Identifying Patient-Reported Care Experiences in Free-Text Survey Comments: Topic Modeling Study
We found that with limited tuning, BERTopic identified care experience topics with interpretable automated labeling. Results are discussed in the context of person-centered care, patient safety, and health care quality improvement. Furthermore, we note the opportunity for the identification of temporal and site-specific trends as a method to identify patient care and safety concerns. As the use of patient experience measurement increases in health care, we discuss how machine learning can be leveraged to provide additional insight on patient experiences.
Authors
- Maria Santana (ORCID: https://orcid.org/0000-0002-0202-5952)
- Paul Fairie (ORCID: https://orcid.org/0000-0003-2611-7680)
- Adam G. D’Souza (ORCID: https://orcid.org/0000-0002-4270-2065)
- Matthias Wilms (ORCID: https://orcid.org/0000-0001-8845-360X)
- Kyle Kemp (ORCID: https://orcid.org/0000-0003-0138-5013)
- Brian Steele (ORCID: https://orcid.org/0000-0002-0081-1079)
Institutions
- Alberta Health Services (CA)
- University of Calgary (CA)
- Canadian Patient Safety Institute (CA)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2024-10-14
- DOI
- https://doi.org/10.2196/63466
- Citations
- 4
- Primary Topic
- Patient Satisfaction in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 3.40