The Data Revolution: Networks, Platforms, and the Data Sciences in Health
The digital health (r)evolution may ultimately succeed or fail based on how we use data to change our behaviors, policies, and organizations over the coming years. In order to see why this is so we will examine some of the health data challenges that our
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The Data Revolution: Networks, Platforms, and the Data Sciences in Health Jody Ranck
Why Health care Is and Isn’t a Data-Driven Science? The digital health (r)evolution may ultimately succeed or fail based on how we use data to change our behaviors, policies and organizations over the coming years. In order to see why this is so we will examine some of the health data challenges that our system has been struggling with over the past decade or so and why these problems will only grow unless the healthcare system becomes a more data-driven system as a whole. Medicine at the bedside is an empirical, data-driven science but also an art based on a subjective evaluation of the patient’s social context, behavior and psychology. While the evidence base for medicine is based on the averages of large studies or trials, physicians treat the n of 1, meaning an individual. One’s doctor is reading your symptoms as a number of data points based on your vital signs, laboratory diagnostics, and so on but also must evaluate these numbers in light of your own particular historical medical record and context. The challenge is that we find dramatic differences in how medicine is practiced and unfortunately there are large variations in how physicians treat a wide number of diseases even when there is an evidence base to guide and standardize treatments. When we hear that artificial intelligence and big data will replace doctors someday, we need to pause for a reality check in light of these aspects above. In this chapter we will look at the research at Dartmouth Medical School that has documented treatment variations across the country over the years and explore the tension that occurs between globalized norms and the art of medicine at the bedside. On the surface these may appear to be at odds, but there are technological and
J. Ranck (*) Health Bank, Ranck Consulting, Ram Group, Washington DC, USA e-mail: [email protected] © Springer International Publishing Switzerland 2016 J. Ranck (ed.), Disruptive Cooperation in Digital Health, DOI 10.1007/978-3-319-40980-1_6
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scientific developments that may help us to remedy some of the tension as the data sciences help to create a more personalized form of medicine while also making healthier populations in the coming years. This tension between personalization of therapy at the genetic level and the need to address public health issues is a very important one. This is where the data you collect on your mobile phone can be married with large data sets to help customize your treatment while simultaneously contributing to research that can help us find better treatments, risk models, and public health interventions customized for the type of neighborhood where one lives. From a broader health system perspective we can use data analytics to help address inefficiencies. Our healthcare system is notoriously inefficient as the Institute of Medicine and many other studies have shown. Population Health Management has become the mantra as value-based care begins to grow in
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