According to the CDC[i], every day around 130 Americans die daily from an opioid overdose. That translates to 47,450 people per year – imagine the population of a suburban town or small city disappearing over the course of a year.
The answer to the American opioid epidemic will take work on many fronts, including the healthcare system, the pharmacy industry, law enforcement and public policy. The common thread running through all facets of a solution to this deadly problem, however, is data analysis. Important prescriber, pharmacy and member patterns live in databases nationwide and can be detected through analytics. These patterns can point the way to different interventions.
Take for example the recent Federal opioid sting[ii] which allegedly found close to 60 doctors, pharmacists, nurse practitioners, and other healthcare professionals involved in illegal sales of opioids and insurance fraud in Appalachia. The criminal investigation started earlier this year and the first step in the process was sending healthcare fraud investigators to conduct data analyses to identify outliers in prescriber information.
Patient Characteristics and the Potential for Opioid Dependence
The Federal arrests in Appalachia highlight the role that “bad actors” play in perpetuating the opioid epidemic. However, many cases of opioid dependence aren’t the result of malicious intent on the part of providers. Data analysis is starting to reveal important connections between patient characteristics, prescriber behaviors and the potential for opioid dependence.
A recent study by McKinsey & Company[iii] analyzed insurance claims data and revealed several meaningful insights about opioid use. For example:
- Providers may prescribe opioids, even when patients have known risk factors that increases the likelihood that they will become dependent
- For example, patients who abuse non-opioid substances or who have two or more behavioral health diagnoses are at higher risk of abusing opioids. Approximately one-third (35 percent) of patients given opioid prescriptions were found to have characteristics like these.
- Patients who have prescriptions for both an opioid and a behavioral health issue have a 30 percent or greater probability of becoming dependent on opioids in the future.
- McKinsey’s analysis also found that behavioral health diagnoses like anxiety, bipolar disorder or recent suicide attempts were correlated with future abuse or dependence on opioids.
Discovering these types of patterns in data can help health plans and providers identify members who are at risk for opioid dependence before prescriptions are written. Population risk intelligence solutions provide detailed member-level profiles to stratify patients with multiple acute and chronic issues, as well as patterns of substance overuse or abuse. This information is essential for developing alternative plans for pain management and addressing existing substance abuse issues.
Risk-stratification is the key to grouping members into different segments that can be targeted with intervention programs. For instance, McKinsey’s research found that behavioral health, medical conditions and socioeconomic factors correlate with healthcare utilization patterns such as emergency room usage or inpatient visits. Population risk intelligence tools can identify individuals who frequent emergency departments with vague pain symptoms to obtain opioids, as well as individuals who visit several different practitioners to obtain multiple prescriptions for opioid drugs.
When Providers Are the Problem…
Unfortunately, a small number of healthcare professionals do consciously contribute to the opioid epidemic for personal gain. Once again, data analysis can identify the patterns that point to suspicious behavior.
In hospital settings, drug diversion is a real concern. Anomalies in the amount of pain medication given to a patient during his or her stay can be a red flag that hospital staff are diverting opioids from patients for themselves or for others. In the outpatient world, “pill mills” are an unfortunate reality. These practitioners inappropriately prescribe or dispense opioids, often without medical examinations, in exchange for money or access to health plan information so they can file false claims.
Population risk intelligence tools can help with this aspect of the opioid problem as well. Analytics highlight which providers and pharmacies are overprescribing certain drugs compared to their peers. Key metrics include provider opioid prescriptions by drug, provider and pharmacy opioid dose level patterns, pharmacy opioid prescription levels, and pharmacy and provider relationships.
In combination with member engagement and targeted intervention programs, population risk intelligence and data analytics are just one aspect of addressing the nation’s opioid crisis. But they are a promising start. To learn more about how population health intelligence can help, please download HMS’s white paper “Stopping the Human Tragedy of Opioid Abuse.”
[i] (Centers for Disease Control and Prevention, 2018)
[ii] (Johnson, 2019)
[iii] (Sarun Charumilind, Mendez-Escobar, & Latkovic, 2018)