PHARMACY

Teamwork and technology: Facing the opioid epidemic head on

The Opioid epidemic paints a grim picture that is impossible to overlook: according to the provisional 2017 data from the National Institute on Drug Abuse, the U.S. had 49,068 opioid overdose deaths, more than any previous year on record. The number of deaths involving a prescription opioid pain reliever climbed to 19,354 in 2016.  The economic burden is thought to be $78.5 billion a year, extending from healthcare to the economy to the justice system and beyond.

On a positive note, the industry has been heeding the frightening call to action: In 2017, the overall national opioid prescribing rate had fallen to the lowest it had been in more than 10 years at 58.7 prescriptions per 100 persons — total of more than 191 million opioid prescriptions. Even so, some counties had rates that were seven times higher than that in 2017.

As providers are prescribing fewer of these medications, pharmacists are taking new leadership roles in an effort to combat the crisis. For most pharmacies, this battle has brought significant changes to the workflow and workload of its employees, taking its toll. To assist in the identification, mitigation, and management of opioid abuse risk factors, pharmacists can leverage new analytic technologies designed to minimize the epidemic’s toll on both patients and the community at large. New tools use big data to identify social groups and other “teams” of schemers who work together to perpetuate the dangerous cycle of drug availability and abuse.

High-risk entities

Knowledge is power. Today’s available data streams — when applied strategically — highlight previously unknown details about the highest-risk stakeholders in the opioid epidemic.

Patients

Our patients, or healthcare consumers, are the most at-risk group in this dangerous equation. At-risk patients include individuals who are new to taking the opioid prescription type, as well as individuals who are intentionally abusing the drug or using it recreationally. Other consumers, albeit a small subset but the more dangerous type, are those who may acquire medications in order to sell them on the black market or to known contacts.

Patients can also be part of social groups which represent clusters or networks of individuals who work in tandem to drive drug diversion on a widespread level. Social groups can be uncovered by outside data technologies that reveal common links, such as the patient’s friends; family members; colleagues; and associates from various walks of life. Using such public records data sets, technology can pinpoint socialization patterns and layer on the footprint and the network of information associated in order to surface active entities and/or clusters of potential abusers or traffickers.

According to the Centers for Disease Control and Prevention, drug diversion, the transference of legally controlled and prescribed substances from one individual to another, is the number one avenue for opioid abuse. It’s critical for pharmacies to hone in on the largest source of potential risk: the social ties that remain present in the complex web of opioid interactions. Using analytics, technology goes outside the realm of health data and into traditional, non-medical sources of information to help identify unknown circumstances, risks, and questionable behaviors that contribute to the proliferation of opioids within the industry.

Prescribers

Providers play a key role in minimizing the number of prescriptions written and identifying the types of patients most at risk for fraud and abuse. Many errant provider behaviors are seemingly innocuous and simply require re-education. Inadvertently, physicians may write scripts for high-risk patients and even to family members of such patients. Other times, perilous actions are more intentional, such as writing opioids for friends and family members, or writing excessive quantities of certain drug types. Analytics, such as real-time prescriber verification, provider-patient socialization and patient record matching, can help capture these data points and flag specific situations to surface and demonstrate provider risk.

Pill Mills

Pill mills are truly a “team effort” of multiple high-risk entities, including patients, providers, and pharmacies who work to dispense drugs inappropriately or for non-medical reasons. It’s a collaborative and complicated operation that requires intense visibility of numerous factors, players, and environments. It’s a perfect scenario for application of data analytics, which can search across both provider claims and outside data sources to determine:

  • prescribers who are treating high percentages of patients receiving high-risk drugs;
  • pharmacies that are filling excessive numbers of scripts for these medications; and
  • prescribers and pharmacies that may be unknowingly participating in a pill mill operation by providing care to a large, specific social network who have organized to obtain large quantities of drugs for misuse.

Prevention tools

Pill mills often rely on “frequent fliers” or “doctor shoppers” who go to providers and pharmacies in close succession to divert drugs for resale and abuse. Other times, these individuals engage in risky behaviors to support a drug habit or to divert drugs to family or relatives in their social circle. The key here is that a social group can be identified through mutual history, joint employment or ownership, shared organizations and others. These insights can reveal a large-scale operation in high-risk patient and provider networks. By analyzing the data, technology identifies the risk represented by the entire network, revealing that seemingly “innocent” players are actually participants in a larger scheme.

By looking across drugs to determine net unsafe MEDs calculations, pharmacies can offer transparency about the other drugs a patient is being prescribed and indicate potential situations of diversion, abuse, or health risk. These figures can and should be tracked through technology — as should the MEDs totals of others within a patient’s social network—to identify the potential for abuse.

In addition to uses we have been discussing, analytics can play an important, proactive role during healthcare benefits enrollment, at point-of-service interactions, and through claims analysis – after care has been provided.  Understanding what occurred, when it occurred, and why gives all stakeholders information that may prevent an adverse event in the future.

Through industry-wide collaboration and availability of disparately sourced health and public records, we have an opportunity to learn more about the patterns of opioid abuse and to potentially help stop it in its tracks. The benefits of data sharing far outweigh the risks, so what are we waiting for?


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