Feedback on the initial blog entry on the subject, Part 1 - The Opiate Epidemic: Applying Better Matching and Analysis, indicated it came across as a bit heavy-handed on enforcement and less focused on identifying abusers for treatment. Part 1 to share the improved application of real-time hyper-accurate matching in PDMPs, and yes, it focused on enforcement while not applying enough to identifying those who need recovery support earlier and getting them into treatment sooner, where it has a higher probability of success. This blog will drill down deeper on the issue of the matching as part of a more technical discussion.
Our country is dealing with an epidemic - the abuse of opiates. While they are medically necessary, the opiate epidemic leads to a death rate equivalent to a 747 crashing once a week. There is an immeasurable cost on society from recovery programs, incarceration, non-productivity, and broader emotional trauma. Early success can be credited to prescriber education and implementation of state-level Prescription Drug Monitoring Programs (PDMPs). This blog advocates a cost-effective and highly-effective approach using a shared PDMP with the state of the art in referential matching, real-time behavior analytics, and accurate national data sets indicating fraudulent IDs.
Live by my simple adage in cybersecurity, "Machines Don't Do Bad Things, People Do." When you look the potential vectors of cyber, physical, and personnel threats: the vulnerabilities, the mistakes, and the attacks, can all be traced back to a person. Using this adage in building a cyber defense strategy, provides a new kind of framework to measure and reduce threats. The challenge: even though you may see a machine going awry, it is really, really hard to find the "bad guy" before the vulnerability is exploited or the attack is in play. So, in an effort to come at this problem a new way, let's examine "Brent's Inverted Corollary of Cybersecurity" (breaking news), "Machines Don't Do Good Things, People Do".