This paper is in the following e-collection/theme issue:

Clinical Communication, Electronic Consultation and Telehealth (615) Cost-Effectiveness and Economics (259) Email Communication, Web-Based Communication, Secure Messaging (165) Big Data (245) Machine Learning (1501)

Published on in Vol 8, No 2 (2020): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16765, first published .
Analysis of Massive Online Medical Consultation Service Data to Understand Physicians’ Economic Return: Observational Data Mining Study

Analysis of Massive Online Medical Consultation Service Data to Understand Physicians’ Economic Return: Observational Data Mining Study

Analysis of Massive Online Medical Consultation Service Data to Understand Physicians’ Economic Return: Observational Data Mining Study

Authors of this article:

Jinglu Jiang1 Author Orcid Image ;   Ann-Frances Cameron2 Author Orcid Image ;   Ming Yang3 Author Orcid Image
Article Authors Cited by (13) Tweetations (1) Metrics

Journals

  1. Jiang J, Yang M, Kiang M, Cameron A. Exploring the freemium business model for online medical consultation services in China. Information Processing & Management 2021;58(3):102515 View
  2. Wang H, Avillach P. Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning. JMIR Medical Informatics 2021;9(4):e24754 View
  3. Subrahmanya S, Shetty D, Patil V, Hameed B, Paul R, Smriti K, Naik N, Somani B. The role of data science in healthcare advancements: applications, benefits, and future prospects. Irish Journal of Medical Science (1971 -) 2022;191(4):1473 View
  4. Sun Y, Gao D, Shen X, Li M, Nan J, Zhang W. Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext + CNN (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study. JMIR Medical Informatics 2022;10(4):e35606 View
  5. Cao B, Huang W, Chao N, Yang G, Luo N. Patient Activeness During Online Medical Consultation in China: Multilevel Analysis. Journal of Medical Internet Research 2022;24(5):e35557 View
  6. Wang R, Huang Y, Zhang X, Yao Y. Online dialogue with medical professionals: An empirical study of an online “Ask the Doctor” platform. International Journal of Medical Informatics 2023;177:105123 View
  7. Liu H, Zhang Y, Li Y, Albright K. Better interaction performance attracts more chronic patients? Evidence from an online health platform. Information Processing & Management 2023;60(4):103413 View
  8. Yang M, Jiang J, Cameron A, Liu X. How do you cope? Online medical consultation service uncertainty, coping strategies, and subsequent payment. Electronic Commerce Research and Applications 2023;61:101294 View
  9. Liu J, Song M, Li C, Guo S, Han J. The Effect of Characteristics of Patient Communication on Physician Feedback in Online Health Communities: An Observational Cross-Sectional Study. Health Communication 2024;39(13):3009 View
  10. Liu X, Guo H, Wang L, Hu M, Wei Y, Liu F, Wang X. Effect of Prosocial Behaviors on e-Consultations in a Web-Based Health Care Community: Panel Data Analysis. Journal of Medical Internet Research 2024;26:e52646 View
  11. Yang D, Zhu K, Wang R. Forecasting healthcare service volumes with machine learning algorithms. Journal of Forecasting 2024;43(6):2358 View
  12. Shen T, Li Y, Chen X. A Systematic Review of Online Medical Consultation Research. Healthcare 2024;12(17):1687 View

Books/Policy Documents

  1. Kessler S. The International Encyclopedia of Health Communication. View