International Journal of Management IT and Engineering
  • Year: 2017
  • Volume: 7
  • Issue: 10

Social Media Analytics in HEALTH CARE

  • Author:
  • Subash Thota
  • Total Page Count: 18
  • Page Number: 263 to 280

Data Architect

Online published on 11 October, 2019.

Abstract

Recent years have seen expanding enthusiasm for the patient-focused care and call to focus on enhancing the patient experience. In the meantime, a substantial number of patients are utilizing the web to describe and share their experiences. We believe the growing availability of patients ’accounts of their care on blogs, social networks, Twitter and hospital review sites presents an intriguing opportunity to advance the patient-centered care agenda and provide a unique quality of health data. Social media has progressed beyond being a tool for sharing private lives like pictures, videos, and messages especially by young individuals to fostering serious and useful discussion on technology and business. On the other hand, the need for ‘Get it right the first time ’and minimize costs remain a major concern in the healthcare industry. Customer feedback thus plays a vital role in growing business. In this article, we review the importance of social media analytics in improving business in the healthcare industry. With the advent of social media into healthcare discussions, it has emerged as a vital source of information which if analyzed could unleash new insights to enhance Health Care. With the emergence of Big Data analytics, trend and predictive analysis have garnered useful business insights to a wide range of industries. One of the significant challenges, researchers trying to address is the interoperability among patient and their records. Here, in this article, we attempt to illustrate the hurdles and various possibilities of Social Media and how streaming data from APIs can be subjected to Big Data analytics in the field of Health Care.

Keywords

Data Analytics, Health Analytics, Social Analytics, Data Management, Information Quality, Data Mitigation, Metadata, Data Profiling