Abuse of prescription opioid drugs is a significant contributor to the opioid epidemic that has struck across the United States. Prescription misuse plays a major role in the 115 daily overdose deaths and the overall economic burden of $78.5 billion per year. Addressing this issue is vital from both policy and analysis standpoints. That’s especially true for retail pharmacies, which are generally the last point of contact between patients and the health care infrastructure before the patients receive such drugs.
How can analytics and big data in health care help the fight against prescription opioid abuse?
Many businesses and organizations involved in the health care industry, as well as related local, state and federal government agencies, have a wealth of information related to patients in general and prescription opioids in particular. Prescription drug monitoring programs (PDMPs) at the state level already help health care industry workers and law enforcement achieve a number of positive outcomes, from limiting abuse to identifying potential issues with diversion and illicit sales. Compared to the state of intra-industry and government/health care industry cooperation less than a decade ago, this is a major improvement.
Of course, the PDMPs rely on a variety of data streams to provide effective analysis, offering an example of how big data in health care can be leveraged to address a serious, widespread issue. Much of the more foundational work is already done or at least in process, in terms of connecting and informing the many stakeholders that have to address this problem. The next steps involve making further improvements, creating broader connections, addressing weak points in current processes and continuing to develop effective strategies and systems to fight this scourge.
Big data: The great leveler
The sheer volume of patients seen by physicians and pharmacists makes it difficult to spot the warning signs of potential abuse in every instance. Although individual professionals fill crucial roles in the fight against prescription opioid abuse, they need support from big data and analytics to clearly visualize and respond to the big picture.
Health systems can implement tools to flag data that corresponds to risky behaviors and negative outcomes, excessive dosages, and numbers of prescriptions written to patients, as well as drug-drug interactions and similar concerns. Pharmacies can track refills and many other dimensions of dispensing information to reach similar conclusions. Payers can also contribute to building this data repository by sharing their own.
Combining this information and making it available to all interested and qualified stakeholders means providing the highest level of visibility. That in turn leads to creating clearer paths for a variety of follow-up responses for individual cases. Of course, pharmacies and all other participants have to remain vigilant of HIPAA and similar laws, as well as clearly determine acceptable uses for their data.
Early detection is an especially important consideration, as effective follow-up actions can help patients avoid the many negative outcomes of addiction, from physical harm to the consequences of breaking controlled substance laws. Discussions with patients are far more effective when an issue is spotted early on and brought to the table before it’s exacerbated and becomes an extremely entrenched, life-changing problem. The factual, evidence-based nature of data gives everyone an opportunity to learn, which is especially important in terms of early detection.
A robust source of data from across the health care system leads to more effective and complete analysis. Providers, payers, pharmacists and other actors need to build on current collaborative efforts and dedicate time and resources to building new ones to continue gaining momentum in the fight against prescription opioid abuse.
Current policy and potential improvements
The landscape for data sharing among health care organizations and government agencies working to address prescription opioid abuse is generally positive. PDMPs encourage and have achieved broader data sharing among pharmacies compared to a decade ago. More complete data sets make it easier for authorities to identify potential cases of fraud and other illicit behavior.
However, there’s plenty of room for improvement. There are a variety of ways in which pharmacies, other health care organizations, and local, state and federal authorities can both encourage more data sharing and utilize that information to derive additional insight. This aligns with federal efforts on the part of the Department of Health and Human Services, which guides the investment of hundreds of millions of dollars into a five-point program that includes strengthening the collection of relevant public health data, and other agencies.
The best path forward for encouraging more participation, cooperation and transparency is likely through incentives that promote voluntary cooperation and reward organizations. With incentives for positive performance already common in many parts of the industry, such as value-based care, familiarity will help organizations participate in data-sharing processes. This approach also creates a collaborative framework where participants join in of their own accord, which can foster a more cooperative environment.
The two most important points to consider for such an arrangement are setting strong but realistic goals with incentives tied to positive progress and ensuring all participants that their data can’t be used against them. A clear understanding of the data’s intended uses is vital for organizations from practical, legal and compliance standpoints, as well as for establishing trust and goodwill on the foundational level. The importance of effective, clear boundaries can’t be overstated in a voluntary framework.
Increased sharing of information and collaboration across the health care and government sectors is also vital for addressing a persistent and complicated problem within the opioid epidemic: those who skirt county and state borders to attempt to trick pharmacists and unlawfully obtain excess prescription drugs. Because of blind spots that exist in county-level programs and the state-level PDMPs, some have turned to such strategies as way to attack weak points in the current regulatory system.
This is where a more holistic view of the many streams of data that can improve opioid abuse outcomes is especially valuable. A central, third-party organizer — perhaps similar to the nonprofit, independent Workers Compensation Research Institute’s work bringing together stakeholders and data in that realm — could help organizations share information more quickly and effectively. Although not an absolute necessity, such an approach could reduce opacity in certain areas and ensure that analysis is well received by everyone involved.
A holistic approach also has to take mental health into account. Because opioid abuse can often stem from a desire to seek mental and emotional relief, addressing underlying causes of stress and worry is vital. Providers, payers and pharmacies can all play a role in increasing mental health awareness and outreach efforts.
Components of future progress
The key components of future progress on the opioid abuse epidemic include:
• Interstate data sharing.
• A voluntary, incentive-driven framework for such sharing.
• Clear boundaries for data use in that context.
• The potential development of a third-party agency for managing and assisting in data analysis.
• An increase in mental health efforts.
• Addressing cross-border drug seeking behaviors.
With these strategies and challenges in mind, the pharmacy industry and the many other stakeholders involved in addressing this problem can continue to deal effectively with the opioid abuse epidemic.
Maulik Bhagat is a managing director in the health practice of AArete, a global consultancy specializing in data-informed performance improvement. Michael Kim is a director in AArete’s retail practice, and heads its Center of Data Excellence. They can be reached, respectively, at email@example.com and firstname.lastname@example.org.