Identifying Patients' Preference During Their Hospital Experience. A Sentiment and Topic Analysis of Patient-Experience Comments via Natural Language Techniques.
Jie Yuan, Xiao Chen, Chun Yang, JianYou Chen, PengFei Han, YuHong Zhang, YuXia Zhang
{"title":"Identifying Patients' Preference During Their Hospital Experience. A Sentiment and Topic Analysis of Patient-Experience Comments via Natural Language Techniques.","authors":"Jie Yuan, Xiao Chen, Chun Yang, JianYou Chen, PengFei Han, YuHong Zhang, YuXia Zhang","doi":"10.2147/PPA.S526623","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Open-ended questions in patient experience surveys provide a valuable opportunity for people to express and discuss their authentic opinions. The analysis of free-text comments can add value to quantitative measures by offering information which matters most to patients and by providing detailed descriptions of the service issues that closed-ended items may not cover.</p><p><strong>Objective: </strong>To extract useful information from large amounts of free-text patient experience comments and to explore differences in patient satisfaction and loyalty between patients who provided negative comments and those who did not.</p><p><strong>Methods: </strong>We collected free-text comments on a broad, open-ended question in a cross-sectional patient satisfaction survey. We adopted a mixed-methods approach involving a literature review, human annotation, and natural language processing technique to analyze free-text comments. The associations of patient satisfaction and loyalty scores with the occurrence of certain patient comments were tested via logistic regression analysis.</p><p><strong>Results: </strong>In total, 28054 free-text comments were collected (comment rate: 72.67%). The accuracy of the machine learning approach and the deep learning approach for topic modeling and sentiment analysis was 0.98 and 0.91 respectively, indicating a satisfactory prediction. Participants tended to leave positive comments (69.0%, 19356/28054). There were 22 patient experience themes discussed in the open-ended comments. The regression analysis showed that the occurrence of negative comments about \"humanity of care\", \"information, communication, and education\", \"sense of responsibility of staff\", \"technical competence\", \"responding to requests\", and \"continuity of care\" was significantly associated with a worse patient satisfaction and loyalty, while the occurrence of negative comments about other aspects of healthcare services had no impact on patient satisfaction and loyalty.</p><p><strong>Conclusion: </strong>The results of this study highlight the interpersonal and functional aspects of care, especially the interpersonal aspects, which are often the \"moment of truth\" during a service encounter when patients critically evaluate hospital services.</p>","PeriodicalId":19972,"journal":{"name":"Patient preference and adherence","volume":"19 ","pages":"2027-2037"},"PeriodicalIF":2.0000,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12276748/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Patient preference and adherence","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/PPA.S526623","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Open-ended questions in patient experience surveys provide a valuable opportunity for people to express and discuss their authentic opinions. The analysis of free-text comments can add value to quantitative measures by offering information which matters most to patients and by providing detailed descriptions of the service issues that closed-ended items may not cover.
Objective: To extract useful information from large amounts of free-text patient experience comments and to explore differences in patient satisfaction and loyalty between patients who provided negative comments and those who did not.
Methods: We collected free-text comments on a broad, open-ended question in a cross-sectional patient satisfaction survey. We adopted a mixed-methods approach involving a literature review, human annotation, and natural language processing technique to analyze free-text comments. The associations of patient satisfaction and loyalty scores with the occurrence of certain patient comments were tested via logistic regression analysis.
Results: In total, 28054 free-text comments were collected (comment rate: 72.67%). The accuracy of the machine learning approach and the deep learning approach for topic modeling and sentiment analysis was 0.98 and 0.91 respectively, indicating a satisfactory prediction. Participants tended to leave positive comments (69.0%, 19356/28054). There were 22 patient experience themes discussed in the open-ended comments. The regression analysis showed that the occurrence of negative comments about "humanity of care", "information, communication, and education", "sense of responsibility of staff", "technical competence", "responding to requests", and "continuity of care" was significantly associated with a worse patient satisfaction and loyalty, while the occurrence of negative comments about other aspects of healthcare services had no impact on patient satisfaction and loyalty.
Conclusion: The results of this study highlight the interpersonal and functional aspects of care, especially the interpersonal aspects, which are often the "moment of truth" during a service encounter when patients critically evaluate hospital services.
期刊介绍:
Patient Preference and Adherence is an international, peer reviewed, open access journal that focuses on the growing importance of patient preference and adherence throughout the therapeutic continuum. The journal is characterized by the rapid reporting of reviews, original research, modeling and clinical studies across all therapeutic areas. Patient satisfaction, acceptability, quality of life, compliance, persistence and their role in developing new therapeutic modalities and compounds to optimize clinical outcomes for existing disease states are major areas of interest for the journal.
As of 1st April 2019, Patient Preference and Adherence will no longer consider meta-analyses for publication.