A week or two ago, I got to correspond with Danielle Brooks of Disruptive Women in Health Care about the work I do here at OReilly. The following interview is reprinted here with their kind permission.

I have mostly worked as a book editor, until just a year or two ago. I was working on books about databases, machine learning, visualization, and other relevant topics when OReilly launched its Strata conference on data science, and so I became involved in that conference. But as Strata took off, it became apparent to us that certain communities and certain types of data were special. Health care is one of those areas: the insights that data analysis can give us about ourselves and the things that ail us are enormous, but the risks of over-sharing and the resulting constraints such as HIPAA also present very real challenges.

In 2012, OReilly decided to launch a new edition of its data science conference to focus on health care, and thats how Strata Rx was born. I was asked to become its Program Chair, along with Colin Hill, CEO of GNS Health care, and so I have spent that last 18 months learning everything I can about the (very complicated!) health care industry. Colin and I are great partners because of the complimentary backgrounds we bring together Colin from the health care industry side and myself from the technology side. Ultimately, thats what Strata Rx aims to do, too: we hope that by bringing together professionals from all parts of the industry (payers, providers, researchers, analysts, advocates, developers, investors, and caregivers, just to name a few) we can begin to solve some of the large and complex problems facing us in this area.

As an Editor and Program Chair, I work primarily in the business of sharing knowledge and ideas, as well as the context for those ideas. Health care faces a number of significant challenges, from the staggering costs (about $2.6 trillion every year in the United States) to the widespread occurrence of chronic conditions such as heart disease and diabetes to the highly variable responses of different individuals to a given treatment. Im interested in helping to connect people with a deep knowledge of things like metrics, statistics, and interaction design to others with a deep knowledge of genomics, epidemiology, drug development, and patient advocacy. Data science and analytics are already making a huge difference in other fields (such as marketing, finance, and retail, just to name a few), and health care is similarly ripe for innovation and advancement.

Its not really possible to speak of the industry in monolithic terms. Just as in any discipline, there are some people doing cutting-edge work, and many others lagging behind. But there are some great examples of where progress is made. Some researchers and companies are using data and analytics to create targeted therapies for specific gene mutations. Some patient communities are sharing their own information to help each other out and identify patterns. The Quantified Self movement uses wearable devices to monitor and change their own behaviors. Doctors and hospitals are using electronic medical records to centralize information and reduce errors, and programs like the VAs Blue Button initiative and online patient portals are helping give access to those electronic records back to patients themselves.

The real advantages will come as these innovations start to cross boundaries between groups of professionals. When you can share the information from your wearable device with your doctor, who can upload that into an electronic record that works with the systems your specialists are using, and they can compare that data against the things your genome suggests you might be at elevated risk for and consider the interventions that are most likely to work for you as an individual then well really be onto something.

Despite the privacy and sharing constraints of legislation like HIPAA, it seems to me that some of the most serious challenges preventing health care professionals from making more use of data and analytics are cultural.

On the patient side, there is a generational divide between people who are used to sharing lots of personal information and people who have been trained to keep everything to themselves. On the provider side, there is an ingrained way of thinking about how to make good decisions (with an over-reliance on gut instinct and subjective experience). On the research side, the practice of publishing only successful studies some with dubious definitions of success means that failed research is never shared, and we lose a lot of available context for the studies that are published, misleading us all about the significance of various findings. In the entire system, incentives are misaligned so that the care and health of the patient isnt actually the primary concern.

The biggest difference that data and analytics can make in health care is increasing the level of granularity at which we can understand ourselves and make decisions. For example, right now most people have their blood pressure and heart rate measured once a year and thats only if they actually show up to an annual physical. Wearable devices can now measure and report those statistics multiple times per day. Thats a huge difference in how much information we can use to paint a detailed picture of our health. Another example would be genome sequencing, which is becoming faster and cheaper all the time. It can potentially tell us as individuals which conditions we may be at risk for, and which treatments were likely to respond to, and allow providers to target interventions more precisely (known asprecision/personalized medicine).

Another significant opportunity I see is to help us measure the interventions and processes that work, so we can standardize best practices. Right now, health care providers mostly rely on a combination of gut feeling and subjective experience. But by carefully tracking and assessing a much broader experience base, we can develop checklists (like the ones that already exist for airline pilots and other professionals who hold lives in their hands). These checklists and standards are already being developed around goals such as preventing the spread of sepsis in hospitals, but arent widely adhered to yet, and could be useful for so many other health care goals.

See original here:

Big Data and Health Care