Impact of policy on big data how do these policies

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Impact of Policy on Big Data How do these policies affect you? Data security and privacy – all those handling patients’ records must meet applicable privacy and security rules Only certain people can view patient records Disclosure of genetic information cannot be used against people for healthcare insurance or employment decisions Almost everyone in the US will/does have insurance records
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Discussion/ For Your Considerati on How might these laws ( HIPAA , GINA , Common Rule ) impact uses of big data? What more is required from an ethical perspective?
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Consent: Opt-in vs. Opt-out With Opt-in , by default you must give affirmative consent With Opt-out , by default you are a participant unless you revoke your consent Most applicable to research that isn’t federally-funded You can’t only opt-out of part of the research; you must opt-out of everything Discussion/For Your Consideration: Why might opt-in vs. opt-out consent be an issue for patients/advocates? How might it impact patients/advocates? Could one type of consent be more beneficial to research?
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Data Privacy: When Are Data No Longer Anonymou s? Enough data can be collected so that a patient is no longer anonymous For example: Some diseases only affect a very small population With enough information, people may be identified (via demographics, hospital/doctor location, food preferences, children, education, diseases, genetics, exercise activity, etc.) Some diseases or even just genetics (e.g. carrier status) can have social or ethical implications For example: Hormone therapy for individuals who are transgender Oral contraception for certain religious groups Abortion status HIV status
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Re- identificati on & Privacy With enough information, anonymized records can be used to identify people. For example: From genetic information : you can get racial background, genetic diseases, mitochondrial DNA (which indicates mother), and basic characteristics such as eye and hair color From insurance information : you can get age, weight, height, diseases, pregnancies, surgeries, current medication, and location of healthcare provider. From pharmacy records and prescription insurance : you can get current medications, which can indicate diseases, psychological diseases, and can indicate if someone is on hormone therapy From self-reported data : activity, location, weight, height, age, major medical conditions, sleep patterns, etc.
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Re- identificati on Example 1 Dr. Latanya Sweeney was able to link de- identified patient-specific medical data to a voter registration list to re-identify patients by name. Showed that 87% of the US population can be uniquely identified by three data points: date of birth, gender, 5- digit ZIP code.
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