Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review.

BACKGROUND: The secondary use of health data is central to biomedical research in the era of data science and precision medicine. National and international initiatives, such as the Global Open Findable, Accessible, Interoperable, and Reusable (GO FAIR) initiative, are supporting this approach in different ways (eg, making the sharing of research data mandatory or improving the legal and ethical frameworks). Preserving patients' privacy is crucial in this context. De-identification and anonymization are the two most common terms used to refer to the technical approaches that protect privacy and facilitate the secondary use of health data. However, it is difficult to find a consensus on the definitions of the concepts or on the reliability of the techniques used to apply them. A comprehensive review is needed to better understand the domain, its capabilities, its challenges, and the ratio of risk between the data subjects' privacy on one side, and the benefit of scientific advances on the other. OBJECTIVE: This work aims at better understanding how the research community comprehends and defines the concepts of de-identification and anonymization. A rich overview should also provide insights into the use and reliability of the methods. Six aspects will be studied: (1) terminology and definitions, (2) backgrounds and places of work of the researchers, (3) reasons for anonymizing or de-identifying health data, (4) limitations of the techniques, (5) legal and ethical aspects, and (6) recommendations of the researchers. METHODS: Based on a scoping review protocol designed a priori, MEDLINE was searched for publications discussing de-identification or anonymization and published between 2007 and 2017. The search was restricted to MEDLINE to focus on the life sciences community. The screening process was performed by two reviewers independently. RESULTS: After searching 7972 records that matched at least one search term, 135 publications were screened and 60 full-text articles were included. (1) Terminology: Definitions of the terms de-identification and anonymization were provided in less than half of the articles (29/60, 48%). When both terms were used (41/60, 68%), their meanings divided the authors into two equal groups (19/60, 32%, each) with opposed views. The remaining articles (3/60, 5%) were equivocal. (2) Backgrounds and locations: Research groups were based predominantly in North America (31/60, 52%) and in the European Union (22/60, 37%). The authors came from 19 different domains; computer science (91/248, 36.7%), biomedical informatics (47/248, 19.0%), and medicine (38/248, 15.3%) were the most prevalent ones. (3) Purpose: The main reason declared for applying these techniques is to facilitate biomedical research. (4) Limitations: Progress is made on specific techniques but, overall, limitations remain numerous. (5) Legal and ethical aspects: Differences exist between nations in the definitions, approaches, and legal practices. (6) Recommendations: The combination of organizational, legal, ethical, and technical approaches is necessary to protect health data. CONCLUSIONS: Interest is growing for privacy-enhancing techniques in the life sciences community. This interest crosses scientific boundaries, involving primarily computer science, biomedical informatics, and medicine. The variability observed in the use of the terms de-identification and anonymization emphasizes the need for clearer definitions as well as for better education and dissemination of information on the subject. The same observation applies to the methods. Several legislations, such as the American Health Insurance Portability and Accountability Act (HIPAA) and the European General Data Protection Regulation (GDPR), regulate the domain. Using the definitions they provide could help address the variable use of these two concepts in the research community.

[Bigdata, artificial intelligence and blockchain for dummies]. / BigData, intelligence artificielle, blockchain : guide pratique.

Digitalization is transforming every aspect of life, it is also transforming deeply medicine. The digitalization era is characterized by a large production of new data streams while existing processes are progressively migrated, such as writing or imaging. The very large and fast-growing amount of data available requires new storage, transport and analytical tools. This paper presents some of them, such as natural language processing, artificial intelligence, and graph databases. A short introduction to the technology of blockchain is also provided, as it is increasingly used in some non-monetary transaction in medicine, such as data exchanges and consent management.

La société en général, la médecine en particulier, sont emportées par la vague de la digitalisation. Ce phénomène s'appuie sur une production d'immenses quantités de données, parfois du fait de la dématérialisation de processus, comme l'écriture ou la photographie, parfois du fait de l'acquisition de nouvelles données, comme la géolocalisation. Ceci nécessite de nouveaux instruments pour le transport, le stockage et le traitement de l'information. Cet article présente quelques enjeux et instruments utilisés, telles les techniques de traitement du langage naturel, de l'intelligence artificielle et des bases de données en graphes. Enfin, nous décrivons brièvement la technologie de la blockchain, qui est de plus en plus proposée en médecine pour des processus non monétaires, tels que l'échange de données ou la gestion du consentement.

De-Identification of Medical Narrative Data.

Maintaining data security and privacy in an era of cybersecurity is a challenge. The enormous and rapidly growing amount of health-related data available today raises numerous questions about data collection, storage, analysis, comparability and interoperability but also about data protection. The US Health Portability and Accountability Act (HIPAA) of 1996 provides a legal framework and a guidance for using and disclosing health data. Practically, the approach proposed by HIPAA is the de-identification of medical documents by removing certain Protected Health Information (PHI). In this work, a rule-based method for the de-identification of French free-text medical data using Natural Language Processing (NLP) tools will be presented.

Common data model for natural language processing based on two existing standard information models: CDA+GrAF.

An increasing need for collaboration and resources sharing in the Natural Language Processing (NLP) research and development community motivates efforts to create and share a common data model and a common terminology for all information annotated and extracted from clinical text. We have combined two existing standards: the HL7 Clinical Document Architecture (CDA), and the ISO Graph Annotation Format (GrAF; in development), to develop such a data model entitled "CDA+GrAF". We experimented with several methods to combine these existing standards, and eventually selected a method wrapping separate CDA and GrAF parts in a common standoff annotation (i.e., separate from the annotated text) XML document. Two use cases, clinical document sections, and the 2010 i2b2/VA NLP Challenge (i.e., problems, tests, and treatments, with their assertions and relations), were used to create examples of such standoff annotation documents, and were successfully validated with the XML schemata provided with both standards. We developed a tool to automatically translate annotation documents from the 2010 i2b2/VA NLP Challenge format to GrAF, and automatically generated 50 annotation documents using this tool, all successfully validated. Finally, we adapted the XSL stylesheet provided with HL7 CDA to allow viewing annotation XML documents in a web browser, and plan to adapt existing tools for translating annotation documents between CDA+GrAF and the UIMA and GATE frameworks. This common data model may ease directly comparing NLP tools and applications, combining their output, transforming and "translating" annotations between different NLP applications, and eventually "plug-and-play" of different modules in NLP applications.

Architecture of a decision support system to improve clinicians' interpretation of abnormal liver function tests.

The objective of this work was to create a self-working computerized clinical decision support system (CDSS) able to analyze liver function tests (LFTs) in order to provide diagnostic suggestions and helpful care support to clinicians. We developed an expert system that processes exclusively para-clinical information to provide diagnostic propositions. Drugs are a major issue in dealing with abnormal LFTs, therefore we created a drug-disease causality assessment tool to include drugs in the differential diagnosis. Along with the results, the CDSS will guide clinicians in the care process offering them case-specific support in the form of guidelines, order sets and references to recent articles. The CDSS will be implemented in Geneva University Hospitals clinical information system (CIS) during year 2011. For the time being, preliminary tests have been conducted on case reports chosen randomly on Pubmed. Considered as medical challenges, case reports were nevertheless processed correctly by the program to the extent that 18 cases out of 20 were diagnosed accurately.