Ethical considerations & privacy
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It is critical to ensure that the collection and dissemination of genomic and phenotypic information is achieved in a manner that respects the privacy rights of the patients from which it is derived. To achieve this goal, the eMERGE Network is developing procedures (often implemented in working software) to embed privacy protection strategies in the data life cycle. These procedures include the design of community advisory groups, patient consenting mechanisms, and de-identification methodologies that mitigate re-identification threats while maximizing the utility of the data shared for research purposes.
Informed consent is a fundamental and required element for Human Research Participants. Informed consent must clearly outline participation requirements, risks and benefits, and communicate that no coercion was used during participant recruitment; these elements ensure that recruited participants’ rights are protected throughout the course of the study. During the recruitment process potential participants are contacted and presented with a study relevant to them specifically. Once the participant has received the study information they can choose to participate in the study by providing their consent or they can decline participation.
Phase I eMERGE model informed consent language compilation
Offers a compilation of consent language for the collection and storage of human biospecimens and data for future research.
Phase II genomic medicine pilot and PGx consent forms
Examples of consent forms created by eMERGE sites and authorized for use in past eMERGE studies.
Phase III genomic medicine pilot and biobank consent forms
Examples of consent forms created by eMERGE sites and authorized for use in current eMERGE studies.
Consent, Education, Regulation, & Consultation (CERC) Survey
The CERC Survey is an experimental survey of 82,328 individuals from 11 healthcare systems in the eMERGE Network that assessed individual willingness to provide broad consent for sharing of biosamples and data.
Public Attitudes toward Consent and Data Sharing in Biobank Research
Sanderson SC, et al. Public Attitudes toward Consent and Data Sharing in Biobank Research. Am J Hum Genet. 2017 Mar 2;100(3):414-427
CERC Survey Data Access request
Data is available for research studies via Controlled Access.
The eMERGE Survey on Taking Part in Biobank Research: Version A
The eMERGE Survey on Taking Part in Biobank Research: Version B
The eMERGE Survey on Taking Part in Biobank Research: Version C
Natural language de-identification
Medical records contain both structured data (like a person’s age and race) as well as free text, for example in a clinic note or pathology report. When the medical records are used in a de-identified environment (with no HIPPA identifiers), it is difficult to transfer the free text that exists in the medical record. The eMERGE Network has been working on this problem and has published several articles looking at the costs and benefits of applying de-identification to these data in a medical record. Click on the manuscripts below to learn more:
Risk based de-identification and data sharing
The Network has also examined risk associated with de-identified data sharing. Though removing personal identfiers from health record data reduces the risk of re-identifying a person, there is still risk. The following three papers use modeling strategies to determine the risks surrounding large scale data sharing in a de-identfied environment. Click on the manuscripts below to learn more:
- Wan Z, Vorobeychik Y, Xia W, Clayton EW, Kantarcioglu M, Malin B. Expanding Access to Large-Scale Genomic Data While Promoting Privacy: A Game Theoretic Approach. Am J Hum Genet. 2017 Feb 2;100(2):316-322.PMID:28065469
- Wan Z, Vorobeychik Y, Kantarcioglu M, Malin B. Controlling the signal: Practical privacy protection of genomic data sharing through Beacon services. Am J Hum Genet. 2017 Feb 2;100(2):316-322. PMID:28786360
- Wan Z, Vorobeychik Y, Xia W, Clayton EW, Kantarcioglu M, Ganta R, Heatherly R, Malin BA. A game theoretic framework for analyzing re-identification risk. PLoS One. 2015 Mar 25;10(3):e0120592. PMID:25807380