Clinical trials are based on a sample of the population. These are small sample sizes consisting of a tiny subset of the population mix, compared to the general public around the world, and focused around where the research center/centers are located. I started that last sentence with relatively small, but decided not to use the word relative because the sample sizes used are nowhere relative, or the composition is nowhere representative of the general public. Clinical trials can also be engineered to reduce the risk of a treatment being rejected. The engineering goes into picking the sample, whether it is people with pre-existing conditions who have not many options and want to see hope, or generally healthy people who are financially motivated. The life cycle of a clinical trial (again based on a tiny, tiny sample size) starts with a pilot group and then on to the larger clinical trial in one or more research centers before it is approved for general release to the public. Once the treatment is approved by an agency (say, the FDA) there is no more oversight of its effectiveness or side effects of the treatment until there is a class action lawsuit. We all know where those class action lawsuits end up and who benefits from them. Now as we step into the IoT era, where all the medical devices are connected and actively monitoring patients receiving treatments, it will provide the ability to collect, aggregate and analyze data from them. You can cross relate the patient’s clinical measurements and electronic medical records to determine effectiveness as well as side effects of already agency approved treatments. You can gain deep knowledge of the physical effects of diseases (and treatments) on actual general population and trend them over a period of time, rather than depend on a handpicked sample of people for a time-boxed clinical study based on their location, availability or financial needs. This is a large amount of data, which creates a big data situation. Using technology that is available now you can analyze this data to gain insights, run models and simulations to determine the results of what-if scenarios and then you actually know how a specific treatment is doing across the general population it is being administered to. Using cognitive computing, one can figure out the effectiveness and side effects of a treatment, whether it be in a particular geographical area, particular gender or race so that the they can be regulated and prescribed to be more effective while avoiding risk. Across the board if an approved treatment is showing negative results the insights provided by the data can be used to withdraw/recall the treatment or issue more appropriate warnings than those issued based on the tiny, sample size before it was approved.
For more information on the clinical trial process: https://en.wikipedia.org/wiki/Clinical_ trial