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

Chen, Qin; Yan, Xiangbin; Zhang, Tingting

doi:10.1007/978-3-030-34482-5_14

Cited by 2 publications

(5 citation statements)

References 32 publications

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“…), they received in offline hospitals. Several e-health studies have also shown that the review score and review volume are linked to the physician's performance (Chen et al. , 2019; Khurana et al.…”

Section: Methodsmentioning

confidence: 99%

“…), they received in offline hospitals. Several e-health studies have also shown that the review score and review volume are linked to the physician's performance (Chen et al, 2019;Khurana et al, 2019;Liu et al, 2016;Shah et al, 2019). H_feedback and V_feedback signify the physician service feedback on both platforms.…”

Section: Popularity Feedback and Physician Demandmentioning

confidence: 99%

“…Based on the available information from prior patients about physicians, patients decide whether to consult a particular physician or not (Yang et al, 2020). Following the signaling theory, a physician sends positive signals to help patients choose a competent physician (Chen et al, 2019;Li et al, 2019a;Lu and Wu, 2019). Recent studies show that a physician online reputation in the form of ratings reflects the physician performance (Li et al, , 2019bLin et al, 2016;Shah et al, 2021c).…”

Section: Service Feedback and Physician Demandmentioning

confidence: 99%

“…, 2020). Following the signaling theory, a physician sends positive signals to help patients choose a competent physician (Chen et al. , 2019; Li et al.…”

Section: Research Hypothesesmentioning

confidence: 99%

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Investigating the effect of service feedback and physician popularity on physician demand in the virtual healthcare environment

Shah

Muhammad

Lee

2022

ITP

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PurposeThis study examines how service feedback and physician popularity affect physician demand in the context of virtual healthcare environment. Based on the signaling theory, the critical factor of environment uncertainty (i.e. disease risk) and its impact on physician demand is also investigated. Further, the research on the endogeneity of online reviews in healthcare is also examined in the current study.Design/methodology/approachA secondary data econometric analysis using 3-wave data sets of 823 physicians obtained from two PRWs (Healthgrades and Vitals) was conducted. The analysis was run using the difference-in-difference method to consider physician and website-specific effects.FindingsThe study's findings indicate that physician popularity has a stronger positive effect on physician demand compared with service feedback. Improving popularity leads to a relative increase in the number of appointments, which in turn enhance physician demand. Further, the impact of physician popularity on physician demand is positively mitigated by the disease risk.Originality/valueThe authors' research contributes to a better understanding of the signaling transmission mechanism in the online healthcare environment. Further, the findings provide practical implications for key stakeholders into how an efficient feedback and popularity mechanism can be built to enhance physician service outcomes in order to maximize the financial efficiency of physicians.

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Section: Methodsmentioning

confidence: 99%

Section: Popularity Feedback and Physician Demandmentioning

confidence: 99%

Section: Service Feedback and Physician Demandmentioning

confidence: 99%

“…, 2020). Following the signaling theory, a physician sends positive signals to help patients choose a competent physician (Chen et al. , 2019; Li et al.…”

Section: Research Hypothesesmentioning

confidence: 99%

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Investigating the effect of service feedback and physician popularity on physician demand in the virtual healthcare environment

Shah

Muhammad

Lee

2022

ITP

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“…Although PRWs have been available for over two decades, the earliest research on different signal transmission on PRWs was published in 2010 [107], and the majority of studies were conducted (61/63, 96.8 per cent) after 2015. As shown in Figure 3, most of the studies belonged to China (38) [7,15,28,[30][31][32][33][34]37,38,40,[44][45][46][47][48][49][50][51][52][53][54]56,60,61,63,65,73,81,104,106,[108][109][110][111][112][113] followed by the U.S. ( 9) [4,5,14,34,35,67,78,99,…”

Section: Time and Place Of The Studymentioning

confidence: 99%

Examining Different Factors in Web-Based Patients’ Decision-Making Process: Systematic Review on Digital Platforms for Clinical Decision Support System

Shah

Muhammad

Lee

2021

IJERPH

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(1) Background: The appearance of physician rating websites (PRWs) has raised researchers’ interest in the online healthcare field, particularly how users consume information available on PRWs in terms of online physician reviews and providers’ information in their decision-making process. The aim of this study is to consistently review the early scientific literature related to digital healthcare platforms, summarize key findings and study features, identify literature deficiencies, and suggest digital solutions for future research. (2) Methods: A systematic literature review using key databases was conducted to search published articles between 2010 and 2020 and identified 52 papers that focused on PRWs, different signals in the form of PRWs’ features, the findings of these studies, and peer-reviewed articles. The research features and main findings are reported in tables and figures. (3) Results: The review of 52 papers identified 22 articles for online reputation, 15 for service popularity, 16 for linguistic features, 15 for doctor–patient concordance, 7 for offline reputation, and 11 for trustworthiness signals. Out of 52 studies, 75% used quantitative techniques, 12% employed qualitative techniques, and 13% were mixed-methods investigations. The majority of studies retrieved larger datasets using machine learning techniques (44/52). These studies were mostly conducted in China (38), the United States (9), and Europe (3). The majority of signals were positively related to the clinical outcomes. Few studies used conventional surveys of patient treatment experience (5, 9.61%), and few used panel data (9, 17%). These studies found a high degree of correlation between these signals with clinical outcomes. (4) Conclusions: PRWs contain valuable signals that provide insights into the service quality and patient treatment choice, yet it has not been extensively used for evaluating the quality of care. This study offers implications for researchers to consider digital solutions such as advanced machine learning and data mining techniques to test hypotheses regarding a variety of signals on PRWs for clinical decision-making.

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

Cited by 2 publications

References 32 publications

Investigating the effect of service feedback and physician popularity on physician demand in the virtual healthcare environment

Investigating the effect of service feedback and physician popularity on physician demand in the virtual healthcare environment

Examining Different Factors in Web-Based Patients’ Decision-Making Process: Systematic Review on Digital Platforms for Clinical Decision Support System

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