JMIR Publications

ABSTRACT

Background:

Online healthcare consultation has become increasingly popular, and is considered a potential solution to healthcare 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 healthcare providers on the platform have the additional challenge of standing out in a crowd of physicians who can provide comparable services.

Objective:

This study uses 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 non-linearly, 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 (i.e., hyperparameter tuning, model training, and validation) were applied with four classifiers – logistic regression, decision tree, random forest and gradient boost – to identify the most important features and their relative importance for predicting paid versus free-only appointments.

Results:

After applying the ML feature selection procedures, we identified 11 key features on the platform that 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. Decision tree analysis further identified five distinct subgroups of patients delineated by five top ranked features: previous offline connection, total dialogue, 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, as compared to features related to physician reputation, service-related features such as service delivery quality (e.g., consultation dialogue intensity, physician response rate), patient source (e.g., online versus offline returning patients) and patient involvement (e.g., provide social returns, reveal previous treatment) appear to contribute more to patient’s payment decision. Promoting multiple timely responses in patient-provider interactions is essential to encourage payment.