No One Party Or Workflow Can Address The Opioid Crisis

By Richard K. Grape, LexisNexis Risk Solutions

This is the second of a two-part series that discusses the role that data analytics can play in helping to fight the opioid epidemic. Click here for part one.

Data from both traditional health sources and non-medical sources, wrapped in analytics, can offer a unique view of individuals as they engage various stakeholders throughout the healthcare system. These data insights can enable extrapolation and identification of risk factors that are often complex and indicative of opioid abuse and misuse.

Bringing together different data assets can empower unprecedented decision making and can help health stakeholders to Prevent, Detect and Collaborate to address known or emerging risks as they engage with the different stakeholders throughout the industry. Together these three core tenets can help eliminate unknown circumstances, health risks, and questionable behaviors that contribute the proliferation of opioids within the industry.

Prevention

One of the greatest challenges facing the industry is figuring out how to keep risky drugs from ending up in the wrong hands before any damage ever occurs. To meet this need, healthcare can use data and analytics to identify at-risk individuals and integrate risk prevention at the front-end—during point-of-service interactions, health plan enrollment, provider credentialing, and plan prepayment claims processes.

What can preventive screening surface, and how might this help across healthcare?

• For health plans: During patient enrollment, preventive screening can reduce patient health risks by:
  • Identifying historical drug use (or lack thereof)
  • Identifying dependent behavior
  • Identifying drug risk associated with their social group

This enables health plans to engage the appropriate staff in order to connect with the at-risk patient early on.

• For health plans: During provider and pharmacy network enrollments, preventive screening can reduce network risks by:
  • Identifying historical and emerging abnormal prescribing habits among providers
  • Evaluating historical and emerging filling patterns among their pharmacy network; or by
  • Identifying at-risk locations

All of this empowers plans to take steps that prevent new or ongoing abusive prescribing/filling behaviors within their networks.

• For providers at point of prescribing and pharmacies at the point of dispensing, preventive screening can identify first-time drug users and identify at-risk drug patients to prevent individuals from obtaining opioids in situations that are suspicious, inappropriate, or possibly fraudulent.

Detection

The primary line of defense to guard against drug risks is the detection activities that are used to evaluate what occurred, when it occurred, the parties involved and how to ensure a high-risk event doesn’t occur again. This critical task is charged to the investigator. Health plans and PBMs detect risks by applying analytic controls to evaluate these “handshakes” that occur as patients visit providers, pharmacies, hospitals, and more -- ultimately generating claims that document what service or drug was transferred and to whom.

Ask Special Investigative Units (SIUs) how they detect risks today, and they will talk about rules-based data mining. Ask them how they will detect it in the future and you will hear about the need for analytics and outside data. The investigative workflow benefits the most by expanding their scope of available intelligence beyond just claims alone. Looking beyond claims, leveraging outside data assets, mapping relationships, and incorporating information from across disparate data sources, helps all parties detect and investigate entities that are perpetrating drug related risks. These entities, or the patients, providers, pharmacies, and social groups under investigation, suddenly become a robust image of interconnected relationships.

To date, detection occurs by setting rules and benchmarking patterns that identify claims and billing patterns that reveal outliers. These analytics root out inefficiencies, waste, misuse and even large-scale abuse, but investigators must continually mine data to identify emerging patterns and develop new rules to stay ahead of bad actors. Since sophisticated analytics yield few false positives, SIUs can become more efficient, directing their resources to a smaller subset of the claims or claimants that merit greater scrutiny.

Further, single claims often may appear ‘cleared’ or ‘less risky’ when viewed by themselves. It’s only when grouped together with other claims, or by evaluating connections between claims such as a similar social group, time or location, that these same claims are found to be suspicious.

In the past, rules-based fraud detection systems that analyzed claims and identified outliers were deployed post-payment. By moving this operation to the frontend of the claims payment process and incorporating relationship mapping and predictive modeling techniques into workflows, health plans and PBMs have a better chance of detecting risky entities early on, reducing future improper payments.

Collaboration

Prevention and detection activities account for the various healthcare touchpoints that can help root out risks, but there is a third tenet, largely unused, that has the greatest opportunity to change drug-related risks for the better -collaboration. All parties within the healthcare industry have an opportunity to work together, aggregating data across constituents, to share known risks, entities, insights, and emerging trends that can stem the tide of opioid abuse. Such an effort would take an unprecedented amount of industry cooperation, but it can be done and should be done in a forum that shares critical intelligence without violating patient privacy or compromising competitive advantages - just think of the rewards!

Imagine the possibilities: If once an at-risk patient, risky provider, pharmacy or social group is identified, then, every business across the industry could immediately leverage this information to prevent, detect and mitigate further behaviors that jeopardize a patient’s health or otherwise undermine the industry’s integrity. Imagine the possibilities if the parties who were once on their own for finding and fighting drug misuse and abuse were able to surface not only the bits of intelligence they had gathered along the way, but the intelligence of others.

The industry is ripe for change to occur as the value of sharing data begins to outweigh the risks. Clearinghouse solutions and contributory networks that bring data together are already successful in other industries such as government, law enforcement and mortgage, property and casualty insurance. Once risks are identified, collaborative stakeholders can immediately benefit from that insight. Similarly, in order to manage the growing challenge of the opioid crisis, pharmacies, health plans, PBMs and providers have a unique opportunity to collaborate and impart their collective knowledge, informing all future entity interactions for high-risk drugs.

About The Author

Richard K. Grape, Jr. is the director of market planning for LexisNexis, overseeing efforts in the areas of fraud, waste and abuse for the Health Care business. In this role, Mr. Grape is a strategist that directs the overall market, product, vision and sales strategies for the company.