best approaches to the visualization of MC data on a case-by-case basis.