ArticleDigital health
Evaluating online doctor-patient communication quality and its effects: Text analysis approach.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Managing uncertainty in antibiotic decisions: factors influencing antibiotic use among chinese parents of children in a multi-source information environment.International journal of qualitative studies on health and well-being · 2026Article
- Person-First or Disease-First? Language Choices in Cancer Communication.Nursing reports (Pavia, Italy) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Building upon information quality assessment frameworks and social exchange theory, this study quantifies online doctor-patient communication quality across three dimensions using text analytics methods. Focusing on gynecological cancers online consultations, we examine how communication quality affects patient choice and satisfaction, while simultaneously investigating the moderating roles of service price and doctor engagement. Methods: This study collected 19,392 doctor-patient interaction records from a leading Chinese online health platform through web crawling. Employing an interdisciplinary methodology integrating natural language processing, machine learning, and sentiment analysis for variable measurement, this study conducted empirical analyses using multivariate linear regression models. Results: The study reveals that all three communication quality dimensions (thematic similarity, terminological similarity, and emotional expression similarity) exert significant positive effects on both patient choice and satisfaction. Furthermore, online service price and doctor engagement are found to positively moderate the relationship between communication quality and doctor performance, suggesting that higher-priced services and more engaged doctors amplify the beneficial impact of quality communication. Conclusions: This research contributes to the theoretical understanding of doctor-patient communication in digital healthcare by extending existing frameworks and providing robust empirical evidence. The findings offer new insights into the mechanisms underlying online medical interactions and present fresh perspectives for optimizing communication models. From a practical standpoint, the study provides actionable recommendations for platform operators, including the implementation of targeted communication training programs for doctors to enhance their ability to deliver relevant and comprehensible information, as well as the development of terminology conversion systems to facilitate patient understanding of medical jargon, thereby improving overall communication quality.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.