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DOI: 10.15265/IY-2017-032
Secondary Use of Patient Data: Review of the Literature Published in 2016
Publication History
Publication Date:
11 September 2017 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00034612/201701/lookinside/thumbnails/10-15265-iy-2017-032-1.jpg)
Summary
Objectives: To summarize recent research and emerging trends in the area of secondary use of healthcare data, and to present the best papers published in this field, selected to appear in the 2017 edition of the IMIA Yearbook.
Methods: A literature review of articles published in 2016 and related to secondary use of healthcare data was performed using two bibliographic databases. From this search, 941 papers were identified. The section editors independently reviewed the papers for relevancy and impact, resulting in a consensus list of 14 candidate best papers. External reviewers examined each of the candidate best papers and the final selection was made by the editorial board of the Yearbook.
Results: From the 941 retrieved papers, the selection process resulted in four best papers. These papers discuss data quality concerns, issues in preserving privacy of patients in shared datasets, and methods of decision support when consuming large amounts of raw electronic health record (EHR) data.
Conclusion: In 2016, a significant effort was put into the development of new systems which aim to avoid significant human understanding and pre-processing of healthcare data, though this is still only an emerging area of research. The value of temporal relationships between data received significant study, as did effective information sharing while preserving patient privacy.
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References
- 1 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521: 436-44.
- 2 Ryan PB, Stang PE, Overhage JM, Suchard MA, Hartzema AG, DuMouchel W. et al. A comparison of the empirical performance of methods for a risk identification system. Drug Saf 2013; 36 Suppl 1: S143-58.
- 3 Li G, Sajobi TT, Menon BK, Korngut L, Lowerison M, James M. et al. Registry-based randomized controlled trials- what are the advantages, challenges, and areas for future research?. J Clin Epidemiol 2016; 80: 16-24.
- 4 Shivade C, Raghavan P, Fosler-Lussier E, Embi PJ, Elhadad N, Johnson SB. et al. A review of approaches to identifying patient phenotype cohorts using electronic health records. J Am Med Inform Assoc 2014; 21: 221-30.
- 5 Lamy J-B, Séroussi B, Griffon N, Kerdelhué G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med 2015; 54: 135-44.
- 6 Sahoo SS, Zhang G-Q, Bamps Y, Fraser R, Stoll S, Lhatoo SD. et al. Managing information well: Toward an ontology-driven informatics platform for data sharing and secondary use in epilepsy self-management research centers. Health Informatics J 2016; 22: 548-61.
- 7 Kamdar MR, Tudorache T, Musen MA. A systematic analysis of term reuse and term overlap across biomedical ontologies. Semantic Web 2016; 1-19.
- 8 Sauer BC, Jones BE, Globe G, Leng J, Lu CC, He T. et al. Performance of a Natural Language Processing (NLP) Tool to Extract Pulmonary Function Test (PFT) Reports from Structured and Semistructured Veteran Affairs (VA) Data. EGEMS (Wash DC) 2016; 04: 1217.
- 9 Sáez C, Zurriaga O, Pérez-Panadés J, Melchor I, Robles M, García-Gómez JM. et al. Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories. J Am Med Inform Assoc 2016; 23: 1085-95.
- 10 Demner-Fushman D, Kohli MD, Rosenman MB, Melchor I, Robles M, García-Gómez JM. Preparing a collection of radiology examinations for distribution and retrieval. J Am Med Inform Assoc 2016; 23: 304-10.
- 11 Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M, Baak A. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016; 03: 160018.
- 12 Prasser F, Kohlmayer F, Kuhn KA. The Importance of Context: Risk-based De-identification of Bio-medical Data. Methods Inf Med 2016; 55: 347-55.
- 13 Goldstein BA, Pencina MJ, Montez-Rath ME, Winkelmayer WC. Predicting mortality over different time horizons: which data elements are needed?. J Am Med Inform Assoc 2017; 24: 176-81.
- 14 Chen JH, Podchiyska T, Altman RB. OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records. J Am Med Inform Assoc 2016; 23: 339-48.
- 15 Miotto R, Li L, Kidd BA, Dudley JT. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Sci Rep 2016; 06: 26094.
- 16 Singh K, Betensky RA, Wright A, Curhan GC, Bates DW, Waikar SS. A Concept-Wide Association Study of Clinical Notes to Discover New Predictors of Kidney Failure. Clin J Am Soc Nephrol 2016; 11: 2150-8.