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Five Big Data solutions to manage chronic diseases

 

For years, retailers, manufacturers, tech companies, and other industries have been turning Big Data into better insights. Sophisticated data analytics platforms, reporting tools, and optimization applications mine valuable insights every day from large data sources.

Those insights are rarely available in healthcare particularly to help with chronic diseases, where the CDC stats show we spend 86 percent of our healthcare dollars.

Even after the widespread adoption of various electronic health record systems, most providers can’t get EHRs to interoperate or exchange significant health information. The ability to gather enough quality data for effective analytics hampers the potential to improve healthcare productivity and chronic care outcomes.

For consumers, things aren’t much different. Sure, you can track workouts or count calories, but are there any great examples of putting Big Data to personal use? Healthcare has not been like personal finance or social media, where you can compare how you spend or what people like for greater insights or smarter actions. Healthcare’s landscape is especially challenging – IT adoption in healthcare is all over the map. EHRs are designed to silo data, and despite meaningful use’s attempts, that does not seem to be changing.

While businesses and consumers may have access to copious amounts of data, what’s lacking is the ability to act. Big Data is most powerful when it generates insightful action, increases efficiency, and in the case of healthcare, improves lives.

Let’s turn healthcare Big Data into an actionable asset rather than a siloed bureaucratic nightmare. Big Data opens up real opportunities for solving big healthcare problems at both the population and personal level.

When someone is diagnosed with diabetes, they are asked to check their blood sugar at least once a day. This simple act creates hundreds of data points per year, per person. This data has the potential to provide some of the richest insights about specific changes to behavior and therapies that can improve quality of life and overall health outcomes.

Now, consider that diabetes affects more than 29 million people in the U.S. and more than 420 million globally, the number of data points explodes exponentially. If these data points are automatically and intelligently organized, terabytes of information would be available to help with chronic care research, diabetes treatments, and even new methods of online care.

Here are five ways Big Data solutions can close the gaps in managing chronic care conditions.

Finding out what’s working

Every person is unique, but our bodies operate on common principles. Big Data can help physicians and patients discover — with far more precision — what treatments work best given multiple medical, biological, environmental, and even socio-economic criteria. Which foods keep sedentary patients in cold climates at their target weight? Which exercises help with both blood sugar and arthritis? Which treatment methods stabilize patients who also travel frequently?

Summarizing, and presenting results automatically

Savvy algorithms can rapidly analyze patient glucose data, for example, to determine a patient’s best day. Combining results from Big Data sets can go further, providing the content and context to both summarize and present information in terms best suited for individual physicians and patients. Rather than spend precious time on data analysis, people can focus on making the most of what they’ve learned.

Enabling decision support

Data drives the creation of valuable decision support tools. Answers to questions like when to change settings on a medical device, if a medication dose should be changed, or if a patient needs moral support can all be garnered from machine learning algorithms that use past behaviors and personal data to recommend decisions. Doctors are starting to access algorithms results and use them during appointments more frequently.

Predicting when changes are coming

Is a person’s glycemic control, blood pressure, or weight trending in the wrong direction? Is a person likely to decline in health? Big Data forms the basis for predictive algorithms that can give patients and their doctors early warning about real issues that may be on the horizon.

Knowing where to ask more questions

Often, data analysis uncovers more questions. In diabetes, for example, knowing a person’s glucose level is only step one. This data uncovers questions about diet and exercise habits, stress, or other health problems. With new, patient-generated health data (PGHD) flowing in from wearables and smartphone, gathering the data required to answer these questions is closer than it has ever been before.

Managing chronic diseases is the fastest growing and most expensive problem facing healthcare today. Big Data analytics has the power to help payers, physicians, nurses, coaches and most importantly people with diabetes – we all need to, rise to the challenge.

This article was originally published on medcitynews.com and can be viewed in full

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