HRB Open Research (Sep 2022)
Generating actionable insights from free-text care experience survey data using qualitative and computational text analysis: A study protocol [version 1; peer review: 1 approved, 2 approved with reservations]
Abstract
Introduction: The National Care Experience Programme (NCEP) conducts national surveys that ask people about their experiences of care in order to improve the quality of health and social care services in Ireland. Each survey contains open-ended questions, which allow respondents to comment on their experiences. While these comments provide important and valuable information about what matters most to service users, there is to date no unified approach to the analysis and integration of this detailed feedback. The objectives of this study are to analyse qualitative responses to NCEP surveys to determine the key care activities, resources and contextual factors related to positive and negative experiences; to identify key areas for improvement, policy development, healthcare regulation and monitoring; and to provide a tool to access the results of qualitative analyses on an ongoing basis to provide actionable insights and drive targeted improvements. Methods: Computational text analytics methods will be used to analyse 93,135 comments received in response to the National Inpatient Experience Survey and National Maternity Experience Survey. A comprehensive analytical framework grounded in both service management literature and the NCEP data will be employed as a coding framework to underpin automated analyses of the data using text analytics and deep learning techniques. Scenario-based designs will be adopted to determine effective ways of presenting insights to knowledge users to address their key information and decision-making needs. Conclusion: This study aims to use the qualitative data collected as part of routine care experience surveys to their full potential, making this information easier to access and use by those involved in developing quality improvement initiatives. The study will include the development of a tool to facilitate more efficient and standardised analysis of care experience data on an ongoing basis, enhancing and accelerating the translation of patient experience data into quality improvement initiatives.
