Since 2000, the rate of deaths from drug overdoses has increased 137%, including a 200% increase in the rate of overdose deaths involving opioids (i.e. opioid pain relievers and heroin).1 In fact, almost 40% of all opioid overdose deaths involved a prescription opioid.2 In addition to the loss of life, opioid overdoses have resulted in an estimated $504 billion in economic cost during 2015.3
Historically, opioid addiction was an issue discussed in the context of illegal drugs such as heroin and other synthetic opioids. However, in the last few years this issue has expanded into the realm of prescription opioids and become a nationwide crisis that can no longer be ignored.
The key question for policy makers and other health care stakeholders is, “What can we do to mitigate this crisis?” The solution needs to address patient behaviors that lead to overdoses and the prescription of unnecessary opioids by healthcare professionals. There isn’t a single right answer that will address both of these underlying issues regarding patient behavior and unnecessary prescriptions. Each and every stakeholder including policy makers, payers, providers, patients, family members, and others (e.g., law enforcement) have a role to play in the solution.
Data analytics can be a key resource in understanding and responding to this crisis. For example, the analysis of claims can help payers and providers gain an in-depth understanding of the utilization patterns of prescribed opioids. Specifically, claims data for prescription opioids should be analyzed across three dimensions:
- Providers: What are the key characteristics of providers who are potentially overprescribing opioids and who are these providers?
- Prescriptions: What is the trend and utilization pattern including potential unnecessary use of opioid prescriptions in a given population?
- Members: What do we understand about the medical condition(s) and other key characteristics of individuals who are prescribed opioids with a specific focus on members/patients potentially receiving unnecessary opioid prescriptions?
We used the Milliman MedInsight platform to analyze sample prescription claims from 2012 to 2016 to demonstrate the analyses for answering these critical questions. The claims data was analyzed at an aggregated population level as well as at the individual member, provider, and claim level. Exploratory analyses were conducted within a plan population to:
- Determine if the overprescribing of opioids is concentrated and isolated within a specific geographic region or network.
- Understand spikes or changes in prescribing rates for specific opioids over time.
- Identify specific providers prescribing opioids at a higher than average prescription rate.
- Identify at-risk members and providers for proactive outreach and education.
- Evaluate the appropriateness of utilization given the use of pain management in selected co-morbidities.
The outcomes from the exploratory analyses were used to guide the in-depth evaluation and identify potential members and providers for targeted outreach.
One of the first places to start on the path to understanding opioid misuse is to track the ongoing utilization pattern for members across different lines of business (e.g., Medicare, Medicaid) over time as illustrated in Table 1. When a health plan has multiple lines of business, it is crucial to understand if the problem is occurring at an aggregate plan level, or within a specific line of business. Using a normalized metric such as scripts per thousand members provides an effective baseline metrics for comparison rather than simply comparing the sheer volume of opioid prescriptions. These baseline metrics can be further stratified by geographic region, product type (e.g., HMO, PPO), or even isolated by specific types of prescription opioids. We stratified the sample data set by lines of business and determined that the majority of the opioid scripts were being written within the Medicare population.
Table 2. Medicare – Top Prescribed Opioids in 2017
From here, we investigated the opioid prescriptions for the Medicare population to understand what types of opioids were being prescribed. Table 2 on the left shows that the majority of the opioids being prescribed within the sample Medicare population were short-acting opioids such as oxycodone/acetaminophen and hydrocodone/acetaminophen. We also analyzed the prescription rate over time to determine if there was a change in prescribing patterns associated with these two types of opioid medications. We found that the prescribing rate for hydrocodone/acetaminophen increased 133% over the last four years. This information can be useful for care managers as they think through mitigation strategies associated with different types of prescription opioids. The ability to analyze the prescription claims data from such different dimensions helps to hone in on a member population to be targeted for intervention.
Once a baseline understanding of opioid use is established, it is critical to identify sub-populations for effective targeting. It is not only important to identify the members who are receiving these prescriptions, but also understand why and how they are receiving them. A detailed member level drill down is essential to understand the underlying conditions and diagnoses related to opioid use. Moreover, the member and claim level details also provide insights regarding the providers prescribing these opioids. These findings are helpful to understand if members received multiple opioid prescriptions from more than one provider. Within our sample dataset, we isolated a member’s opioid prescriptions in 2016. We were then able to analyze the total prescriptions they had per month, and how many distinct providers wrote opioid prescriptions in that month. If a plan has a large proportion of members who are receiving opioid prescriptions from more than one provider, they can develop mitigation strategies such as a pharmacy lock-in policy, which “locks-in” members to specific providers in order to monitor and reduce unnecessary distribution.
The ability to conduct such detailed analyses helps develop a deep understanding of prescription opioid misuse which is essential to design an appropriate response. In order to battle the ongoing crisis, payers and providers can utilize their claims data and sophisticated analytics to pinpoint areas for interventions.
(1)Accessed on February 8th 2018 from the website of CDC (https://www.cdc.gov/mmwr/volumes/65/wr/mm655051e1.htm)
(2) Accessed on February 8th 2018 from the website of CDC (https://www.cdc.gov/drugoverdose/data/overdose.html)
(3) Accessed on February 8th 2018. ‘The Underestimated Cost of the Opioid Crisis’ by the Council of Economic Advisers – November 2017 (https://www.whitehouse.gov/sites/whitehouse.gov/files/images/The%20Underestimated%20Cost%20of%20the%20Opioid%20Crisis.pdf)