Superior medical resources or geographic proximity? The joint effects of regional medical resource disparity, geographic distance, and cultural differences on online medical consultation

Online medical consultation platforms enable patients to seek health advice from physicians across geographic regions. In this study, we analyze patterns of online consultation between patients and physicians. We examine the joint effects of regional medical resource disparity, geographic distance, and cultural differences between patients and physicians on patients' decisions about which physicians they consult online. Using a unique dataset of city-to-city tuples based on 813,684 online consultation records and combining it with region-level data from multiple external sources, we find that while regional medical resource disparity drives patients from medically disadvantaged regions to seek online consultations with physicians from medically advantaged regions, geographic distance and cultural differences tend to constrain these consultations. We also find that cultural differences can amplify the impact of regional medical resource disparity, whereas geographic distance may lessen this effect. Further, we discover that the constraining effect of geographic distance is partly due to the online-to-offline nature of online medical consultations. Moreover, additional analyses suggest that physicians' online reputation and information about physicians' participation on the platform can help alleviate the negative effects of geographic distance and cultural differences. These findings hold significant implications for the allocation of medical resources and the formulation of healthcare policies.

Communication model in Chinese online medical consultations: Insights and implications

OBJECTIVE

To comprehensively analyze and further enhance the established E4 communication model for online medical counseling in Chinese settings, by proposing the novel E5 model. Additionally, it aims to evaluate the performance of Chinese doctors in fulfilling the E5 model.

METHODS

Through thematic analysis and grounded theory of 500 online medical consultations in China, we developed the extended E5 model from the E4 model. We identified four dimensions of patient attitudes and behaviors using Stanford Topic Modeling Toolbox, then employed Chi-square analysis to investigate their influence on doctors' performance of E5 model.

RESULTS

Our study illustrates that the extended E5 model, with its operable strategies, accurately mirrors the nuanced dynamics of online medical counseling in China, significantly varying in doctors' execution in response to the four identified dimensions of patient attitudes and behaviors.

CONCLUSION

The extended E5 model, coupled with insights into patient attitudes and behaviors, provides a comprehensive framework for understanding and enhancing communication in China's online healthcare context.

PRACTICE IMPLICATIONS

The findings highlight the necessity for doctor training in the E5 model for effective online communication. Furthermore, fostering conducive relationship between patients and doctors could potentially boost doctors' E5 performance.

Repetitions in online doctor-patient communication: Frequency, functions, and reasons

OBJECTIVES

To attain insights into language repetition during online doctor-patient communication (DPC), understand why doctors and patients use repetition, and improve the current deficiencies in online medical platforms.

METHODS

The study performed a content analysis of 72 sets of textual doctor-patient conversations on the Chinese online medical consultation platform Chunyu Doctor.

RESULTS

Repetitions occurred 1412 times in the 72 sets of online DPC. Patient self-repetitions were the most prevalent (30.7 %), while patient repetitions of doctors were the least common (17.1 %). Doctors used repetitions for explanations and affirmations. Patients used repetition for emphasis, verification, and turn-taking. The repetition frequencies of doctors and patients were primarily influenced by personal factors. However, doctor-dimension factors exerted a greater impact on the frequency of patient repetitions.

CONCLUSIONS

The reasons for repetitions in online DPC differ from those offline. Online DPC increases patient initiative and reduces doctor authority. Nevertheless, it could be affected by gender stereotypes generated during offline consultations. Doctors still dominated the conversations but attended sufficiently to the patient discourse. Online DPC is gradually attempting to fulfill the expectations of a new patient-centered healthcare pattern.

PRACTICAL IMPLICATIONS

The findings yield suggestions for healthcare providers and the designers of online healthcare platforms.

Small talk is a big deal: A discursive analysis of online off-topic doctor-patient interaction in Traditional Chinese Medicine

This study aims to examine the small talk of Traditional Chinese Medicine (TCM) during online medical consultation (OMC). With the textual and audio doctor-patient conversations collected from Chunyu Doctor, an asynchronous and synchronous e-health platform in mainland China, this study systematically analyzed 432 pieces of TCM consultations. Results indicate that TCM doctors actively initiate small talks in online scenarios to acquire holistic information for diagnosis and boost patients' face for rapport management, both of which further contribute to patient-centeredness in Chinese OMC. Importantly, TCM doctors attach great importance to small talk, while patients perceive it with insufficient attention. To some extent, this study contributes to existing knowledge of small talk by examining its informative and interpersonal functions under the online circumstance of TCM in Oriental scenarios.

Cross domains adversarial learning for Chinese named entity recognition for online medical consultation

Deep learning methods have been applied to Chinese named entity recognition for the online medical consultation. They require a large number of marked samples. However, no such database is available at present. This paper begins with constructing a larger labelled Chinese texts database for the online medical consultation. Second, a basic framework unit is proposed, which is pre-trained by the transfer learning from both Bidirectional language model and Mask language model trained on the larger unlabelled data. Finally, cross domains adversarial learning (CDAL) for Chinese named entity recognition is proposed to further improve the performance, which not only uses the pre-trained basic framework unit, but also uses the adversarial multi-task learning on both electronic medical record texts and online medical consultation texts. Experimental results validate the effectiveness of CDAL.

Virtualized healthcare delivery: understanding users and their usage patterns of online medical consultations

BACKGROUND

Virtualization of healthcare delivery via patient portals has facilitated the increasing interest in online medical consultations due to its benefits such as improved convenience and flexibility, lower cost, and time savings. Despite this growing interest, adoption by both consumers and providers has been slow, and little is known about users and their usage and adoption patterns.

OBJECTIVE

To learn characteristics of online healthcare consumers and understand their patterns of adoption and usage of online clinical consultation services (or eVisits delivered via the portal) such as adoption time for portal users, whether adoption hazard changes over time, and what factors influence patients to become early/late adopters.

METHODS

Using online medical consultation records between April 1, 2009 and May 31, 2010 from four ambulatory practices affiliated with a major healthcare provider, we conduct simple descriptive analysis to understand the users of online clinical consults and their usage patterns. Multilevel Logit regression is employed to measure the effect of patient and primary care provider characteristics on the likelihood of eVisit adoption by the patient, and survival analysis and Ordered Logit regression are applied to study eVisit adoption patterns that delineate elements describing early or late adopters.

RESULTS

On average, eVisit adopters are younger and predominantly female. Their primary care providers participate in the eVisit service, highlighting the importance of physician's role in encouraging patients to utilize the service. Patients who are familiar with the patient portal are more likely to use the service, as are patients with more complex health issues. Younger and female patients have higher adoption hazard, but gender does not affect the decision of adopting early vs. late. These adopters also access the patient portal more frequently before adoption, indicating that they are potentially more involved in managing their health. The majority of eVisits are submitted during business hours, with female physicians responding faster (from submission to reply), on average.

CONCLUSIONS

This study addresses virtualization of primary care delivery via patient portals and online clinical consultations and examines factors that distinguish eVisit adopters from patient portal users. Among many delineating characteristics, it is particularly significant that familiarity with the patient portal service and participation of primary care provider are found to be key elements that motivate patients to become an eVisit user and early/late adopter. These findings can be used by provider organizations to design and implement strategies to improve uptake of online medical consultations to complement traditional office visits. Offering such alternative channels of care delivery may potentially improve access, efficiency and outcomes for both patients and providers alike.