ABSTRACT
Recent years have witnessed the rapid growth of an emerging digital healthcare service - online medical consultation (OMC). Despite its popularity, many OMC platforms have encountered issues in initial adoption and continuance use among patients. We posit that many of the hesitation and resistance may arise from a lack of trust toward OMC, which is a complex phenomenon that involves both interpersonal and technological-oriented considerations. This study seeks to clarify the conceptualization of online trust in the context of OMC. It compares two plausible explanations (i.e., trust as a direct cause vs. trust as a moderator) regarding how interpersonal and technological trust contributes to the service continuance decision in OMC. By contextualizing the valence framework, we identify the critical factors in making the risk-benefit assessment of patients' OMC decision. We conduct an online survey of 365 experienced OMC users and analyze our structural model using a partial least square approach. Our results show that the multidimensional conceptualization approach, which incorporates both interpersonal and technological aspects of trust, is superior to the unitary approach. Besides, our findings suggest that the role trust plays in determining service continuance decisions in OMC is more of a direct cause than a qualifier that buffers the impacts of risk-benefit evaluation. We believe the findings can help both researchers and practitioners recognize the multidimensional perspective of trust and better understand the role trust plays in OMC and other online healthcare delivery problems.
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.
ABSTRACT
A novel active zinc substrate-induced sequential self-construction method is presented for the fabrication of hydrated WO(3) hierarchical octahedrons, flakes, lanterns, and arresting sandwiched double-layer nanorods arrays architectures for the first time. Photocatalytic activity and gas sensing properties of the as-obtained various WO(3).0.33H(2)O architectures were studied as well.
