Earlier this year the Ontario Ministry of Health announced that it will delist high-dose opioids in January 2017 in order to help reduce the number of opioid overdose deaths in the province. While this is a start, addressing the opioid epidemic will require a comprehensive, data-based approach that touches all aspects of the health system, from prevention to treatment and recovery. Data analytics will be the key.

Proper data management and analysis can provide a broad spectrum of integrated solutions. It can help develop better treatment protocols, enable pharmacies to identify dispensing anomalies, and allow large hospital systems and public health agencies to better analyze the possible outcomes of well-intentioned initiatives. But for any solution to be effective, each of these groups must work together by sharing data and creating a flow of information.

Where to Start?

Fighting this epidemic is a highly complex challenge that requires a variety of players to collaborate in order to fully understand and address the problem. The key health and government groups that must be involved include:

  • Public Health Agency of Canada, as well as local substance abuse and mental health service groups whose mandate is to strengthen intergovernmental collaboration on public health and facilitate national approaches to public health policy and planning
  • Provincial health agencies that set local policy and standards for physician education
  • Pharmaceutical companies, distributors, and pharmacies that constitute the physical supply chain
  • Insurance companies and government services that pay for the prescriptions
  • Treatment centres that are at the front-line of assisting the addicted
  • Law enforcement agencies that deal with opioid abusers and can influence whether they are charged or treated
  • Physicians who are responsible for prescribing the drugs

All of these players have access to data that can be useful to one another. Databases of written and filled prescriptions, electronic health records, and emergency room records are all key pieces of the data puzzle the come directly from the healthcare system’s frontlines.

Working together to address a complex information management issue

Information on opioid use and abuse is partial, fragmented, and often not actionable. If stakeholders worked together to share and more effectively manage the data owned by each of these players, the origins of the problem would be better understood. The resulting insights could help make smart policy changes while motivating physicians and patients to change their behaviours. Analytics can enable all of these players to be more forward looking and predictive, and to avoid unanticipated consequences of well-intentioned initiatives.

For this to work, all parties in the healthcare system need to begin taking a data-based approach to care. From physicians and provider institutions, to licensing boards, these groups must begin to make decisions based on data. Physicians and provider institutions across the health care system require better, ongoing education about opioid abuse. A data-driven approach to education will help physicians understand what to look for. Where physicians and providers fall short, licensing boards will need to revise policies and standards of care to fill in the gaps. Again, data will provide the big picture view that is necessary to make informed policy decisions. Finally, data analysis can help public health groups, community coalitions, and substance abuse treatment improve their operations, measure their results, and contribute valuable data of their own to the larger cause of saving lives.

Through data sharing, physicians and their patients, medical policy makers and licensing boards, pharmaceutical companies and pharmacies that all work together can stem the opioid epidemic and achieve the fundamental objectives of reducing addiction and deaths.

Results and Benefits

By establishing partnerships and relationships based on information sharing, data management and analytics, we can help develop better treatment protocols. These moves will affect pain in the first place and remediation when patients become drug dependent. Physicians should know how their treatments and results compare with those of their peers, as well as what specific patterns give early warning of addiction or overdose.

Large hospital systems, licensing boards, and public health agencies need the ability to benchmark providers in order to get a better picture of where and how to educate them. Pharmacies need to know how they compare by geography, payment source, provider and patient mix. Data and analytics are fundamental to identifying anomalies of distance and dispensing behaviours. These organizations are in the best position to aggregate and give providers a peer-to-peer comparison. Analytics can inform treatment guidelines, educational initiatives, and resource allocations, including treatment centres and community prescription drug take-back programs.

Access to this data can help government agencies distribute funding for treatment facilities based on the combination of local need and which facilities have the best outcomes. Analyzing the populations in treatment for opioid dependence can inform medical decisions (such as when to prescribe Naloxone to counteract respiratory depression), suggest how to keep patients in the health care system (rather than risk them transitioning to another substance), and indicate which facility is likely to best fit the patient’s needs.

A Success Story In The Making

Despite the clear benefits of data sharing among stakeholders, Canada has yet to implement such a system. The practice is more common in the U.S. and one success story is the Commonwealth of Massachusetts, which saw opioid-related deaths increase 350% since 2000. It recently released a report entitled Chapter 55, in which it outlined how it took a data-first approach at tackling this public-health crisis. The Massachusetts case demonstrates how government, academia, and the private industry can all collaborate to answer a complex health question.

From working through complex legal issues around data privacy, to implementing multiple agency MOUs (memorandum of understanding) for data sharing, and addressing complexities with data integration and entity resolution/identity resolution, Massachusetts has shown that with the political will a data-based approach can pay off. With the help of SAS, the state set up a “privacy shield,” which is a mechanism that anonymizes or sanitizes personally identifiable information within the data, so that the information was safeguarded from the analysts conducting the work, yet still allowed the analytics to occur.

While data collection and analysis is ongoing, insights gleaned so far have been significant, and Chapter 55’s model of cooperative data analysis has the potential to become the new standard. For this type of initiative to be effective in Canada, provincial health ministries would have to drive the charge in establishing a similar type of collaboration and fortified data aggregation.

Conclusion

Bringing together data from all of these sources will help constituencies work more effectively together. It can provide a more comprehensive overview of the issue and identify areas for improvement in order to put actions in place that will make a real impact in the fight against opioid abuse. This can only begin once we have an in-depth understanding of the issue. Much like a GPS navigation system can tell you how to get somewhere, you still need to input a destination. Forming partnerships among these stakeholders will set the stage for a collaborative approach to analytics which can provide a dynamic view of how individuals are abusing the system, identify high risk individuals, and inform the best strategies for addressing the problem.

 

Greg Horne is the National Healthcare Lead at SAS Canada