Online medical consultation: a review

Singh, Ajeet; Joshi, Hari Shankar; Singh, Arun K.; Agarwal, Medhavi; Kaur, Palveen

doi:10.18203/2394-6040.ijcmph20181195

Cited by 18 publications

(9 citation statements)

References 2 publications

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“…[ 2 ] A narrative review on online medical consultation reported that convenience, changes in disease patterns, cost-effectiveness, privacy, and the need for a second opinion as to the common reasons for the increase in online tele-consultations. [ 6 ] These benefits were similar to our study. The common reasons for patient/family members to opt for TAC consultations were convenience, affordability of technology, and preference of consultation with the same doctor regularly.…”

supporting

confidence: 91%

Video consultations from tele aftercare clinic: An early experience from an Indian geriatric psychiatry service

Mukku

Manjunatha

Kumar

et al. 2021

Indian J Psychiatry

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supporting

confidence: 91%

Video consultations from tele aftercare clinic: An early experience from an Indian geriatric psychiatry service

Mukku

Manjunatha

Kumar

et al. 2021

Indian J Psychiatry

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“…A new era of medical consultation – online medical consultation (OMC) – has been ushered in (Singh et al, 2018), owing to technological advancements in healthcare industry. OMC here refers to Internet-based remote medical consultation which involves not only advice/information-giving, but also medical agendas, such as diagnosis, treatment recommendation and prescription (see Al-Mahdi et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Discursive strategies for addressing patients’ disalignment with diagnosis in online medical consultations in China

Zhang

2021

Discourse & Communication

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While online medical consultations (OMCs) have become increasingly popular, not least in times when epidemics make access to medical facilities difficult, research exploring the interactive value of OMC is still in its infancy. This study examines patients’ disalignment with speculative diagnosis and its management by doctors in OMC interactions, which to my best knowledge are not examined. From a discourse analytic perspective, this study adopts Du Bois’s notion of stance-taking to approach text-based interactions collected from a widely used OMC website in China. This study finds that patients’ disalignment is often based on hard evidence, which is different from the major findings of previous studies. When addressing such disalignment, doctors adopt varied discursive strategies, which are hardly mentioned in prior studies, in particular, strategies that address the affective aspect of patients’ disalignment. This study thus provides a different perspective on disalignment and its management in the clinical context.

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“…Despite the popularity and potential benefits, some online medical consultation platforms are struggling to attract and retain patients who are willing to pay for these services, for example, patient dissatisfaction after an initial failed experience, fear that diagnoses are made with limited consideration of patients' medical history, and concerns about privacy may impede patients' use of online consultation [10,11]. In addition, online medical consultation usually follows the Pareto principle in that 80% of the services are provided by 20% of the physicians on the platform [1], suggesting that many health care service providers on the platform have the challenge of attracting patients and standing out in the crowd of physicians who can provide comparable services [6,12].…”

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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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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Online medical consultation: a review

Cited by 18 publications

References 2 publications

Video consultations from tele aftercare clinic: An early experience from an Indian geriatric psychiatry service

Video consultations from tele aftercare clinic: An early experience from an Indian geriatric psychiatry service

Discursive strategies for addressing patients’ disalignment with diagnosis in online medical consultations in China

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

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