How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach

• Main Authors: Simone A. Cammel, Marit S. De Vos, Daphne van Soest, Kristina M. Hettne, Fred Boer, Ewout W. Steyerberg, Hileen Boosman
• Format: Article
• Language: English
• Published: BMC 2020-05-01
• Series: BMC Medical Informatics and Decision Making
• Subjects: Natural language processing; Patient experience analysis; PREM; Text analytics; Data science; Machine learning
• Online Access: http://link.springer.com/article/10.1186/s12911-020-1104-5

Abstract Background Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could provide a data-driven, hospital-independent solution to indicate points for quality improvement. Methods This retrospective study used routinely collected patient experience data from two hospitals. A data-driven NLP approach was used. Free-text responses were categorized into topics, subtopics (i.e. n-grams) and labelled with a sentiment score. The indicator ‘impact’, combining sentiment and frequency, was calculated to reveal topics to improve, monitor or celebrate. The topic modelling architecture was tested on data from a second hospital to examine whether the architecture is transferable to another hospital. Results A total of 38,664 survey responses from the first hospital resulted in 127 topics and 294 n-grams. The indicator ‘impact’ revealed n-grams to celebrate (15.3%), improve (8.8%), and monitor (16.7%). For hospital 2, a similar percentage of free-text responses could be labelled with a topic and n-grams. Between-hospitals, most topics (69.7%) were similar, but 32.2% of topics for hospital 1 and 29.0% of topics for hospital 2 were unique. Conclusions In both hospitals, NLP techniques could be used to categorize patient experience free-text responses into topics, sentiment labels and to define priorities for improvement. The model’s architecture was shown to be hospital-specific as it was able to discover new topics for the second hospital. These methods should be considered for future patient experience analyses to make better use of this valuable source of information.