How to automatically turn patient experience free-text responses into actionable insights
| Autor: | Simone A. Cammel, Daphne van Soest, Ewout W. Steyerberg, Marit S. de Vos, Hileen Boosman, Kristina Hettne, F. Boer |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Topic model
Quality management Computer science PREM Health Informatics Text analytics lcsh:Computer applications to medicine. Medical informatics computer.software_genre Health informatics Data science 03 medical and health sciences 0302 clinical medicine Patient experience analysis Patient experience Machine learning Text messaging Humans 030212 general & internal medicine Architecture Language Retrospective Studies Text Messaging business.industry 030503 health policy & services Health Policy Natural language processing Natural language programming Quality Improvement Hospitals Computer Science Applications Patient Outcome Assessment Categorization lcsh:R858-859.7 Artificial intelligence 0305 other medical science business computer Research Article |
| Zdroj: | BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-10 (2020) BMC Medical Informatics and Decision Making BMC Medical Informatics and Decision Making, 20(1). BMC |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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