With the impetus of information communication technology (ICT), emerging eHealth has attracted increasing number of doctors’ participation in online health platforms, which provide various potential benefits to doctors. However, previous studies on eHealth have seldom distinguished different service modes provided by doctors. In addition, the bulk of the literature has considered doctors’ motivations based solely on online environments. To fill this gap, this study combines expectancy theory and the Bagozzi, Dholakia, and Basuroy (BDB) model to examine the relationships between anticipated outcomes, performance expectations, and effort intentions from online and offline perspectives. Doctors’ behavioral intentions are further divided into two categories: the willingness to offer free services and paid services. Using SmartPLS, this study conducts structural equation modeling (SEM) to analyze 311 sample data. The results show that extrinsic motivations (i.e., extrinsic rewards, expected relationships, and image) and intrinsic motivation (i.e., a sense of self-worth) significantly influence the desire to serve patients well, which in turn positively affects the willingness to offer free services and the willingness to offer paid services. Moreover, counseling time is confirmed as the main cost, which negatively moderates the relationships between desire and behavioral intentions. The findings provide theoretical insights for eHealth and provide practical suggestions to develop marketing strategies for online health platform providers.

Online healthcare services are growing rapidly. Patients can receive health support through communication with online health professionals. However, previous research on eHealth has focused on patients’ online behavioral intentions. The purpose of this study is to investigate the effect of online patient–doctor communication on offline behavioral intentions and to propose a conceptual model for eHealth. We integrate fairness theory and band extension theory to develop a research model. This is further tested by structural equation modeling (SEM), with 279 valid sets of data from patients on the online health platform. Using partial least squares analysis (PLS), the results show that perceived fairness is an important factor affecting online satisfaction and the willingness to ask online doctors, which in turn has a positive impact on the willingness to go to offline doctors. These findings shed light on the research model for eHealth and offer suggestions for improving patients’ online satisfaction, as well as both online and offline behavioral intentions.

Elderly patients with aged physical status and increased underlying disease suffered from more postoperative complication and mortality. We design this retrospective cohort study to investigate the relationship between existing comorbidity of elder patients and 30 day post-anesthetic mortality by using International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) from Health Insurance Database. Patients aged above 65 years old who received anesthesia between 2000 and 2010 were included from 1 million Longitudinal Health Insurance Database in (LHID) 2005 in Taiwan. We use age, sex, type of surgery to calculate propensity score and match death group and survival one with 1:4 ratio (death: survival = 1401: 5823). Multivariate logistic model with stepwise variable selection was employed to investigate the factors affecting death 30 days after anesthesia. Thirty seven comorbidities can independently predict the post-anesthetic mortality. In our study, the leading comorbidities predict post-anesthetic mortality is chronic renal disease (OR = 2.806), acute myocardial infarction (OR = 4.58), and intracranial hemorrhage (OR = 3.758). In this study, we present the leading comorbidity contributing to the postoperative mortality in elderly patients in Taiwan from National Health Insurance Database. Chronic renal failure is the leading contributing comorbidity of 30 days mortality after anesthesia in Taiwan which can be explained by the great number of hemodialysis and prolong life span under National Taiwan Health Insurance. Large scale database can offer enormous information which can help to improve quality of medical care.

Purpose: Locoregional therapies, such as transarterial embolization (TAE) and transarterial chemoembolization (TACE), are central to the treatment of unresectable hepatocellular carcinoma (HCC), particularly at the intermediate stage. However, there have been few large-scale real-world data comparisons of their effectiveness with that of systemic therapies. This study aimed to assess the relationships between different treatment modalities and all-cause mortality in a nationwide HCC cohort.