By Adrian Gropper and Deborah C. Peel

Among other rich nations, US healthcare stands out as both exceptionally privatized and exceptionally expensive. And taken overall, we have the worst health outcomes among the Western Democracies.

On one hand, regulators are reluctant to limit private corporate action lest we reduce innovation and patient choice and promote moral hazards. On the other hand, a privatized marketplace for services requires transparency of costs and quality and a minimum of economic externalities that privatize profit and socialize costs.

For over two decades, the HIPAA law and regulations have dominated the way personal health data is used and abused to manipulate physician practice and increase costs. During these decades, digital technology has brought marvels of innovation and competition to markets as diverse as travel and publishing while healthcare technology is burning out physicians and driving patients to bankruptcy.

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By Adrian Gropper

Will the machines use our own personal information against us? The answer depends on privacy regulations that are yet to be written.

I know that the current approach to privacy regulation, be it general as in GDPR or sectoral as in HIPAA, is not readily extensible to a world where the principal value of personal data is machine learning. It’s easier to follow my logic if you agree that technology costs are already low compared to the value of personal data. Buying your own AI is increasingly sensible. Then, who will teach your personal AI in school, at work, in your community? You and your doctor will both have personal AI. What is the role of intellectual property when the cost of personal data dominates the cost of your AI? How do you use your AI to license the use of your personal data by others? Standards will be essential to maximize the market for your personal data, whether it’s sold or donated for the public good. These personal data standards are less-than-welcome in a process dominated by enterprises. Nonetheless, thoughtful privacy regulation designed around machine learning will keep humans responsible for the machines.

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