Should doctors use or avoid medical terms? The influence of medical terms on service quality of E-health

Abstract

With epidemics and pandemics like COVID-19, many offline healthcare services have been suspended and shifted to online, where patients and doctors typically communicate by typing texts. The limited communication poses a threat to the service quality of E-health, and also raises higher demand on the language skills of doctors, in which medical terms are a common concern. Traditional studies of offline healthcare mostly hold a negative attitude towards the use of medical terms by doctors. However, should we still advise doctors to avoid using medical terms in E-health? To answer this question, this paper conducts a study combining technical and empirical analyses based on real data. In this paper, a novel unsupervised text-mining method is proposed to automatically identify medical terms with crowd wisdom from large-scale doctor-patient communication texts. Then, a TREC-type experiment is carried out to validate the proposed method in terms of Precision, Recall, and F_1-measure, demonstrating that it can identify accurate and comprehensive medical terms. Based on the identified medical terms, an empirical analysis is conducted to verify the influence of medical terms used by doctors on the service quality of E-health. The analysis results show that for patients with low health literacy, the use of medical terms by doctors would decrease their service quality. However, for patients with high health literacy, the use of medical terms by doctors can significantly increase their service quality, revealing that doctors could improve their service quality in E-health by adjusting their medical term usage according to the health literacy of patients.