BLOG on IT/IS in Healthcare & Life Sciences Joerg Schwarz on Healthcare [Health + Care]

Translational research is about using insight from science to improve delivery of health care - it is the link between scientific research and medical practice. Fact is that it usually takes a long time from science in to medical practice, and we know we need to improve this in order to cure big diseases like cancer and Aids.

Yesterday at the Minnesota Health Technology Institute  in Minneapolis, Jeff David from HIMSS talked about a vision of integrating the information flow between payers, providers and life sciences, in the attempt to deliver the best, safest and most effective treatment of care. Everything is about personalization.

Payers, which have probably the most complete record of diagnosis and medication history based on their claims data, could enable consumer directed health care if they acted as "intfomediary", using their information for managed care and decision support. HIPAA  allows and requires payers to transfer such data should a subscriber change health plans (which is why the "p" in HIPAA stands for portability), so it would be a reliable, longitudinal data source. Especially payers would be motivated to discriminate members or potential members based on genotype profiles.

Provider data from hospitals usually covers only individual episodes of care, yet those with more detail than payers would never be able to triangulate from claims data. David's suggestion is therefore to combine payer data with its breadth and provider data with its depth for a comprehensive, longitudinal view of a person. The third leg of this stool comes from life sciences.

Life Sciences could contribute by delivering consumer centered, personalized care through "smart drugs, smart devices, smart discovery and smart delivery". Ever since we began to understand the genome and learned that certain genotypes react differently to the same drugs, researchers dream about drugs targeting a specific genotype groups with very high efficiency. It would be natural to assume how this can help in medical practice, but it is also clear why we need genetic privacy to get broad based support for this practice. Today, we'd be happy to know which medication and medical history a patient has. In the future, it should become practice to conduct genotype analysis that probe for certain predisposition markers, in order to develop optimal treatment plans. We have heard about gene markers like BRCA and how they help to determine high risk breast- or ovarian cancer patients before the cancer actually develops. This led for example to the practice of prophylactic mastectomy saving the lives of thousands of potential cancer victims.

Bart de Witt mentioned in his comment to my google entry a company called 23andme*, a perfect example for personalized genomics. And one day, genomic profiling will probably be as normal as a regular blood screen. It should be. Not only for prophylactic procedures like in the case of BRCA, but also to determine optimal drug treatments for a certain genotype.

In drug development, genetic profiling needs to become practice in order to determine if drug targets react differently based on polymorphisms. Once we know that say Aspirin works better with polymorphism A, and Tylenol better with polymorphism B, for example, we could start giving only Aspirin to carriers of A and hence be much more effective in our entire medical practice.

There are, however, enormous consequences for the life sciences industry and the drug development process. In part 2 of this blog entry, I will discuss a data mining architecture we are designing together with Indigo Biosystems. Indigo developed a data mining tool for scientific data, called Rubicon. We believe Rubicon will revolutionize life sciences research, drug development, and medical practice, because it will allow storing, retrieving and using massive amounts of raw instrumentation data. Why this is necessary for smart development and delivery of drugs is also the topic of part 2 of this article.

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*my guess is that 23 eludes to the fact that humans have 22 gene pairs of autosomes plus one pair of allosomes, which determine gender. So, 23andMe is about personal human genome profiling. As Bart pointed out, it's kind of cool that Sergey Brin's wife runs that company, while Sergey runs google and hence google health. If I ever needed an excuse to write in a healthcare blog about life sciences, here it is. Maybe I should run a gene analysis with 23andME and find out if I can store it in google health?