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

Abstract

Background Online health care consultation has become increasingly popular and is considered a potential solution to health care resource shortages and inefficient resource distribution. However, many online medical consultation platforms are struggling to attract and retain patients who are willing to pay, and health care providers on the platform have the additional challenge of standing out in a crowd of physicians who can provide comparable services. Objective This study used machine learning (ML) approaches to mine massive service data to (1) identify the important features that are associated with patient payment, as opposed to free trial–only appointments; (2) explore the relative importance of these features; and (3) understand how these features interact, linearly or nonlinearly, in relation to payment. Methods The dataset is from the largest China-based online medical consultation platform, which covers 1,582,564 consultation records between patient-physician pairs from 2009 to 2018. ML techniques (ie, hyperparameter tuning, model training, and validation) were applied with four classifiers—logistic regression, decision tree (DT), random forest, and gradient boost—to identify the most important features and their relative importance for predicting paid vs free-only appointments. Results After applying the ML feature selection procedures, we identified 11 key features on the platform, which are potentially useful to predict payment. For the binary ML classification task (paid vs free services), the 11 features as a whole system achieved very good prediction performance across all four classifiers. DT analysis further identified five distinct subgroups of patients delineated by five top-ranked features: previous offline connection, total dialog, physician response rate, patient privacy concern, and social return. These subgroups interact with the physician differently, resulting in different payment outcomes. Conclusions The results show that, compared with features related to physician reputation, service-related features, such as service delivery quality (eg, consultation dialog intensity and physician response rate), patient source (eg, online vs offline returning patients), and patient involvement (eg, provide social returns and reveal previous treatment), appear to contribute more to the patient’s payment decision. Promoting multiple timely responses in patient-provider interactions is essential to encourage payment.

The role of data science in healthcare advancements: applications, benefits, and future prospects

“…Consultation online is becoming increasingly common and a possible solution to the scarcity of healthcare resources and inefficient delivery of resources. Numerous online consultation sites do however struggle to attract customers who are prepared to pay and maintain them, and health care providers on the site have the additional challenge to stand out from a large number of doctors who can provide similar services [45]. In this research, Jiang et al [45] used ML approaches to mine massive service data, in order (1) to define the important characteristics related to patient payment rather than free trial appointments, (2) explore the relative importance of those features, and (3) understand how these attributes work concerning payment, whether linearly or not.…”

“…Numerous online consultation sites do however struggle to attract customers who are prepared to pay and maintain them, and health care providers on the site have the additional challenge to stand out from a large number of doctors who can provide similar services [45]. In this research, Jiang et al [45] used ML approaches to mine massive service data, in order (1) to define the important characteristics related to patient payment rather than free trial appointments, (2) explore the relative importance of those features, and (3) understand how these attributes work concerning payment, whether linearly or not. The dataset refers to the largest online medical consultation platform in China, covering 1,582,564 consultation documents among patient pairs between 2009 and 2018.…”

“…Harpaz R et al [47] improved precision in the identification of ADRs by jointly considering several data sources. When using EHRs that are available publicly in conjunction with the AER studies of the FDA, they achieved a 31% (on average) increase in detection [45]. The authors identified dose-dependent ADRs with the help of models built from structured as well as unstructured EHR data [48].…”

Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning

“…As the number of variants was too large to apply deep learning models directly, to construct the features for the deep learning models, we used feature selection to reduce variant dimension (Figure 1B). Feature selection is one of the core concepts in machine learning that hugely impacts the performance of a model [32][33][34][35]. The data features that are used to train machine learning models have a huge influence on the performance that we can achieve.…”

Analysis of Online Medical Services Availability during Covid-19 Pandemic In Nepal

“…The findings offer managerial insights on managing patients' continuous consultation behaviors and enhancing their satisfaction by considering time scope and service type with important insights into how online medical care can be delivered more effectively, so relieving the demand for traditional healthcare system capacity (Yang et al 2019). the promoting multiple timely responses in patient-provider interactions is essential to encourage payment (Jiang et al 2020). Policies and promotions could attract more doctors to provide Web-based consultation and result could be a reference for policy making to improve the medical system both online and offline (Lil et al 2019).…”