Population Health Data Overload: 5 Ways to Stay Afloat in the Big Data Sea

Posted on January 26, 2016

We live in an abundance of data. It surrounds us—in structured and unstructured forms and in a variety of places, in warehouses, EHRs and within cloud-based analytics platforms. We’re swimming in data and with the sheer amount of data we come into contact with on a daily basis, you can go from swimming to sinking quickly.

One of the chief complaints about big data is that there is so much available to us that we become overwhelmed and unable to take action on it. Instead of adding value to our interventions, services and other applications it becomes fruitless and flat. In population health, the useful data draught is epidemic, to say the least.

There’s enough data to fill an ocean and yet, even when data is accessible, it’s often not strung together in ways that are useful and meaningful, let alone interoperable with other systems. We’re often hamstrung by our inability to access and parse data together, so we use expensive in-field research projects or we settle for something less because something is better than nothing right?

Unfortunately, there is no magic bullet solution for many of the data issues we’re facing in health care. We’re forced to think strategically and carefully about our data choices to ensure that we’re making the most of all of the data resources available. So, how can you avoid being stranded in the sea of data?

Here we offer five tips for sailing through the big data sea without feeling overwhelmed:

  1. Establish Patient-Centric Outcomes. Make patient-centricity the organizing principle of how you approach data collection. Design goals, interventions and strategies that reflect the needs of your population. Then, go back and find the data you need to uncover how to meet those identified needs.
  2. Use Only the Data You Need. Narrow the scope of needed data further to only those measures or data points that are relevant to the objective at hand. When you focus on the needs of your patient populations, your outcomes will be greater than using a generalized data strategy. Instead of a constant loop of all data structures, you’ll be keyed into to those data sources only relevant to your populations.
  3. Visualize Your Data. It’s common knowledge that visuals are far more tangible than words. Consider using a data visualization service like Tableau that will allow you to see data in ways that are meaningful and useful to your patient-centric goals.
  4. Integrate Syndicated Survey Data. Most of the time, when we think of secondary data, we think of publicly available data that doesn’t reflect the true needs of our population. This leads to a great deal of interesting but often irrelevant-to-the-goal data resources. By integrating syndicated survey data into your strategy, you can tailor the data definitions to ensure you’re capturing insights only relative to your populations. For example, instead of viewing all diabetics in the state of Nevada, you can look for Type II Diabetics within Reno Over Age 35 who are Uninsured. Specificity is your friend with syndicated survey data.
  5. Focus on the Outcome when Analyzing Data. Use the patient-centric outcomes you established when analyzing datasets before making plans, strategies or interventions. Look at all of the data holistically, ensuring all pieces of the puzzle are in place to get a clear picture of the population before taking action.


By following these five tips you’ll be able to stay above the fray and keep patient needs top of mind.

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