Risk Classification Modeling to Combat Opioid AbuseImprove Data Sharing to Identify High-Risk Prescribers of Opioids

Christopher SterlingChief Statistician NCI, Inc.

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According to data from the Centers for Disease Control and Prevention (CDC), opioids contributed to over 40,000 deaths in 2016 and more than 1,000 people every day are treated for prescription-related opioid misuse . Practitioners that overexploit their authority to prescribe are provoking the unnerving rise in opioid-related patient safety issues, as well as increasing the risk of opioid-related fraud and abuse.

To help address this serious ongoing public health issue, organizations are beginning to leverage big data analytics to develop risk classification models that identify these bad agents, as well as the patients at the highest risk for substance abuse. Let’s look at how silos of data across multiple organizations such as clinical care and pharmacy centers, public and private healthcare insurers, and state and federal agencies are being broken down and integrated to develop statistical models.

Data Access, Data Collection and Data Usage It traditionally has been very difficult to access various types of claims data. For example, many oversight agencies do not have access to both clinical and pharmacy data. Public and private healthcare organizations often retain separate locations and access for their data, which creates a “silo” effect. Zoom out even further and you will find gaps in the Medicare and Medicaid system, the largest healthcare payer in the world when combined, yet these programs are administered separately at both the national and state level.

Now, government healthcare payers, such as the Centers for Medicare and Medicaid Services (CMS) and the Department of Veterans Affairs (VA), are starting to play a big role by opening access to its claims data, as well as provider and eligibility data. With the Medicaid population being among the most vulnerable, accessing this type of data would allow the ability to see trends for providers that span states across various regions. When combined with other drug integrity programs, data analysts gain an even clearer and more thorough picture of prescription patterns for prescribers and dispensers.

Additional efforts are being made to increase communication with state medical boards and other governing bodies in an effort to promote further cross-state data sharing. In fact, the President-appointed Opioid Commission recommended a slew of enhancements to the Prescription Drug Monitoring Program (PDMP) in a report released last fall. Law enforcement is becoming more involved in regional task forces and even broader Department of Justice efforts are being established in some of the highest-risk areas.

With better mechanisms in place, the value of improving data sharing at the state level is critical in helping curb abuse of opioid distribution, particularly at the prescriber level. For example, dashboards could facilitate easier sharing and, as a result, produce more visibility and accountability.

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https://www.cdc.gov/drugoverdose/data/overdose.html

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One of the hardest hit demographics is the veteran population. Within VA health systems, opiate prescriptions are readily accessible and distributed. Per the VA’s own admission, the number of veterans reporting opioid addictions grew by 55 percent over a five-year period (from 2010 to 2015), and accounted for more than 68,000 veterans who suffer from opioid related disorders .

The VA is an active participant in PDMPs. Using a statewide database, VA providers are able to access state information on controlled prescriptions dispensed to veterans outside the VA healthcare system. Available prescription data includes the type of medications prescribed, who issued the prescription, the amount or quantity prescribed and suggested dosage. As PDMP programs and information exchanges fully deploy nationwide, providers outside the VA will be able to access data on controlled prescriptions given to their patients from within the VA healthcare system. This bi-directional visibility will enable more informed insights into drug history and potential risks, resulting in better decision making that can reduce opioid misuse and abuse.

Constructing Statistical Models Evaluating clinical and pharmacy claims analytics accelerates fraud, waste and abuse detection efforts. A statistical model can be constructed to identify patients at the highest risk for substance abuse and chemical overdoses based on predictive factors found in published, scientific literature on the topic, such as the following:

• Average daily dose

• Total supply of non-opioid drugs

• Overlapping prescriptions

• Number of prescribing and dispensing providers

• Past diagnoses, including those for mental illness or chronic pain

• Lack of appropriate drug testing

• Demographic information

Special data packages can be constructed for each identified prescriber, listing the patients and claims associated with key actionable risk factors, including: average daily dosing in excess of the CDC-recommendation per day, patients assigned a substance abuse diagnosis by a different provider during the days’ supply of a prescription by the provider in question, and patients with a diagnosis of drug overdose from providers during the days’ supply of the prescription. A recipient risk score is created and rolled up to the provider level and, therein, flags providers with sizeable populations of risky patients. These providers are further scrutinized for other patterns of aberrant billing and potential fraud, waste and abuse in healthcare systems.

2 https://www.veterans.senate.gov/imo/media/doc/VA%20Clancy%20Testimony%20

3.26.20151.pdf

Overcoming Challenges in Interoperability and Standards Providers at all levels of private and public healthcare organizations should continue to shore up their active participation in more frequent and collaborative data sharing. At the same time, industry has to help establish interoperable and integrated health information exchange systems and standards. Artificial intelligence (AI) could also become a bridge between federal and private payers as a way to facilitate and exchange data. As more models for risky providers or patient populations are developed and become established — think of AI as the agent to scour the data for unforeseen trends and anomalies that could add and / or refine the model for providers and beneficiaries.

As the practice of opioid prescribing continues to garner major scrutiny, opening up more pathways to access data is becoming very effective. The need to improve and facilitate opioid data exchange remains imperative — as does the ability to capture, handle and access such a large amount of data across federal and state systems and queries.

Virtualization and data management technologies are helping create pathways. Over 20 billion claims and millions of covered participants across every type

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Results in ActionOutcomes can be seen in provider investigations, collaboration with federal and state government, Medicare and Medicaid administrative actions, law enforcement investigations and medical board reviews.

Using the model above, NCI leveraged Medicare and Medicaid data to conduct a study designed to identify high-risk prescribers of opioids. More than half a billion pharmacy claims and nearly 15 billion clinical claims were analyzed to determine the relationship between opioid prescription behavior and clinical outcomes. As claims are continually received, the model is refined and improved.

From the original model, the results were significant. NCI referred 33 providers/groups for potential inappropriate prescribing practices for further investigation. All referrals were sent to the respective Medicaid Program Integrity units, with the majority sent to the state medical / license boards, CMS, Quality Improvement Organization (QIO), and, in some cases, the Medicare Drug Integrity Contractor (MEDIC) to potentially collaborate on mutually beneficial prescribers / pharmacies identified in the study. These referrals generated 27 Zone Program Integrity Contractor (ZPIC) investigations.

The study identified millions of dollars at-risk in Medicare and Medicaid programs. Numerous bad agents have been identified and are referred for review to outside agencies and medical boards, and some states have organized task forces in response to addressing issues of high-risk opioid prescribers. The statistical model also has identified at-risk pharmacies, some of which have strong connections to the at-risk prescribers.

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of healthcare benefit can be aggregated and analyzed to uncover risk in opioid fraud and abuse. Data can be retained in a highly virtualized and modern server environment and implementing predictive algorithms can help orient and store data in a fashion optimized for data analysis.

The goal in combating the opioid crisis using this type of modeling envisions a future where providers can access data at the point of care, integrate it into their clinical workflows and EHR systems, and care for patients with effective pain management treatments that ultimately improve their longevity and wellness. Looking at predictive analytics and trends analysis of claims data can surface individual risk scores to intervene at the patient level, as well as aggregate at the prescriber and pharmacy levels, to flag practices contributing to the riskiest patient populations.

About the Author Christopher Sterling helps to lead NCI’s Agile & Analytics sector in combatting fraud, waste and abuse in Medicare and Medicaid healthcare systems. As chief statistician, he manages and conducts proactive data analysis in support of this goal. With a background in mathematics, specifically graph theory, Christopher has helped lay the groundwork for identifying complex networks of providers, and his interest in applying mathematical concepts in the fight against Medicare and Medicaid fraud has resulted in many successful recoveries for these programs.

Navigate, Collaborate, InnovateNCI is a leading provider of enterprise solutions and services to U.S. defense,

intelligence, health and civilian government agencies. The company has the

expertise and proven track record to solve its customers’ most important and

complex mission challenges through technology and innovation.

With core competencies in delivering

cost-effective solutions and services in areas such as:

• Agile digital transformation

• Advanced analytics

• Artificial intelligence

• Cybersecurity and information assurance

• Engineering and logistics

• Fraud, waste and abuse detection

• Hyperconverged infrastructure

Coupled with a refined focus on strategic partnerships, NCI is committed to

bringing commercial innovation to missions of critical importance.

Headquartered in Reston, Virginia, NCI has approximately 2,000 employees

operating at more than 130 locations worldwide.

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