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Healthcare and Big Data Management

Overview of attention for book
Attention for Chapter 1: How to Become a Smart Patient in the Era of Precision Medicine?
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Chapter title
How to Become a Smart Patient in the Era of Precision Medicine?
Chapter number 1
Book title
Healthcare and Big Data Management
Published in
Advances in experimental medicine and biology, January 2017
DOI 10.1007/978-981-10-6041-0_1
Pubmed ID
Book ISBNs
978-9-81-106040-3, 978-9-81-106041-0
Authors

Yalan Chen, Lan Yang, Hai Hu, Jiajia Chen, Bairong Shen

Abstract

The objective of this paper is to define the definition of smart patients, summarize the existing foundation, and explore the approaches and system participation model of how to become a smart patient. Here a thorough review of the literature was conducted to make theory derivation processes of the smart patient; "data, information, knowledge, and wisdom (DIKW) framework" was performed to construct the model of how smart patients participate in the medical process. The smart patient can take an active role and fully participate in their own health management; DIKW system model provides a theoretical framework and practical model of smart patients; patient education is the key to the realization of smart patients. The conclusion is that the smart patient is attainable and he or she is not merely a patient but more importantly a captain and global manager of one's own health management, a partner of medical practitioner, and also a supervisor of medical behavior. Smart patients can actively participate in their healthcare and assume higher levels of responsibility for their own health and wellness which can facilitate the development of precision medicine and its widespread practice.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 13%
Student > Bachelor 5 13%
Student > Master 4 10%
Professor 3 8%
Student > Ph. D. Student 3 8%
Other 6 15%
Unknown 14 35%
Readers by discipline Count As %
Medicine and Dentistry 6 15%
Computer Science 4 10%
Social Sciences 3 8%
Nursing and Health Professions 2 5%
Agricultural and Biological Sciences 2 5%
Other 8 20%
Unknown 15 38%