Data analytics have taken a larger role in medicine in the past two decades, and no area has seen more innovation than precision medicine.
Rather than a “one size fits all” approach for treatment for patients, precision medicine uses data to tailor medical treatment specifically to individual patients. Using healthcare analytics, medical professionals develop treatment plans that take into account a patient’s genetic makeup, environment and lifestyle.
In short, it creates a personalized approach to giving people the right care at the right time.
Government agencies already have taken steps to clear the way for more use of precision medicine. Under the 21st Century Cures Act of 2016, the Food and Drug Administration is developing regulatory approaches for oversight of genomic technologies, which seek ways to find genetics-based causes of disease and develop more effective ways of treating them.
That act came as a result of President Barack Obama’s 2015 launch of the Precision Medicine Initiative.
The Role of Big Data In Precision Medicine
Medical professionals now have access to a greater wealth of data on patients. That allows them to research various characteristics and determine the types of treatment that work best for specific demographics of the population.
To put it simply, you don’t necessarily order the same treatment for a 50-year-old overweight, lifelong smoker as you would a person of the same age with an active, healthy lifestyle. Other variables, in addition to age and lifestyle, would include where the patient lives, family history and genetic makeup.
A wide range of data sources have added to the advancement of precision medicine.
Electronic health records, mandated by the Obama administration, are the foremost tool used in precision medicine. These records create large databases of patient records, allowing for physicians and researchers to tap into extensive, detailed records on both conditions and treatments.
Wearable technology that uses the internet of things (IoT) also has led to advances, as continuous information on a patient’s health and even level of activity can be recorded, helping medical professionals fine tune the course of treatment.
Challenges remain, particularly around linking the various data collection platforms to give health workers the best information in the fastest time possible. But steps have already been taken, and the future for precision medicine looks bright, especially in cancer research.
Where Precision Medicine Shows Promise
The Precision Medicine Initiative launched during the Obama Administration came with more than just a promise. The federal government backed the initiative with cash – $215 million overall, with $130 million going to the National Institutes of Health (NIH), according to Healthcare IT news.
The NIH is using the money to fund research, using data collection and sharing, on the potential use of precision medicine on a variety of healthcare issues. They include cancer treatment, Alzheimer’s Disease, diabetes, heart disease, obesity and mental health.
Aiding the effort of using data analytics for precision medicine, the American Association For Cancer Research (AACR) last year released data on 19,000 cancer cases for use by international researchers. The cases include 59 different types of cancer, including breast, lung and colorectal cancer.
Five cancer research centers in the United States participated in the release, as well as three located in France, Canada and The Netherlands.
Margaret Foti, CEO of the AACR, said the data sharing encouraged by the Precision Medicine Initiative will aid in making leaps forward in cancer research. Such collaborative efforts hold “the promise for significantly enhancing the future utility of precision medicine in the treatment of cancer and for the benefit of patients around the world.”
The data-sharing shows the effect of the Precision Medicine Initiative, as well as the potential for this data-driven approach to healthcare to vastly improve patient outcomes.