People are more likely to avoid loss than to seek gains. HIPAA creates a framework where it rewards risk adverse behavior for data sharing even when data sharing would ultimately be beneficial to the enterprise, the mission, and the patients. This is a general issue at the heart of making progress in healthcare regarding data sharing and interoperability. I have some new thoughts on how to bridge this divide.
Recently I read the book ‘Thinking, Fast and Slow’ by the Nobel Prize winning economist Daniel Kahneman. This book discusses the concept of Prospect Theory. In reading through it I could see a hint of why our industry has so much trouble trying to share medical records and in general has trouble sharing almost anything among trading partners and competitors. If you haven’t read about Prospect Theory, the following tests provide some of the basics into how humans make decisions about risk.
Decision 1: Which do you choose? Get $900 for sure OR 90% chance to get $1,000
Decision 2: Which do you choose? Lose $900 for sure OR 90% chance to lose $1,000″[i]
The common answer to #1 is to take the $900. The common answer to #2 is to take the 90% chance to avoid the loss. As a result, we take risks to avoid danger but avoid risks when we see certain rewards. This behavior is relevant to data sharing and access to PHI and can be instructive on how people will approach risk.
Join me in attacking an endemic problem in health care today by Hacking HIPAA. I am crowdfunding the development of a new legal form to be used on and after September 23, 2013 to allow patients to opt-in to easier health care communications – a Common Notice of Privacy Practices that is patient-focused. (Text me, please! Email me, please! etc.)
Depending on how much support this project garners, we can attack some related problems as well. Contributions at any level are welcome; contributions at the levels designated on the Hacking HIPAA Medstartr page get you a seat at the virtual table, voicing your concerns that need to be met in the CNPP and in follow-on projects.
I’m working on this project with two leading health care open source software developers, Ian Eslick and Fred Trotter. Check out Fred’s video intro to the project on the Medstartr page – you can find Ian and Fred online via the links on the project page, too.
Here’s an excerpt from the crowdfunding project page:
Right now we have the worst of all worlds with regards to patient privacy in healthcare. Patients are frequently subject to sub-standard security and privacy practices AND healthcare innovators are unable to deliver solutions that would be useful to patients because their technical approaches are uncomfortably novel for health care bureaucrats. Patients end up getting poor security and no innovation, the worst of all options. This problem is going to get worse before it gets better, since the new Omnibus HIPAA Rule will make cloud hosting of health care projects untenable very soon.
A couple of weeks ago, President Obama launched a new open data policy (pdf) for the federal government. Declaring that, “…information is a valuable asset that is multiplied when it is shared,” the Administration’s new policy empowers federal agencies to promote an environment in which shareable data are maximally and responsibly accessible. The policy supports broad access to government data in order to promote entrepreneurship, innovation, and scientific discovery.
If the White House needed an example of the power of data sharing, it could point to the Psychiatric Genomics Consortium (PGC). The PGC began in 2007 and now boasts 123,000 samples from people with a diagnosis of schizophrenia, bipolar disorder, ADHD, or autism and 80,000 controls collected by over 300 scientists from 80 institutions in 20 countries. This consortium is the largest collaboration in the history of psychiatry.
More important than the size of this mega-consortium is its success. There are perhaps three million common variants in the human genome. Amidst so much variation, it takes a large sample to find a statistically significant genetic signal associated with disease. Showing a kind of “selfish altruism,” scientists began to realize that by pooling data, combining computing efforts, and sharing ideas, they could detect the signals that had been obscured because of lack of statistical power. In 2011, with 9,000 cases, the PGC was able to identify 5 genetic variants associated with schizophrenia. In 2012, with 14,000 cases, they discovered 22 significant genetic variants. Today, with over 30,000 cases, over 100 genetic variants are significant. None of these alone are likely to be genetic causes for schizophrenia, but they define the architecture of risk and collectively could be useful for identifying the biological pathways that contribute to the illness.
We are seeing a similar culture change in neuroimaging. The Human Connectome Project is scanning 1,200 healthy volunteers with state of the art technology to define variation in the brain’s wiring. The imaging data, cognitive data, and de-identified demographic data on each volunteer are available, along with a workbench of web-based analytical tools, so that qualified researchers can obtain access and interrogate one of the largest imaging data sets anywhere. How exciting to think that a curious scientist with a good question can now explore a treasure trove of human brain imaging data—and possibly uncover an important aspect of brain organization—without ever doing a scan.