Risk prediction is an important part of personalized medicine, but it requires a fairly large sample of individuals on which to base the prediction. Moreover, the individuals used should be fairly similar to the patient (or patients) whose disease risk we want to estimate. One of the most promising ways of doing this is by using data collected by hospitals. This is called a "secondary use" of health data since it is not being used for its original purpose - making a diagnosis about the patient from whom it was collected. There are other potential secondary uses also, including using this data for pharmacogenomic drug development - another important goal of personalized medicine. These secondary uses are not currently possible, and policies must be developed to allow this data sharing in such a way that protects a patient's confidentiality and their right to opt-out of these uses.

Genetic information has the potential to reveal a large amount about a person's risk of developing a disease in the future. Insurance companies are likely to want to use this information to set policy rates and limits or even potentially to deny coverage altogether. Even if insurance companies have no immediate plans to do this, fear of it may prevent people from having this information collected. One policy solution has already been proposed, a legislative initiative called the "Genetic Information Nondiscrimination Act". The US Senate passed this act in both 2003 and 2005, but it was never brought out of subcommittee in the House of Representatives. The bill has been re-proposed in both the House and Senate this year, but its future is still unclear. Legislation of this type is extremely important, and must be undertaken at a national level. Hopefully mounting public pressure will force the House to consider and pass a bill to prevent this discrimination.

As of now, even if the scientific issues I've discussed before were solved, many physicians would not understand how to use this information. Doctors who don't specialize in genetic conditions are exposed to genetics mainly during medical school, and often only at a very basic level. Although many understand the impact of genes on simple Mendelian diseases, they are unfamiliar with the role genetics plays in complex disease and the way that genetic information can assist diagnosing and treating patients. Increased education, both during medical school and through continuing education programs, is the answer to this problem, but fitting that into an already overcrowded curriculum is no small task. As insurance companies, hospital systems, and patients realize the benefits of personalized medicine, though, doctors will catch up out of necessity. Being proactive with policy solutions should make that process both quicker and easier.

Physicians aren't alone in not understanding genetics. Patients have an even more limited knowledge of genetics, with much of their information coming from popular media. And let's be honest, if all a person knows about genetics they learned from watching Heroes or CSI, then making informed decisions about how to use genetics in medical care is going to be difficult. They will have unrealistic expectations about what can be done to correct genetic problems ("Isn't there some sort of gene therapy for this?") and exaggerated fears about what might be done with this information ("I don't want my DNA in some database where the government can get it!"). Bringing expectations in line with reality and talking about the reasonable risks of gathering this information then must be accomplished through public education policies. The exact forms of these campaigns could vary, but drawing on the successes of cancer awareness and safer sex awareness programs should inform the process.

Bringing personalized medicine to the public is a large computational challenge. Databases must be created to store the information, computing power to actually perform risk prediction calculations must be purchased, deployed, and maintained, and access to high-throughput technologies (genome-wide genotyping, transcriptomic profiling, proteomic profiling, and metabolomic profiling) must be increased. What is the best way to accomplish this? Are private doctor's offices ever going to have the resources to build and maintain a cluster of servers? And will their patient population be large enough to use for risk prediction? High-throughput screening for now is limited to research hospitals, but just as most doctor's offices use external labs to perform clinical tests, external companies will likely fill this role for them.

Of course, none of these problems will be solved cheaply. Educating physicians and the public require some cost, but are not terribly onerous, and the government and professional groups are likely to step into these tasks, because they have some expertise in providing this education. The infrastructure, on the other hand, is far more expensive. Some of the cost will likely be covered by private industry as companies spring up to do high-thoughput screening or provide application hosting for doctor's offices, but that cost will then be passed to doctors, insurance companies, and inevitably to patients. Policy solutions must be created to ensure that health care consumers (i.e., patients) don't get stuck with a disproportionate amount of the bill, and that these infrastructure concerns aren't used to hike insurance rates unjustifiably.

So there is not shortage of policy issues that have to be addressed to make personalized medicine a reality, but some solutions have already been proposed. In some cases, the problems are similar to other problems that have been extensively studied. By taking advantage of these, we can make sure that these challenges don't prove to be long-term roadblocks. Next up I'll talk about some of the ethical issues in personalized medicine. I have my very first dissertation committee meeting on Thursday, so I may be a day late, but I'll do my best to get the post finished on time.