Understanding a moderating effect of physicians' endorsement to online workload: An empirical study in online health-care communities

Yu, Haiyan; Xiang, Kun; Jiang, Yu

doi:10.1109/bigdata.2017.8258570

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…This type of digital health care service is experiencing significant growth and research attention [5]. These platforms offer many benefits, such as reduced medical costs, improved medical service efficiency, more efficient health care resource distribution, and fewer health care resource shortages in remote areas [2,[6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…As medical consultation is highly professional, physicians need to be credible or trustworthy to attract and retain paying patients. A physician's affiliation, seniority, and location are usually used as proxies for reputation [8,9,15,16]. Patient evaluation, which is the feedback left on the platform by previous patients about the physician, is also frequently examined [2,6,17].…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

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The Impact of Physician’s Login Behavior on Patients’ Search and Decision in OHCs

2019

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Which Physicians Attract Payment? Mining Massive Platform Data to Understand Patient Payment in Online Medical Consultation (Preprint)

Jiang¹,

Cameron²,

Yang³

2019

Preprint

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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.

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Understanding a moderating effect of physicians' endorsement to online workload: An empirical study in online health-care communities

Cited by 3 publications

References 5 publications

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

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

The Impact of Physician’s Login Behavior on Patients’ Search and Decision in OHCs

Which Physicians Attract Payment? Mining Massive Platform Data to Understand Patient Payment in Online Medical Consultation (Preprint)

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