Document Type

Article

Publication Date

2025

DOI

10.3390/info16020090

Publication Title

Information

Volume

16

Issue

2

Pages

90 (1-17)

Abstract

Classic health-related quality of life (HRQOL) metrics are cumbersome, time-intensive, and subject to biases based on the patient’s native language, educational level, and cultural values. Natural language processing (NLP) converts text into quantitative metrics. Sentiment analysis enables subject matter experts to construct domain-specific lexicons that assign a value of either negative (−1) or positive (1) to certain words. The growth of telehealth provides opportunities to apply sentiment analysis to transcripts of adult spinal deformity patients’ visits to derive a novel and less biased HRQOL metric. In this study, we demonstrate the feasibility of constructing a spine-specific lexicon for sentiment analysis to derive an HRQOL metric for adult spinal deformity patients from their preoperative telehealth visit transcripts. We asked each of twenty-five (25) adult patients seven open-ended questions about their spinal conditions, treatment, and quality of life during telehealth visits. We analyzed the Pearson correlation between our sentiment analysis HRQOL metric and established HRQOL metrics (the Scoliosis Research Society-22 questionnaire [SRS-22], 36-Item Short Form Health Survey [SF-36], and Oswestry Disability Index [ODI]). The results show statistically significant correlations (0.43–0.74) between our sentiment analysis metric and the conventional metrics. This provides evidence that applying NLP techniques to patient transcripts can yield an effective HRQOL metric.

Rights

© 2025 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The open-ended questions in our study, the domain-specific lexicon for spinal deformity that is paired with the questions, the fictional patient responses that were used to alpha-test our domain, and the source code used to conduct the analysis are located online in a Mendeley Data repository [47]. Unfortunately, we are unable to share the actual patients responses due to IRB restrictions. Data are contained within the article and Supplementary Materials

Original Publication Citation

Gore, R., Safaee, M. M., Lynch, C. J., & Ames, C. P. (2025). A spine-specific lexicon for the sentiment analysis of interviews with adult spinal deformity patients correlate with SF-36, SF-36, and ODI scores: A pilot study of 25 patients. Information, 16(2), 1-17, Article 90. https://doi.org/10.3390/info16020090

ORCID

0000-0003-4065-6146 (Gore), 0000-0002-4830-7488 (Lynch)

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