Engaging citizens in experiments with computational analysis of patient stories: From unwarranted reductions to meaningful insights

Abstract

In recent years, citizen engagement in policy and research has gained considerable momentum. In the healthcare domain, patient narratives, through various mediums, have emerged as a valuable source of insight into the experiences of patients and the healthcare system. Recognizing the value of such textual data, diverse analytical methods have been developed, spanning from text mining to narrative analysis. This article presents experiments that combine computational methods, qualitative methods and citizen science for analyzing patients’ stories. In this article, we reflect on two experiments in which we combined these approaches, which we analyze through a generative lens. We distinguish three main effects of the experiments: they provide a platform for discussions as a 'site of controversy'; they act as 'mediator', fostering new connections and mutual understanding among participants; and they serve as 'tin opener', stimulating substantive discussions about methodological development and substantive healthcare matters. Narrative reduction, which occurs when rich narrative data is simplified into structured quantifiable forms, is not inherently problematic; instead, it can be meaningful when combined with qualitative methods and citizen science, emphasizing the importance of utilizing diverse methods to balance authenticity and gaining broader insights. The study highlights the significance of collaborative sense-making and meaning-making in interdisciplinary research. Engaging patients, their relatives, and professionals in the analytical process, facilitated by tools like word clouds, promotes engaged discussions that yield actionable insights. Further development of such interdisciplinary approaches holds promise for a more nuanced understanding of patient experiences, fostering epistemological pluralism, and refining healthcare practices.

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