With all of the unstructured data in medical journals, doctors' notes, radiology images, and faxes, health IT vendors have been developing technology to help the healthcare industry make sense of all the loose data. And it is important to remember that big data is more than just a sea of information; it is an opportunity to find insights in new and emerging areas of healthcare.

Enter precision medicine, an emerging field that aims to harness the wealth of data available from the human genome and research into the molecular basis of disease, and integrate it on both a personal and global level with information on environmental factors and patients’ electronic health records (EHRs).

The practice of precision medicine would allow scientists to share emerging research findings faster, drug companies to develop more precise therapies, and clinicians and patients to make more informed decisions about treatments that would ultimately improve care, save lives, and reduce healthcare costs.

Recently, I had the chance to speak with Michael Blum, M.D., who has been tapped to lead the new Center for Digital Health Innovation (CDHI) at the University of California San Francisco (UCSF). Blum told me he thinks the key to advancing precision medicine is to leverage our digital assets to build the knowledge network and information commons that are the foundations of precision medicine. The knowledge networks not only link sources together, but then bring this knowledge to providers at the time they’re providing care, he said.

To help me better understand precision medicine, Blum gave an example of having a patient in front of him who has heart problems. “Now, I can go to different risk calculators, and say, ‘Based on your family history and risk factors, you have a five percent chance of having a heart attack in the next 10 years.’ Although that is better than nothing and sometimes it might motivate a patient to change behavior, it’s ultimately not enough. What I really need to be able to tell them, based on what we know, based on the censored data, the biomarker data, and the 100,000 patients similar to you—genetically and life wise—your risk is actually 50 percent, or your risk is actually a quarter of a percent. We need to make those strong predictions, and prescribe the right drugs that they’ll respond to. That’s the real move to precision medicine.”

Another example of how one can look at this came to me from M. Wayne Craige, an HCI blogger, who wrote: “You woke up feeling sick, and you know you better see your doctor. In the office, your doctor looks you over, listens to your symptoms, but before he prescribes a drug, the doctor said, ‘Let’s get a sample of your blood so that I can take a look at your Deoxyribonucleic Acid’. You heard me right, your DNA!” Today, doctors usually give you an "average" dose of a medicine based on your body size and age. In 10 years from now, we are expecting that genetics—the study of how genes influence actions, appearance, and health—will pervade today’s medical treatment, Craige said.

Certainly, we are getting there—sometime soon, the hope is that future medicines will match the chemical needs of your body, as influenced by your genes. And healthcare organizations other than UCSF are working towards this goal as well. For one, the Mount Sinai Medical Center, the New York City-based 1,171 bed tertiary- and quaternary-care teaching facility, recently announced it's creating a new data management and analysis platform that will be able to communicate with the organization’s EHR. The platform, Mount Sinai says, will be able to give doctors real-time therapeutic and diagnostic guidance based on the patient’s genetic profile.

Mount Sinai says it will pilot-test the platform through a research program called CLIPMERGE, which stands for Clinical Implementation of Personalized Medicine through Electronic Health Records and Genomics. Mount Sinai is recruiting 1,500 patients from a pool of more than 25,000 candidates that are enrolled in its BioMe Biobank of patient DNA. Once a patient consents to participate, the hospital collects a saliva sample, extracts DNA, and analyzes it for genetic variants that may affect the patient’s response to treatments.

Additionally, Oregon Health & Science University (OHSU), a research university in Portland, Ore., has announced a new partnership with Intel to develop computing technologies that will aim to advance the field of personalized medicine. The technology will look to increase “the speed, precision, and cost-effectiveness” of analyzing a patient’s genetic profile, as engineers and scientists from both institutions will work together on the multi-year project.

Kaiser Permanente has also initiated a research project matching saliva DNA samples from Northern California residents with their EHR database, to find new ways to identify people at risk before they develop problems like heart disease, cancer, and diabetes.  Over 100,000 members of Kaiser’s health plan sent in saliva samples which will be correlated with EHR information already in Kaiser’s database, according to NPR.

Another endeavor into linking clinical and genomic data was made by the University of Pittsburgh Medical Center (UPMC), when researchers integrated clinical and genomic information on cancer patients. This technological advancement comes eight months after UPMC rolled out a five-year, $100 million enterprise healthcare analytics initiative to foster personalized medicine and other strategic healthcare IT goals. UPMC says the first part of that initiative, laying the foundational architecture of the data warehouse, was completed and allowed for this integration to occur.

At UPMC, Pitt researchers were able to electronically integrate for the first time clinical and genomic information on 140 patients previously treated for breast cancer. In this case, the researchers found intriguing molecular differences in the makeup of pre-menopausal vs. post-menopausal breast cancer. While understanding those differences will require more research, the findings eventually could provide a roadmap for developing targeted therapies, UPMC officials said.

And last month, when HCI Editor-in-Chief interviewed David Artz, M.D., CMIO at Memorial Sloan-Kettering Cancer Center in Manhattan, Artz said that personalized medicine has really affected cancer care. You can think of cancer as a genetic disease, not necessarily an inherited genetic disease, but one that moves forward at the cellular level, within the DNA and RNA of the cell, he said, adding that Memorial Sloan-Kettering will need to create ways to incorporate the institution's new data into the medical record.

While the above efforts made should seem promising, not everyone has a sunny and rosy outlook when it comes to personalized medicine and EHRs. A recent article from the Journal of the American Medical Association (JAMA) has a more negative point of view on the ability to incorporate genomic information into EHRs and clinical care. EHRs are designed to display only clinically-relevant information, the authors noted. That's why radiological images are typically stored in picture archiving and communication systems (PACS), and only the radiology report is sent to the EHR. Further, EHRs do not include the analytics needed to interpret genetic variations in light of the latest scientific research, they said.

But here’s hoping that the aforementioned initiatives will inspire other organizations to exert similar efforts.  While we can acknowledge that we’re only in the beginning stages of getting EHR systems to work with genomic data, the work that already has been done and will continue to take place will likely get us there before too long. Because together, progress in research, clinical care, and policy enabling personalized medicine has great potential to improve the quality of patient care and help contain healthcare costs—and that is the ultimate goal.