The opioid crisis has received widespread attention from the media, politicians and public health agencies alike, but the problem of opioid addiction and overdose persists.
In 2016 alone, 63,000 people died of a drug overdose, according to the Centers for Disease Control and Prevention. Of those, 42,000 involved opioids. And of those 42,000 deaths, 40% involved prescription opioids.
As healthcare and political leaders scramble to find an answer and curb the misuse of opioids, which are most often prescribed for pain, one strategy that has gained popularity is the use of analytics to help slow the flow of overdose deaths, particularly those involving prescription drugs.
Population Health Management
The most powerful approach may involve combining healthcare analytics with the tools and techniques used in population health management. Those strategies are what healthcare providers have used to combat health conditions such as obesity and diabetes.
As with all efforts involving analytics, the strategy starts with a large database that can help analysts find patterns among those who have a specific health issue. Once those patterns are detected in others, it gives healthcare providers the opportunity to intervene earlier and with a better chance at prevention.
This same approach can possibly work with opioid addiction.
How Data Analytics Are Being Used
Analysis of the impact of prescribing of opioids shows that how patients acquire the drug is problematic. About 1-in-15 people who are prescribed opioids end up developing an addiction, according to an article in the peer reviewed journal Population Health Management titled “The American Opioid Epidemic: Population Health Implications and Potential Solutions. Report from the National Stakeholder Panel.”
Much of the problem lies with how drugs are prescribed. For example, laws against doctors prescribing opioids over the telephone have led to an increase in the prescription of extra medication, “just in case.” This surplus of medication has contributed to the current epidemic, where two out of every three opioid addicts are using someone else’s medication, according to the Population Health Management article.
Also, “doctor shopping” patients have taken advantage of the fact that there has typically been a lack of data management in the healthcare system.
Every state (except Missouri) now has an electronic monitoring system in place that allows for tracking opioid prescriptions – both the patient and prescriber. With this database, states can look for signs of potential misuse or abuse.
That can include unusual habits among patients, such as frequent prescriptions or filling prescriptions without a recent doctor’s visit.
However, not all state systems are the same. And many have loopholes that can be exploited, such as not requiring frequent enough reporting.
One of the most important aspects of using analytics to combat the opioid crisis is the sharing of data. A free flow of information between primary physicians, insurers, government healthcare agencies and public treatment centers that work with addicts and law enforcement could help identify opioid abusers early.
Other potential uses of this data include:
- The ability for the government to see which treatment facilities are having the most success and fund them accordingly
- Supporting more informed medical decisions by allowing healthcare professionals to know immediately if a patient is already on pain medication
- Finding better ways to help patients transition out of addiction, based on data showing what methods work best in certain situations
Data analytics has potential in fighting the opioid epidemic, but it will take a lot of effort on the part of healthcare providers, insurers and government agencies to share data. Only then can analytics develop the clear picture needed to spot trends in opioid abuse and, hopefully, prevent deaths.