June 2, 2017
The True Face of Opioid Addiction
by David Hom
The tough reality is that addicts are everywhere. We need to start using behavioral analytics to help identify them and help them in time.
It’s likely that when people hear about the growing opioid addiction problem in America, the face that comes to mind is the one commonly shown on TV and in the movies, which is a very broad generalization : the young, strung-out heroin addict living on the streets. Or dying of an overdose.
Heroin abuse is definitely a growing problem in America. But it’s not the only opioid-related issue we’re facing. In 2012, an estimated 2.1 million people were suffering from substance abuse disorders from prescription opioid use, and deaths from accidental overdoses of prescription pain relievers quadrupled between 1999 and 2015. Sales of prescription opioids also quadrupled during this period.
While prescription pain killers are often seen as a gateway drug to heroin among the young, the issue is much broader than just one demographic group. The reality is that the face of opioid addiction could be the soccer mom down the block who has been experiencing back pain. It could be the marathon runner who is trying to come back after knee surgery. It could be your grandmother baking cookies as she works on recovering from hip replacement surgery.
In fact, it could be anyone. And that diversity is what has made prescription opioid addiction so difficult to manage.
Drivers of addiction
What is driving this explosive growth of such a potentially dangerous substance? Part of it, quite frankly, has been the incredible improvements in healthcare over the last 20-some years. Hip replacements, knee replacements, spinal surgery and other procedures that were once rare are now fairly common. More surgeries mean more patients who need pain relievers to help them with recovery.
The greater focus on patient satisfaction, especially as the healthcare industry shifts from fee-for-service to value-based care, has also had some unintended consequences. Physicians concerned about patient feedback from Healthcare Effectiveness Data and Information Set (HEDIS) measures or Medicare Star ratings have additional incentive to ensure patients leave the hospital pain-free. Physicians may prescribe opioids, particularly if patients request them, rather than relying on less addictive forms of pain management.
See also: In Opioid Guidelines We Trust?
Here’s how that translates to real numbers. An analysis of 800,000 Medicaid patients in a reasonably affluent state showed that 10,000 of them were taking a medication used to wean patients off a dependency on opiates. This particular medication is very expensive and difficult to obtain – physicians need a specific certification to prescribe it. So it is safe to assume that the actual number of patients using prescription opiates is two to three times higher.
Those numbers aren’t always obvious, however, because the prescriptions may be obscured under diagnoses for other conditions such as depression. Indeed, more than half of uninsured nonelderly adults with opioid addiction had a mental illness in the prior year and more than 20% had a serious mental illness, such as depression, bipolar disorder or schizophrenia, according to the Kaiser Family Foundation. The result is that, without sophisticated behavioral analytics, it can be difficult to determine all the patients who are addicted to opioids. And what you don’t know can have a significant impact on care, costs and risk.
Complications, risk, and prioritization
Opioid addiction tends to interfere with the treatment of other concerns, especially chronic conditions such as depression, congestive heart failure, blindness/eye impairment and diabetes. As a result, physicians must first take care of the addiction before they can effectively treat these other conditions.
That is what makes identifying patients with an addiction, and prioritizing their care, so critical. Failure to do so can be devastating, not just clinically but financially – especially as healthcare organizations take on more risk in the shift to value-based care.
Take two patients with an opioid addiction who are on a withdrawal medication. Patient A also has eye impairment while Patient B is a diabetic. If the baseline for cost is 1, analytics have shown that Patient A will typically have a risk factor of 1.5 times the norm while Patient B, the diabetic, will have a risk factor of 5 times.
Under value-based care, especially an Accountable Care Organization (ACO) where the payment is fixed, the organization can lose a significant amount of money on patients who are costing five times the contracted amount. For example, if the per member per month (PMPM) reimbursement for the year is $2,000, this patient — who is using this medication for withdrawal from an opiate dependency and is a diabetic — will end up costing $10,000.
It is easy to see why that is unsustainable, especially when multiplied across hundreds or thousands of patients. Yet the underlying reason for failure to treat the diabetes effectively – the opioid addiction – may not be obvious.
Healthcare organizations that can use behavioral analytics to uncover patients with hidden opioid dependencies, including those on withdrawal medications, will know they need to address the addiction first, removing it as a barrier to treating other chronic conditions. That will make patients more receptive to managing conditions such as diabetes, helping lower the total cost of care.
They can also use the analytics to demonstrate to funding sources why they need more money to manage these higher-risk patients successfully. They can demonstrate why an investment in treating the addiction first will pay dividends in the long term with a variety of chronic conditions.
See also: How to Attack the Opioid Crisis
It’s easy to see that opioid abuse in all forms has reached epidemic levels within the U.S. What is not so easy to see at face value is who the addicts are — or could be.
Despite popular media images, the reality is that opioid addition in America has many faces. Some of them may be closer to us than we think. Behavioral analytics can help us identify with much greater clarity who the likely candidates are so we can reverse the trend more effectively.