Title |
Can learning health systems help organisations deliver personalised care?
|
---|---|
Published in |
BMC Medicine, October 2017
|
DOI | 10.1186/s12916-017-0935-0 |
Pubmed ID | |
Authors |
Bright I. Nwaru, Charles Friedman, John Halamka, Aziz Sheikh |
Abstract |
There is increasing international policy and clinical interest in developing learning health systems and delivering precision medicine, which it is hoped will help reduce variation in the quality and safety of care, improve efficiency, and lead to increasing the personalisation of healthcare. Although reliant on similar policies, informatics tools, and data science and implementation research capabilities, these two major initiatives have thus far largely progressed in parallel. In this opinion piece, we argue that they should be considered as complementary, synergistic initiatives whereby the creation of learning health systems infrastructure can support and catalyse the delivery of precision medicine that maximises the benefits and minimises the risks associated with treatments for individual patients. We illustrate this synergy by considering the example of treatments for asthma, which is now recognised as an umbrella term for a heterogeneous group of related conditions. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 8 | 40% |
United States | 3 | 15% |
Spain | 2 | 10% |
Colombia | 1 | 5% |
Austria | 1 | 5% |
Unknown | 5 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 11 | 55% |
Scientists | 7 | 35% |
Science communicators (journalists, bloggers, editors) | 1 | 5% |
Practitioners (doctors, other healthcare professionals) | 1 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 94 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 17 | 18% |
Student > Master | 13 | 14% |
Researcher | 12 | 13% |
Student > Doctoral Student | 4 | 4% |
Professor | 4 | 4% |
Other | 15 | 16% |
Unknown | 29 | 31% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 19 | 20% |
Computer Science | 6 | 6% |
Nursing and Health Professions | 6 | 6% |
Economics, Econometrics and Finance | 4 | 4% |
Social Sciences | 4 | 4% |
Other | 18 | 19% |
Unknown | 37 | 39% |