Public Perception on Healthcare Services: Evidence from Social Media Platforms in China

被引:18
作者
Hu, Guangyu [1 ,2 ]
Han, Xueyan [1 ,2 ]
Zhou, Huixuan [1 ,2 ]
Liu, Yuanli [1 ,2 ]
机构
[1] Chinese Acad Med Sci, Sch Publ Hlth, Beijing 100730, Peoples R China
[2] Peking Union Med Coll, Beijing 100730, Peoples R China
基金
中国国家自然科学基金;
关键词
healthcare; social media; China; WeChat; Qzone; natural language processing; DOCTOR;
D O I
10.3390/ijerph16071273
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Social media has been used as data resource in a growing number of health-related research. The objectives of this study were to identify content volume and sentiment polarity of social media records relevant to healthcare services in China. A list of the key words of healthcare services were used to extract data from WeChat and Qzone, between June 2017 and September 2017. The data were put into a corpus, where content analyses were performed using Tencent natural language processing (NLP). The final corpus contained approximately 29 million records. Records on patient safety were the most frequently mentioned topic (approximately 8.73 million, 30.1% of the corpus), with the contents on humanistic care having received the least social media references (0.43 Million, 1.5%). Sentiment analyses showed 36.1%, 16.4%, and 47.4% of positive, neutral, and negative emotions, respectively. The doctor-patient relationship category had the highest proportion of negative contents (74.9%), followed by service efficiency (59.5%), and nursing service (53.0%). Neutral disposition was found to be the highest (30.4%) in the contents on appointment-booking services. This study added evidence to the magnitude and direction of public perceptions on healthcare services in China's hospital and pointed to the possibility of monitoring healthcare service improvement, using readily available data in social media.
引用
收藏
页数:10
相关论文
共 37 条
  • [1] Patient experience with outpatient encounters at public hospitals in Shanghai: Examining different aspects of physician services and implications of overcrowding
    Bao, Yuhua
    Fan, Guanrong
    Zou, Dongdong
    Wang, Tong
    Xue, Di
    [J]. PLOS ONE, 2017, 12 (02):
  • [2] China Internet Network Information Center, 41 CHIN STAT REP INT
  • [3] What Predicts Patients' Adoption Intention Toward mHealth Services in China: Empirical Study
    Deng, Zhaohua
    Hong, Ziying
    Ren, Cong
    Zhang, Wei
    Xiang, Fei
    [J]. JMIR MHEALTH AND UHEALTH, 2018, 6 (08):
  • [4] Gonzalez-Hernandez G, 2017, Yearb Med Inform, V26, P214, DOI 10.15265/IY-2017-029
  • [5] Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online
    Greaves, Felix
    Ramirez-Cano, Daniel
    Millett, Christopher
    Darzi, Ara
    Donaldson, Liam
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2013, 15 (11)
  • [6] Harnessing the cloud of patient experience: using social media to detect poor quality healthcare
    Greaves, Felix
    Ramirez-Cano, Daniel
    Millett, Christopher
    Darzi, Ara
    Donaldson, Liam
    [J]. BMJ QUALITY & SAFETY, 2013, 22 (03) : 251 - 255
  • [7] The Development of Online Doctor Reviews in China: An Analysis of the Largest Online Doctor Review Website in China
    Hao, Haijing
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2015, 17 (06) : e134
  • [8] Predicting HCAHPS scores from hospitals' social media pages: A sentiment analysis
    Huppertz, John W.
    Otto, Peter
    [J]. HEALTH CARE MANAGEMENT REVIEW, 2018, 43 (04) : 359 - 367
  • [9] Instagram and WhatsApp in Health and Healthcare: An Overview
    Boulos, Maged N. Kamel
    Giustini, Dean M.
    Wheeler, Steve
    [J]. FUTURE INTERNET, 2016, 8 (03):
  • [10] Systematic review of surveillance by social media platforms for illicit drug use
    Kazemi, Donna M.
    Borsari, Brian
    Levine, Maureen J.
    Dooley, Beau
    [J]. JOURNAL OF PUBLIC HEALTH, 2017, 39 (04) : 763 - 776