Ojo, A and Rizun, N and Isazad Mashinchi, M and Walsh, G S and Gruda, D and Narayana Rao, M and Venosa, M and Foley, C and Rohde, D and Flynn, R (2023) Generating actionable evidence from free-text feedback to improve maternity and acute hospital experiences: A computational text analytics & predictive modelling approach. European Journal of Public Health, 33 (Supple). ISSN 1101-1262

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Abstract

Background Patient experience surveys are a key source of evidence for supporting decision-making and quality improvement in healthcare services. These surveys contain two main types of questions: closed and open-ended, asking about patients’ care experiences. Apart from the knowledge obtained from analysing closed-ended questions, invaluable insights can be gleaned from free-text data. Advanced analytics techniques are increasingly used to harness free-text data, yet existing approaches do not offer the rigour required to support formal decision-making through free-text. Methods This study addresses the challenge of effectively and rigorously analysing patients’ free-text feedback to improve maternity and acute hospital services in Ireland. Aspects of healthcare services (i.e. themes) that could be improved were determined using computational text analytics and predictive modelling. Themes extracted from comments were prioritised based on volume, the intensity of negative affect expressed in the texts, and the estimated influence of the themes on overall patient satisfaction. Results Results demonstrate the viability of producing rigorous evidence for prioritising interventions to improve healthcare services based on free-text feedback. Specifically, consistency in advice and support in breastfeeding were among the most important issues for maternity services. For acute hospital services, meals quality and access, A&E waiting time, ward hygiene and communication at discharge were among the most important issues. Women also wanted more emphasis on prior birth experience and complications in future maternity care surveys. Conclusions Advances in computational text modelling enable the extraction of concrete and actionable insights from the analysis of free-text data. This approach also allows decision-makers to prioritise emergent themes and inform actions that will positively impact overall patient satisfaction.

Item Type:	Article
Keywords:	client satisfaction; breast feeding; decision making; feedback ireland patients; rooms hygiene; obstetrics prenatal care quality; improvement waiting time; closed-ended questions;
Academic Unit:	Faculty of Social Sciences > School of Business
Item ID:	18794
Identification Number:	https://doi.org/10.1093/eurpub/ckad160.517
Depositing User:	Jon Gruda
Date Deposited:	22 Aug 2024 11:14
Journal or Publication Title:	European Journal of Public Health
Publisher:	Oxford University Press
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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