Genetic testing has been available for decades, in one form or another. But genetic testing was expensive (thousands of dollars per patient), time-consuming (results often took months to return), and limited in scope (usually 1 or 2 genes at a time). As a result, it was used rarely, and primarily for people with suspected rare hereditary conditions.
Over the past decade, all of that has changed. Testing technology has improved, human gene patents were banned, and competition has increased — all leading to much less expensive genetic testing. For reference, the cost of whole genome sequencing was a whopping $ 2.7 BILLION in 2001. Today several reputable laboratories offer this service for less than $ 1000, and the price will drop further. Turn-around-time is now measured in weeks or days, and testing is available for more common conditions that impact a greater segment of the population.
Consumers can now order their own genetic testing, without the help (or hindrance) of the health care provider. This process is both empowering for the consumer and littered with caveats: consumers can buy a test online for less than $ 200, have it shipped directly to their homes, spit in a tube, send it back, and just a few weeks later receive information about their health risks. Depending on the company and the testing, the quality of the information returned varies from highly accurate to complete rubbish. But millions of people are choosing to have direct-to-consumer genetic testing, with estimates that 100 million consumers will have done so by 2021. This represents about 1 in 3 Americans.
It is critical to realize that not every genetic test is created equally. The scope and quality of the genetic testing data differ drastically between companies and between tests. But there is no doubt that mountains of baseline genetic data now exist.
How can we use these data wisely, to their full potential, to help more people? In my quest to answer this question, I’ve begun to speak to people who are taking unique approaches to smart use of health data. First up was Carla Balch, CEO at Inteliquet, about how they’ve reimagined the clinical trial process using health and genetic data. Inteliquet, formerly known as TransMed Systems, describes itself as a provider of technology and services for clinical research and translational medicine. But in simpler terms, I learned that Inteliquet pulls in data from their partners’ (hospitals, oncology practices, academic medical centers) electronic medical records so that it can be used effectively. They organize, digitize, and constantly mine those data to match individual patients to clinical trials for which they qualify now, or may in the near future. They also examine the overall data for a partner and advise them, based on their own patient population data, which trials they should open to best serve their patients. Inteliquet can then bring those trials to that partner and open them quickly.
Bending the workflow around clinical trials – which to open, how to effectively recruit – is just one of the ways we need to think smarter in using genetic data to better serve the individual consumer. As pharmacogenetic data get better and better, we will need to integrate that information into current workflows for medication ordering and dispensing so that you can be prescribed the medication most likely to work for you, and least likely to result in an adverse side effect, the first time around. Recent statements from professional genetics societies also underline that return of results and information to consumers should be on-going, as our information improves. The next challenges down the road will all revolve around translating the mountains of data into meaningful action that consumers and their providers can use effectively to improve their health care. Stay tuned.