Machine Learning: Impact on precision medicine

Technology presently has the ability to bring the promise of genomics and precision medicine directly into the clinical work process, while establishing a foundation for trial and research. This enables the business to apply new genomic data models in a sensible way — to deliver the right information to the supplier at the opportune time, while creating "research-ready" data to support a variety of objectives.

This can transform the way we care for a wide range of diseases and conditions – from cancer to hyperlipidemia to diabetes to renal disease to neurodevelopmental disorders. The ultimate goal is to drive better and more accurate diagnoses, treatments and outcomes — while simultaneously making this knowledge available for research and pharmacogenomics.

Machine learning is additionally engaging us to examine patient data at a level never possible. We can now transform data into insights and significant data.

Simply think how a "data lake," where we can store millions of de- distinguished patient data to structure and to analyze data and study issues that are important to health care, could change diabetes mind, for instance.

We now have the power to compare things like blood sugar levels, body mass index, age and other risk factors and analyze treatment outcomes. Then, when clinicians are designing a treatment plan for a single patient, they can look to other similar patients and see which treatments worked well and identify other turning points that result in better, managed care.

This could be applied to the study of other areas of healthcare as well, including the opioid crisis. We can now couple information that is within the EHR with our "data lake" – and combine it with data that is available through public health mechanisms, such as PDMPs.

The goal is to develop algorithms to identify or even predict at-risk patients and look at prescription patterns that most often lead to problems with abuse and overdose. Our research on this is still early, and we are just scratching the surface; it is clear that this is the direction in which we'll see excellent results.

Machine learning brings us an exceptionally exciting arrangement of capabilities today that didn't exist 10 years back. It enables computers to deal with more prominent amounts of work than individuals can attempt and will turn out to be increasingly important in this time of consumerization. It's making what we do better by improving the overall healthcare experience for both patients and providers.