According to the US Department of Health and Human Services, in 2016 over 115 million people abused prescription pain meds resulting in more than 42,000 deaths. And the epidemic continues to grow out of control. It is estimated the economic cost of the Opioid Epidemic exceeds $500 Billion annually. Contributing factors include subjective prescribing by
medical practitioners, drug diversion or theft by healthcare workers, patients gaming the system, and once addicted – the costs associated with relapses and rehabilitation programs.
Through the 1990s, the medical community believed that prescription opioids were not addictive and were a good tool for chronic pain management. The explosion of addiction has since proven otherwise. Increased misuse of prescribed painkillers led to increased abuse and addiction to both prescribed and illegal opioids. While there has been tremendous visibility of this crisis in the media, solutions are never easy. Legislating and criminalizing addiction only serves to exasperate the problem. Healthcare providers need to find solutions the identify problems earlier, so that intervention can be more effective.
Big Data and Artificial Intelligence have proven beneficial in offering insights to assist healthcare providers in many areas of early detection, drug discovery and best practices for value care. Data Analytics and Machine Learning offer valuable potential in combating the opioid crisis through better pain management solutions, stricter control of prescription abuse and drug theft, greater insights into addictive behaviors and relapses using predictive analytics and digital tools for ongoing support for recovery and rehab.
Read the Whitepaper – Fighting The Opioid Crisis With Artificial Intelligence