Abstract
BackgroundPatient 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.MethodsThis 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.ResultsA 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.ConclusionsIn 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.
Highlights
Patient experience surveys often include free-text responses
Patient experience surveys are a popular means of gathering feedback from patients
The patient experience survey of these hospitals starts with two openended questions: ‘What went remarkably well during your stay?’ (Q1) and ‘What did not go as well during your stay?’ (Q2)
Summary
Patient experience surveys often include free-text responses. Analysis of these responses is timeconsuming 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. Surveys often consist of a combination of closed- and open-ended questions. Closed-ended questions yield quantitative results that can be used to measure patient experiences and derive priorities for improvement [1]. Open-ended questions can complement quantitative measures by providing information on experiences not covered by closed-ended questions and by offering greater detail to help contextualize responses to closed questions. Free-text responses are often underutilized [2]. This may be because analysis of free-text responses requires substantial effort due to the unstructured nature of the responses. Raw freetext data from large scale surveys are not always analysed systematically, risking the loss of potentially valuable insights for hospital improvement
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