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 useful 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 answers and 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 provide valuable potential in combating the opioid crisis through better pain management solutions, stricter control of prescription abuse and drug theft, more significant insights into addictive behaviors, and relapses using predictive analytics and digital tools for ongoing support for recovery and rehab.
AI is never intended to replace the doctor’s clinical expertise, but instead to close knowledge gaps and present the practitioner with the latest scientific research to consider when exploring pain management options. Further, Artificial Intelligence can identify patterns of behavior predictive of addiction so that healthcare providers can intervene before the abuse occurs. There are no simple solutions to the opioid crisis, but machine learning can offer healthcare organizations options to mitigate the risk of abuse. HealthTech can offer support and constant intervention. Wearables and mobile devices will track behavior and alert healthcare professionals when something is amiss.
Further, these devices can help patients self-manage their care through reminders, peer to peer support, and direct interactions with their care teams via TeleHealth applications and tools and resulting in positive behavioral changes. As patients will have more significant support avenues, healthcare providers can focus their efforts and optimize their workflow. The more patients interact with digital health devices, the more data that is collected for analysis. As the data increases, machine learning gets smarter. It can begin to identify the potential for abuse earlier, allowing healthcare providers to intervene with a preventative measure to prevent relapse or misuse.
Further, AI will track adherence to treatment protocol and the efficacy of various medications that will offer valuable insights to healthcare providers, pharmaceuticals, and payers to determine the best course of care for each patient resulting in better care outcomes. The opiate epidemic is a severe and overwhelming problem in healthcare. HealthTech solutions combined with Machine Learning offer opportunities to intervene earlier and reduce identifiable abuses. While this alone won’t solve the problem, it begins to offer providers tools to address the primary causes of abuse and offer patients support in their recoveries. With less abuse, healthcare organizations will experience better outcomes, safer environments, and direct savings to the bottom line.
