Denaxas, SC;
Asselbergs, FW;
Moore, JH;
(2016)
The tip of the iceberg: challenges of accessing hospital electronic health record data for biological data mining.
BioData Mining
, 9
, Article 29. 10.1186/s13040-016-0109-1.
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Abstract
Modern cohort studies include self-reported measures on disease, behavior and lifestyle, sensor-based observations from mobile phones and wearables, and rich -omics data. Follow-up is often achieved through electronic health record (EHR) linkages across primary and secondary healthcare providers. Historically however, researchers typically only get to see the tip of the iceberg: coded administrative data relating to healthcare claims which mainly record billable diagnoses and procedures. The rich data generated during the clinical pathway remain submerged and inaccessible. While some institutions and initiatives have made good progress in unlocking such deep phenotypic data within their institutional realms, access at scale still remains challenging. Here we outline and discuss the main technical and social challenges associated with accessing these data for data mining and hauling the entire iceberg.
| Type: | Article |
|---|---|
| Title: | The tip of the iceberg: challenges of accessing hospital electronic health record data for biological data mining |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s13040-016-0109-1 |
| Publisher version: | http://doi.org/10.1186/s13040-016-0109-1 |
| Language: | English |
| Additional information: | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Mathematical & Computational Biology, MEDICINE |
| UCL classification: | UCL 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 > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1521951 |
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