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May 23, 2014
Health gets smart with analytics & big data—welcome to the patient era
By: Jeff Elton Ph.D.

We are on the dawn of a new era in the life sciences industry—the patient era. It is one that I am personally very excited about—not just as someone who has worked in the field for more than 20 years, but as a person, husband, son and father.

For the first time, health care players from pharmaceutical companies, physicians health providers, clinics, insurance companies, governments and medical technology companies are coming together to share and analyze patient data to fully understand the patient journey. And armed with this understanding, they can collectively work together to figure out what steps to take and services to provide that will improve patient care and health outcomes in very cost-efficient, effective ways.

This truly is groundbreaking. Historically, each party has had one slice of the patient picture—for instance, their visits to the doctor or prescription history. But, as we all know, illnesses and diseases are treated or cured from the full range of activities, treatments and medicines that an individual engages in. It has often been difficult to determine where things might fall off the treatment course for a patient—or why it never really got on course (think repeat visits to the ER for the same issue).

New collaborations among healthcare stakeholders combined with big data and new analytics are rapidly shaping the way for all parties to come together and crack the code on patient care. No more guessing. We are talking about looking at what patients are actually doing at each step of the care process and being able to identify the gaps in care and barriers to achieving their best health outcome. This means being able to measure the effectiveness of treatment programs and improving patient access to care—in real time.

The move to a patient outcome or evidence based operating model is already happening with many pharmaceutical companies. We are working with one major pharmaceutical company to aggregate and analyze data from a major clinic and physician group to help improve treatments for diabetes patients where it is often hard for a specific set of the population to stay on their treatment course. But this approach can also yield insights into populations that have not yet been formally diagnosed but are at a high risk for developing the disease.

These are new ways of working. We are creating a “data supply chain” where data moves through the health care ecosystem, is analyzed at different points to predict outcomes and direct actions, to the benefit of all involved parties, but most especially the patients themselves.

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