Constructing and De-Constructing Patient Experience Via Big Data and Small Data

Sociologists have increasingly offered critical reflections on the ways in which patient experience is shared in digital formats, and commodified and harnessed as forms of usable data. Some have commented on the methodological issues and potential for such Big Data sources to be used for new social research insights. However, there has been a steady and growing interest in the routine use of such data to inform service improvement underpinned by policy goals to enhance the quality and safety of healthcare services. Existing sociological research has considered some of the tensions inherent in drawing upon Big Data sources to generate in-depth insights regarding patient experience that has traditionally been researched within sociology using in-depth qualitative and interpretative methodologies. However, there has been a lack of research focused on the construction and deconstruction of patient experience data in terms of how it is generated and analysed to create new knowledge on patient experience within specific healthcare contexts.

In this paper, we draw on qualitative research conducted as part of a project to develop and evaluate new digital tools, in order to critically reflect on the construction and deconstruction of digital knowledge on patient experience within four service delivery areas for people with long-term conditions. Computer scientists developed text mining programmes within the wider project for analysing large volumes of free text comments within the NHS Trusts. Responses to these new methods were also considered within the qualitative research.

The findings highlight the scope and limitations of applying a Big Data approach that was perceived to have value at higher levels in large organisations. There was a perceived need to generate and use Small Data based on interactive and highly contextual mechanisms in order to generate more meaningful (and useful) data.