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
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% |