What Key Factors Affect Patient Satisfaction on Online Medical Consultation Platforms? A Case Study from China
Bibliographic record
Abstract
Background/Objectives: Online medical consultation (OMC) platforms have become an essential tool for facilitating communication between doctors and patients, providing an efficient way for patients to access healthcare services. However, research on the key drivers of patient satisfaction within this context remains limited. This study aims to identify and prioritize the key factors influencing patient satisfaction on OMC platforms, with a focus on the Chinese “Chunyu Doctor” app as a case study. Methods: Data from patient comments on the “Chunyu Doctor” app were collected and analyzed using grounded theory to identify the influencing factors of patient satisfaction. The decision-making trial and evaluation laboratory (DEMATEL) method was then applied to assess and prioritize the factors influencing patient satisfaction, identifying the key determinants from a complex set of potential influences. Results: The study identified 11 key factors out of 23 that significantly impact patient satisfaction. These factors include doctors provide professional treatment plans, doctors accurately understand patients’ concerns, doctors explain and advise on prescriptions, doctors personally respond, doctors provide comprehensive replies, cost-effectiveness, consultation fees, effectiveness of treatment outcomes, reasonableness of the doctors’ consultation process, avoidance of templated responses by doctors, and alignment of doctors responses with patient expectations. Conclusions: This study enriches the understanding of patient satisfaction in the context of online medical consultations. The findings offer theoretical insights for future research and provide practical implications for enhancing the management and development of OMC platforms, improving the quality of healthcare services, and boosting patient satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".