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Intro to Data: Using Big Data in the Public Health Sector and the Military Health System

DHCC graphic - (Click to Enlarge)
By Zachary Peters, MPH, Program Analyst
June 1, 2017

Welcome to the second blog in our “Intro to Data” series where we’ll talk about how big data can be used in the health and public health sectors, specifically within the Military Health System (MHS). In our first blog, Dr. Waitsman introduced the 3 Vs that often describe big data: volume, velocity and variety. As technology continually develops, organizations build new ways to capture the increasing volume and variety of data that’s available with increasing velocity.

With larger and more diverse data sources, it’s no surprise that new and innovative ways of using big data emerge daily. For example, in the public health sector, numerous organizations and institutions are analyzing key words in internet searches to help identify disease outbreaks. By tracking the number of internet searches for a symptom or a treatment in a given area, public health agencies can better and more quickly direct funding and resources to cities, states or countries in the early stages of an outbreak.

Health care systems are also using big data in important ways at the patient and population levels. If you aren’t wearing one yourself, it won’t take long to spot someone wearing a fitness tracker of some kind. Tech companies are lining up to collaborate with insurance and electronic medical record companies to integrate health and fitness app data into individual health records, which can be used to help patients and providers monitor everything from medication adherence to fitness goals to blood pressure.

But how does that relate to psychological health in the MHS? When you enter clinical information into electronic medical records (whether it’s AHLTA or the forthcoming GENESIS), you are able to review that information every time you see a patient. But most of that information also gets stored as variables in datasets that are then cleaned up and managed by a team of data scientists who make it available to analysts and researchers. Using this data, analytic teams, like our team at the Deployment Health Clinical Center (DHCC), are able to track important trends in disease burden, patient care, prescription patterns and more for all TRICARE enrollees. You can subscribe to receive DHCC “Psychological Health by the Numbers” reports to your email.

These datasets contain variables that tell us important information about the provider, the patient and the facility, which we can use to focus in on a specific population of interest. For example, we’re able to look at the prevalence of specific mental health conditions among active-duty service members (as opposed to the entire population) or assess follow-up rates among only those patients who were hospitalized for a mental health condition. When we focus on areas where providers and clinic leaders may be able to have an impact (e.g. individuals who are particularly vulnerable in the days after a hospitalization or only on active-duty service members who receive most of their care in military treatment facilities), big data becomes much more manageable, useful and informative for decision makers.

The uses of big data are broad and endless, but by honing our questions to our populations of interest, we can better harness its power. There is much more to discuss on this topic, so stay tuned for the next installments in our “Intro to Data” series. If you have specific topics that you’d like to hear more about, please share in the Comments section below; your comments can help to shape the direction of future posts!

Zachary Peters is a contracted program analyst and data quality specialist at the Deployment Health Clinical Center. He has bachelor’s degrees in zoology and physiology and a master of public health in sociomedical sciences.

The views expressed in Clinician's Corner blogs are solely those of the author and do not necessarily reflect the opinion of the Psychological Health Center of Excellence or Department of Defense.

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