I want to tell you about Andy. Andy’s mom, Pam, is a colleague of mine. Growing up an only child, Andy was a happy kid. He was a straight-A student, loved to play the violin, and spent a year as an exchange student in Europe. Andy had two loving parents. But Andy suffered an injury in college, and needed to have some minor surgery performed to repair his sinuses. Following that surgery, his doctor prescribed opioid pain medication for him, to which he became addicted. Despite several years of effort, Andy was unable to shake the addiction, and tragically lost his life to a heroin overdose two years after his surgery. This was a normal kid with a normal family, like mine, and like yours.
Andy’s story is an important story. The opioid epidemic has led to the deadliest drug overdose crisis in the history of the United States, killing more than 64,000 people in 2016 alone – the last year numbers were available. This is a true national epidemic, and one that continues to get worse. For the first time in nearly 60 years, life expectancy for Americans has dropped for two years in a row due to the opioid epidemic.
The opioid crisis has been so difficult to curtail, in part, because of the inability to integrate data from various stakeholders and systems. With so many players and data sources, today’s information is partial, fragmented, and often not actionable.
While this disconnect applies directly to the opioid epidemic it is a systematic problem that affects the healthcare community at large. Better data and analytics can help develop better treatment protocols for a wide array of medical and public health challenges that affect the general public. For opioids, that could be to develop better pain management programs or for better, more-targeted remediation and rehabilitation for those that become dependent on drugs.
A Data-Driven Healthcare Approach: Making Information Real
Ample data has been collected on the opioid epidemic, but disparate sources are not communicating with one another. Addressing this disconnect and lack of communication is something that can provide researchers, lawmakers and the public with improved insights.
Data-driven healthcare can help provide this guidance by using available data and analytics to help create programs that can make a tangible difference on population areas that need the most help. By looking at the data, lawmakers, hospital administrators and doctors can begin to make impactful changes throughout the system.
While much can be learned from this data, most of it is not being analyzed in a way that brings true benefits. It has been put in a silo and/or it is not organized in a way that is interoperable with other data systems.
The 21st Century Cures Act, which established the Health Information Technology Advisory Committee, shows the commitment of national leaders to improving healthcare information sharing. Analytics can take this data and turn it into something real. Subsequent visualization of this analyzed data presents the information in a way that can truly tell a story, making sense of data that analysts sometimes miss. Analytics can arrange and organize data in different ways and pick up previously undetected trends or anomalies. This information can be turned into real programs that produce real outcomes for those affected.
The data management and integration process can also help us understand where our knowledge gaps are, revealing flaws in data quality and availability. Organizations may learn that they lack sufficient data in a certain area where they want to learn more, but are currently limited. They can then make changes to data collection efforts or seek out different sources to fill these larger gaps. They can resolve data quality issues across systems and arrive at a consistent, reliable version of the truth.
As organizations get better at assembling and managing the data, automating processes to generate standard reports and file exchanges can ease the burden on analysts. Streamlining the user interfaces for prescription drug monitoring programs and other systems allows analysts and medical informatics staff to spend less time working on the data itself and more time enabling and encouraging the use of predictive modeling and “what-if” scenario capabilities.
Helping to Solve a Problem
The national opioid epidemic is a terrible and complex issue. It is not something that can be solved with just one action, approach or program. It is a layered issue that will require systematic changes to how patients are treated and how the healthcare system operates. Some of the nation’s best continue to work on providing operational solutions to these problems, but as the statistics show, they need more help.
A data-driven approach can be that help. Using data analytics to find better and deeper insights into the root problems of this epidemic can help decision-makers make real change. While opioids are the focus now, there will come a day when a new problem emerges. Having data and analytic solutions in place can prepare these organizations to tackle these future challenges as well.
64,000 people died in 2016 as a result of opioid abuse. But 64,000 is more than a large number – it’s also Andy and his family. With analytics and a data-driven approach, government and healthcare leaders can make better decisions that can help people in need.
Steve Bennett, Ph.D., is the director of SAS' global government practice. He is the former director of the National Biosurveillance Integration Center within the Department of Homeland Security