Understanding patient feedback on online doctor review platforms: Divergent topics, sentiments, and service needs by disease risk level.

INTRODUCTION: There is currently limited understanding of patient satisfaction with online healthcare platforms by disease risk levels. This study aims to collect online doctor reviews from online healthcare platforms in China to investigate patient satisfaction by disease risk levels, identify the factors influencing satisfaction, and understand patients' service needs. METHODS: Data were collected from Haodf.com, resulting in a total of 24,742 low-risk disease patient reviews and 9,821 high-risk disease patient reviews. BERTopic modeling analysis, sentiment analysis, Kano-IPA modeling analysis and statistical analysis were conducted to derive insights. RESULTS: This study identified 13 topics among low-risk patients and 10 topics among high-risk patients. For both low-risk (coef=0.26) and high-risk (coef=0.17) patients, clinical expertise was the top positive factor influencing satisfaction. Poor value for money was the top negative influencing factor for low-risk patient satisfaction, whereas inadequate information response was the top negative influencing factor for high-risk patient satisfaction. The dimension-level analysis suggests a potential moderating role of disease risk in the factors associated with patient satisfaction. The results of the Kano-IPA modeling analysis indicate that service needs differ between low-risk and high-risk patients. CONCLUSION: This study provides a comprehensive understanding of factors influencing patient satisfaction and patients' service needs in online healthcare service by disease risk levels. The findings offer valuable implications for doctors' service provision, online healthcare platform design and healthcare management.