Yearb Med Inform 2017; 26(01): 68-71
DOI: 10.15265/IY-2017-032
Special Section: Learning from Experience: Secondary Use of Patient Data
Georg Thieme Verlag KG Stuttgart

Secondary Use of Patient Data: Review of the Literature Published in 2016

D. R. Schlegel
Department of Computer Science, SUNY Oswego, Oswego NY, USA
G. Ficheur
Department of Medical Informatics, EA 2694, Lille University Hospital, France
› Author Affiliations
Further Information

Publication History

Publication Date:
11 September 2017 (online)


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.

Learning from Experience: Secondary Use of Patient Data

Chen J, Podchiyska T, Altman R. OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records. J Am Med Inform Assoc 2016;23:339-48

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;6:26094

Prasser F, Kohlmayer F, Kuhn KA. The Importance of Context: Risk-based De-identification of Biomedical Data. Methods Inf Med 2016;55:347-55

Saez C, Zurriaga O, Perez-Panades J, Melchor I, Robles M, Garcia-Gomez JM. 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