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Systems Medicine 2.0: Potential Benefits of Combining Electronic Health Care Records With Systems Science Models

Tillmann, T; Gibson, AR; Scott, G; Harrison, O; Dominiczak, A; Hanlon, P; (2015) Systems Medicine 2.0: Potential Benefits of Combining Electronic Health Care Records With Systems Science Models. Journal of Medical Internet Research , 17 (3) e64. 10.2196/jmir.3082. Green open access

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Abstract

Background: The global burden of disease is increasingly dominated by non-communicable diseases.These diseases are less amenable to curative and preventative interventions than communicable disease. This presents a challenge to medical practice and medical research, both of which are experiencing diminishing returns from increasing investment. / Objective: Our aim was to (1) review how medical knowledge is generated, and its limitations, (2) assess the potential for emerging technologies and ideas to improve medical research, and (3) suggest solutions and recommendations to increase medical research efficiency on non-communicable diseases. / Methods: We undertook an unsystematic review of peer-reviewed literature and technology websites. / Results: Our review generated the following conclusions and recommendations. (1) Medical knowledge continues to be generated in a reductionist paradigm. This oversimplifies our models of disease, rendering them ineffective to sufficiently understand the complex nature of non-communicable diseases. (2) Some of these failings may be overcome by adopting a “Systems Medicine” paradigm, where the human body is modeled as a complex adaptive system. That is, a system with multiple components and levels interacting in complex ways, wherein disease emerges from slow changes to the system set-up. Pursuing systems medicine research will require larger datasets. (3) Increased data sharing between researchers, patients, and clinicians could provide this unmet need for data. The recent emergence of electronic health care records (EHR) could potentially facilitate this in real-time and at a global level. (4) Efforts should continue to aggregate anonymous EHR data into large interoperable data silos and release this to researchers. However, international collaboration, data linkage, and obtaining additional information from patients will remain challenging. (5) Efforts should also continue towards “Medicine 2.0”. Patients should be given access to their personal EHR data. Subsequently, online communities can give researchers the opportunity to ask patients for direct access to the patient’s EHR data and request additional study-specific information. However, selection bias towards patients who use Web 2.0 technology may be difficult to overcome. / Conclusions: Systems medicine, when combined with large-scale data sharing, has the potential to raise our understanding of non-communicable diseases, foster personalized medicine, and make substantial progress towards halting, curing, and preventing non-communicable diseases. Large-scale data amalgamation remains a core challenge and needs to be supported. A synthesis of “Medicine 2.0” and “Systems Science” concepts into “Systems Medicine 2.0” could take decades to materialize but holds much promise.

Type: Article
Title: Systems Medicine 2.0: Potential Benefits of Combining Electronic Health Care Records With Systems Science Models
Open access status: An open access version is available from UCL Discovery
DOI: 10.2196/jmir.3082
Publisher version: http://dx.doi.org/10.2196/jmir.3082
Language: English
Additional information: © 2015 Taavi Tillmann, Alexander R Gibson, Gregory Scott, Oliver Harrison, Anna Dominiczak, Phil Hanlon. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 23.03.2015. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
Keywords: gene-environment interaction; systems theory; electronic health records; epidemiology; online social networks; crowd-sourcing; Web 2.0
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health
URI: https://discovery.ucl.ac.uk/id/eprint/1465945
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