Aggregate Drug Effectiveness Data

CURRENT PROCESS
• FDA approves drugs after clinical trials (2000 to 5000 cases over 8-10 years)
• Doctors prescribe drugs based on FDA clinical trial results
• Patients take drugs / Results measured and noted in medical records (Millions of cases over an increased timeline)

IDEA
Compile prescription medication effectiveness data, from patient medical records, based on generic demographic information (age, sex, and race), in order to:
• Assist in the decision making process associated with prescribing medication
• Lower costs associated with prescribing less effective medications
• Increase consumerism via information proliferation

PRACTICAL EXAMPLE
Prozac went through clinical trials and the results from 5,000, or so, patients were monitored and tracked, in some manner, over an 8-10 year period. Once Prozac made it through clinical trials with the go ahead from the FDA, 4.5 million Americans were prescribed the drug within five years.

5,000 is .1% (one tenth of one percent) of 4.5 million, and that percentage decreases every year that new prescriptions get filled. So, 4.5 million people used Prozac over the course of five years and experienced varying results that were all logged in their medical records. However, physicians continued to prescribe the drug based on the results from .1% (and shrinking) of that total (and growing) population.

Now let’s say I am a Physician / Healthcare Consumer / Insurance Company, and I want to know which drug is the most effective, given age, sex, and race, between the following Selective Serotonin Reuptake Inhibitors:
• Prozac
• Zoloft
• Lexapro
• Luvox
• Paxil
• Celexa
• Pexeva

That data currently exists in the millions of medical records of patients who have been prescribed these medications. Imagine the increase in prescription efficiency if that data was consolidated and accessable.

SOURCES
FDA Process Link
http://www.medscape.com/viewarticle/405869_4
Prozac Link
http://www.answers.com/topic/prozac?cat=health