Thursday evening I was following the great #PACCR feed on Twitter from a "Patients at Center of Clinical Research" discussion hosted by Eli Lilly Clinical Open Innovation team. (Thank you Rahlyn Gossen, @RebarInter, for the pointer)A couple of interesting comments came up in some tweets on the topic of de-identification. As de-identification (sometimes called anonymization) is a key topic for clinical trial data transparency, I did find these quotes really interesting.
It was said in the meeting by Regina Holliday (@ReginaHolliday), a great tweeter promoting patients rights within medicine..@reginaholliday No one even asks if clinical trial participants want to be de-identified. Some people don't want to. #PACCR— Rebar Interactive (@RebarInter) November 14, 2013Is it ethical to remove rare/genetic diseases within De-identified Data to protect against re-identification? http://t.co/cGRxe9Kzv5 #PACCR
— Daniel Barth-Jones (@dbarthjones) November 14, 2013Daniel Barth-Jones (@dbarthjones), Columbia University and expert in Data Privacy and De-identification Policy, asked in another tweet and referenced a very interesting blog post from Harvard Law School on Ethical Concerns, Conduct and Public Policy for Re-Identification and De-identification Practice."When re-identification risks are exaggerated, we need to recognize that the resulting fears cause needless harms. Such fears can push us toward diminishing our use of properly de-identified data, or distorting the accuracy of our statistical methods because we’ve engaged in ill-motivated de-identification and have altered data even in cases where there was not anything more than re-identification risks."
From the same blog post from the Online Symposium on the Law, Ethics & Science of Re-identification Demonstrations, at the Bill of Health at Harvard Law School, in the fields of health law policy, biotechnology, and bioethics.
“We must achieve an ethical equipoise between potential privacy harms and the very real benefits that result from the advancement of science and healthcare improvements which are accomplished with de-identified data."There were also a couple of interesting #PACCR tweets on the topic of Informed Consent quoting Sharon Terry (@sharonfterry), CEO of Genetic Alliance:
Informed consent information should be dynamic, granular, matrixed and contextual. @sharonfterry #PACCR #clinicaltrials
— Lilly Clinical OI (@Lilly_COI) November 14, 2013
I would like to learn more about this thinking and how they potentially could be realized by:
Structuring and formalizing the Informed Consent content to become a semantic rich, and machine-executable, contract/policy for transparency and accountability in using clinical trial data.For more information see:
- Permission Ontology for informed consent and HIPAA compliance (presentation in pdf) in the CTSA Ontology Workshop, Febr 2013
- Information accountability policy whose restrictions are based on usage rules, not access or collection rules testimony by Danny Weitzner (@djweitzner) in the Privacy and Civil Liberties Oversight Board Workshop Regarding Surveillance Programs Operated, July 2013
I do find all of this very interesting. And I hope such a "dynamic, granular, matrixed and contextual" approach can be part of new clinical trial data transparency policies:"To find solutions that are 'good enough' and provide both dramatic privacy protections and useful analytic data" (from the same blog post).