Computer-assisted textual analysis of free-text comments in the Swiss Cancer Patient Experiences (SCAPE) survey.

ABSTRACT

BACKGROUND: Patient experience surveys are increasingly conducted in cancer care as they provide important results to consider in future development of cancer care and health policymaking. These surveys usually include closed-ended questions (patient-reported experience measures (PREMs)) and space for free-text comments, but published results are mostly based on PREMs. We aimed to identify the underlying themes of patients' experiences as shared in their own words in the Swiss Cancer Patient Experiences (SCAPE) survey and compare these themes with those assessed with PREMs to investigate how the textual analysis of free-text comments contributes to the understanding of patients' experiences of care. METHODS: SCAPE is a multicenter cross-sectional survey that was conducted between October 2018 and March 2019 in French-speaking parts of Switzerland. Patients were invited to rate their care in 65 closed-ended questions (PREMs) and to add free-text comments regarding their cancer-related experiences at the end of the survey. We conducted computer-assisted textual analysis using the IRaMuTeQ software on the comments provided by 31% (n = 844) of SCAPE survey respondents (n = 2755). RESULTS: We identified five main thematic classes, two of which consisting of a detailed description of 'cancer care pathways'. The remaining three classes were related to 'medical care', 'gratitude and praise', and the way patients lived with cancer ('cancer and me'). Further analysis of this last class showed that patients' comments related to the following themes 'initial shock', 'loneliness', 'understanding and acceptance', 'cancer repercussions', and 'information and communication'. While closed-ended questions related mainly to factual aspects of experiences of care, free-text comments related primarily to the personal and emotional experiences and consequences of having cancer and receiving care. CONCLUSIONS: A computer-assisted textual analysis of free-text in our patient survey allowed a time-efficient classification of free-text data that provided insights on the personal experience of living with cancer and additional information on patient experiences that had not been collected with the closed-ended questions, underlining the importance of offering space for comments. Such results can be useful to inform questionnaire development, provide feedback to professional teams, and guide patient-centered initiatives to improve the quality and safety of cancer care.