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How Predictive Analytics Can Help Address the Opioid Crisis

August 14, 2017
by Sanket Shah, Clinical Assistant Professor, University of Illinois at Chicago
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American lives are being cut short by prescription opioid abuse and overdoses. According to the latest data from the Centers for Disease Control and Prevention, more than 183,000 Americans died between 1999 and 2015 as a result of prescription opioid overdoses. Experts struggle on how manage this public health crisis.

Using healthcare data and actionable analytics could play a key role in helping to combat this crisis. Specifically, diagnostic, descriptive, predictive, and prescriptive analytics make it possible to identify individuals who are at risk of becoming opioid dependent. Armed with this information, healthcare providers can be better informed about the risk of possible dependency.

Descriptive Analytics Highlight What’s Happening and Where

Descriptive analytics help provide a snapshot of what’s occurring in society, and provides considerable insight into the scope of the opioid epidemic. According to a 2017 study conducted by the Blue Cross Blue Shield Association and Blue Health Intelligence, the rate of opioid use disorders increases when patients fill high-dosage prescriptions. Other key takeaways from the study include:

  • Higher rates of opioid use are found among women aged 45 and older and among males younger than age 45
  • Greater numbers of women filling opioid prescriptions—across all age groups
  • Highest-risk regions for opioid use include the Southern and Appalachian regions of the U.S.

Diagnostic Analytics Help Pinpoint Causes

A deeper dive into the 2017 study using diagnostic analytics helped pinpoint the causes of prescription opioid use and abuse as well as driving factors behind the epidemic. For instance, potential determinants for opioid dependency include gender, age, whether the patient sought treatment for an acute injury or a chronic condition, and the size of the dosage and duration of the prescription.

Once diagnostic analytics are used more effectively to recognize emerging trends and pinpoint their causes, practitioners will be better informed to predict which individuals are at risk for opioid abuse.

Predictive Analytics Help Anticipate Future Developments

Predictive analytics allows us to leverage data to anticipate what is to come. In addition to using EMR (electronic medical record) data, predictive analytics incorporates providers’ prescribing habits and patients’ social determinants of health. For example, equipped with this information, healthcare providers could anticipate what might happen if they prescribe a high dose of opioids to one patient versus another.

Prescriptive Analytics Generate Actionable Insights

Once predictive analytics have identified at-risk individuals for developing an addiction, we can use prescriptive analytics to offer up actionable insights. Providers can predict what may happen and make changes to their treatment plan. Armed with actionable insights, new treatment models and alerts can be developed to de-emphasize opioid medication use for at-risk individuals.

America’s prescription opioid epidemic continues to be a public health crisis. Using diagnostic, descriptive, predictive, and prescriptive analytics, we have an opportunity to identify at-risk individuals and change the course to help address the epidemic.

We have the data. We need to use it.

Sanket Shah is a Clinical Assistant Professor in the Department of Biomedical and Health Information Sciences at the University of Illinois at Chicago. Shah is also the director of client relationships at Blue Health Intelligence.

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