The Influence of Doctors’ Online Reputation on the Sharing of Outpatient Experiences: Empirical Study (Preprint)

Abstract

BACKGROUND Reviews are important for consumers to make informed decisions in online communities and for organizations to predict sales in the future. Most existing literature are conducted in product fields, with little attention paid to healthcare. Whether patients prefer to use these new platforms to discuss the reputation of doctors has so far remained an open question. OBJECTIVE We investigate how patients’ propensity to post the treatment experiences (PPPTE) changes with doctors’ (individual) online reputation (medical quality and service attitude) for outpatient care service. We also investigate the moderating effects of hospital (organizational) online reputation and disease severity. METHODS The fractional logistic regression under the GLM framework based on data from 7,183 doctors within a Chinese online health community are used to get the empirical results. RESULTS Our results show that patients prefer to share their treatment experiences for doctors who have higher medical quality and service attitude and work in higher online reputation hospitals. Comparing with the doctors who treat less severe diseases, PPPTE is smaller for doctors who treat severe diseases. In addition, the hospital’s online reputation positively (negatively) moderates the relationship between medical quality (service attitude) and PPPTE. Further, the moderating effects of disease severity on the doctor’s online reputation are negative. CONCLUSIONS Our research contributes to both theory and practice by researching the impact of individual reputation on consumer behavior, investigating the moderating effects of organizational reputation and consumer characteristics in healthcare.

Medicine-Engineering Interdisciplinary Research Based on Bibliometric Analysis: A Case Study on Medicine-Engineering Institutional Cooperation of Shanghai Jiao Tong University

“…Intriguing chainof-thought (CoT) techniques have greatly fully exploited the emergent ability of LLMs by eliciting them to decompose multi-step reasoning. Recent work in this field can be broadly classified into four categories: (i) Improving the performance of general-purpose reasoning tasks (Wei et al, 2022;Kojima et al, 2022a;Wang et al, 2022b;Zhou et al, 2022;Fu et al, 2022), i.e., arithmetic, symbolic, logical, and commonsense reasoning; (ii) Applying to domain-specific reasoning, such as multi-modality , or some purely linguistic tasks, such as translation (He et al, 2023), summarization , sentiment analysis (Fei et al, 2023), question-answer , etc; (iii) Analyzing the mechanics and interpretability of CoT (Wang et al, 2022a;Lyu et al, 2023); (iv) Distilling CoT techniques for smaller models (Ho et al, 2022;Kim et al, 2023).…”