Overcoming healthcare data challenges

In a recent Jive Software blog bost, they review overcoming healthcare data challenges. When healthcare and big data collide, things can get messy.

On the one hand, the sheer volume of data being collected is opening up new paths to discovery and holds out the prospect of a smarter, data-driven future. On the other hand, we're often buried in more information than we know what to do with, and it's not always the information we need most.

The good news is that we're now nearing the point of convergence, where healthcare providers can actually gather and harness the right inputs to make more precise, predictive and productive decisions. The data can be used to build models that not only lead to better patient treatments, but help solve payer and provider problems as well. Imagine using the same set of tools to better understand and enhance everything from therapeutics and clinical trials to health insurance processes and medical billing operations.

The Goal: Better Decision Support
The need for those solutions has never been greater. The healthcare industry is under intense pressure from payers and regulators to streamline processes, improve care management and deliver better outcomes at lower cost. Patient expectations have risen, too, driven by the consumerization of healthcare. No longer content to take whatever's handed them, patients want everything faster, in a way that's convenient, affordable, transparent and tailored to their needs.

Meeting those demands means making faster, better-informed decisions, and that's where big data can make a big difference. In the past, healthcare organizations had a very restricted data set to work with. They had access to patient records through EHR (Electronic Health Records) systems, of course, but lacked broader contextual information, ranging from the clinical interactions that occur during care delivery to the external variables known as the "social determinants of health" (SDH). These are the conditions in which people are born, grow up, work and age. They include a range of environmental factors and large-scale social forces such as economic policies, development agendas, social norms and political systems.

Health systems need decision support solutions that factor in these broader inputs, helping clinicians to create personalized health plans based not just on a patient's immediate condition, but also their history and that of their family, the impacts of social determinants, and data from people with similar conditions and backgrounds. Combining all of that information with the interoperability of EHR systems across different networks, and we're looking at a new era when broad data sets can be used to help drive healthier outcomes for patients and families.

Several of the pieces are already in place. Every day, systems collect thousands of pieces of health data about patients. New technologies such as telehealth, wearables, bluetooth devices and remote sensors are delivering additional statistics on blood pressure, physical activity, blood glucose levels, weight and more. And then there's all the data within the health system and payer networks – data on how treatments are prescribed, how medications are used, how patients respond, how billing processes work... the list goes on. This information is then stored away, waiting to be extracted, transformed and analyzed.

At the same time, new analytics technologies have emerged to help us make sense of the data. It's not just headline-grabbing breakthroughs like IBM Watson; all sorts of less glamorous but no less important machine learning, micro-services and AI innovations are quietly making their way into many of the tools we use, providing new insights and more precise results.

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