CyberHealth: Computerizing Personalized Comparisons of Treatment Effectiveness
[Via Jotwell: Cyberlaw]
Sharona Hoffman & Andy Podgurski, Improving Health Care Outcomes through Personalized Comparisons of Treatment Effectiveness Based on Electronic Health Records, 39 J.L. Med. & Ethics 425 (2011).Economists are beginning to lose faith in technological progress. As one wag puts it: instead of cancer cures, “Captain Kirk & the USS Enterprise, we got the Priceline Negotiator and a cheap flight to Cabo.” Even formidable companies like Google have fled the health field, daunted by the complex legal environment. Some have called for radical deregulation as a solution. But a more viable approach is to turn to the work of some of the smart, committed, and impartial legal scholars who are pioneering the field of cyberhealth law. Particularly instructive is Sharona Hoffman & Andy Podgurski’s article, Improving Health Care Outcomes through Personalized Comparisons of Treatment Effectiveness Based on Electronic Health Records.
In an information economy, even cheesecake can be optimized using data-driven methodology. Unfortunately, leading health care providers often resist such methods of improvement. Pharmaceutical firms have sometimes continued to market drugs even after reports emerge that undermine the rationale for taking the drug, let alone paying for it.That troubling method of attaining short term profits at the cost of long term sustainable business models needs to be countered by sophisticated methods of analyzing (and disseminating) data on the real effect of medical interventions. Hoffman and Podgurski help develop a legal and technical framework for assuring that happens.
[More]
More and more information is coming out detailing how ineffective many clinical trials are now becoming. Ben Goldacre has a very interesting piece at Salon about what is going on with Big-Pharma funded clinical trials.
In particular, since we really only see successful clinical trials published for us all to see, there is a huge systematic selection bias. We never get to see many of the unsuccessful studies because they never see the light of day. Thus the drugs look effective because we only see the data that demonstrates that.
Data showing a drug does not work often never gets published.
Goldacre describes one example where, when ALL the studies – published and unpublished – were examined, only 1 study – of about 250 people – showed that the drug was effective. Six other studies – looking at almost 10 times as many people found no effectiveness over a placebo. These studies were never published.
So, the literature would say that this drug was effective. Yet the totality of the data show it is not. In fact, the unpublished data also showed more side effects and showed that the drug was not as effective as others.
This is the hidden problem of our clinical trials. So how do we solve it?
This article suggests using the upcoming era of Big Data to help. Essentially every person will be generating a huge amount of genetic, dietary and life style information for their health professionals.
All this can be collected to create something like a ‘virtual’ clinical trial. A patient can be compared to a known cohort of people with similar data patterns in order to see how a drug might work with them.
This still focusses on the medical community communicating between themselves and the patients. It leverages what is already there in novel ways due to the amount of data.
But I think it might go even further.There will be even more data because we are entering a period where the individual will be able to have access to huge amounts of personal dat, all looking at health factors. Not only weight or sugar levels and such but also tremendous metabolic data from hundreds of pathways.
Everyone will know what their own system looks like when healthy. And we will know if a drug treatment restores the system to health. This ‘demand’ side use of the data, rather than the ‘supply’ side we currently have will alter medicine tremendously.
People will participate much more, seeing directly the effects of specific treatments or, more importantly, the effects of lifestyle change. on their health. They will demand treatments that work for them and will know very rapidly if those treatments do not work.
Because they will see the data.
