Every state except for Missouri has implemented a prescription drug monitoring program, or PDMP, to combat opioid misuse, focusing mainly on inappropriate prescribing and patients which exhibit behavior consistent with doctor shopping. The goal is to enable states to collect and analyze data about the prescribing behavior of practitioners, the prescription habits of patients, and the activities of pharmacies. In addition, PDMP data should provide insight and intelligence to drive improved clinical decision-making.
Without question, the United States has experienced an increase in the use of prescription opioids in recent decades. Although it seems contradicting, opioid prescribing has been on the decline. In fact, a report from the American Medical Association Opioid Task Force stated that the number of opioid prescriptions decreased by more than 22.2% between 2013 and 2017, and a follow-up report went on to show a 37.1% decrease between 2014 and 2019. However, despite the decline in opioid prescribing, the amount of opioids prescribed per patient in terms of morphine milligram equivalents remains approximately three times higher than two decades ago. Combined with the fact that close to a quarter of one million people died from overdoses involving prescription opioids between 1999 and 2019, it leads one to realize that increased restraints on opioid prescribing is not the single best solution to the problem.
When PDMP data is utilized during the clinical decision-making process, it enables healthcare providers to positively influence patient outcomes by:
Generally speaking, the PDMP is able to serve as a tool that enables prescribers to track their patients' opioid use and pinpoint those who start to exhibit drug-seeking behavior, which may be an early indicator of opioid dependence. In the past, states with PDMPs have reported smaller increases in admissions to opioid treatment facilities.
Additionally, studies have shown that healthcare providers who routinely use a PDMP are more mindful when it comes to prescribing opioids, and state PDMP implementation has often resulted in substantial decreases in opioid prescribing.
For example, after the state of Florida implemented its PDMP, oxycodone prescribing declined by over 50% within two years, not to mention the fact that oxycodone-caused mortality declined 25% the month following PDMP implementation. Because the number of PDMP queries was inversely related to the decrease in mortality, the statutory authority of prescribers to access the state PDMP is believed to have contributed to the outcome. Whereas the post-PDMP decrease in opioid prescribing has allegedly caused some patients to seek out illicit drugs like heroin, there is a lack of statistically significant data to support this claim, warranting further research into the unintended outcomes of PDMPs, especially in light of newer systems that are deemed to be operationally more effective.
Florida’s PDMP is also attributed with reducing emergency department visits and hospitalization rates due to opioid overdose. Likewise, other states have experienced decreases in opioid-related deaths subsequent to implementing a PDMP.
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Some studies have sought to classify PDMPs as superior vs. standard or proactive vs. reactive in an effort to evaluate how variations in PDMP architecture contribute to patient outcomes. Evidence shows that PDMPs that are more robust in their design and governance directly correlate to slower increases in opioid treatment admissions and reduced overdose mortality.
Whereas states with standard PDMPs have demonstrated slower increases in opioid treatment admissions over time, states with superior PDMPs have demonstrated even slower increases. Similarly, PDMPs classified as proactive have been associated with fewer deaths related to opioids.
As it stands, some PDMPs have limited capabilities and are unable to provide a complete picture of a patient’s opioid use history, or furthermore, analyze patient data. Additionally, PDMP laws differ from state to state, especially with regard to the extent that providers are required to utilize and update PDMP patient data. After the HIMSS Electronic Health Record Association’s Opioid Crisis Task Force looked at data from across the U.S., it found considerable differences with respect to PDMP data and accessibility, which has contributed to clinical workflow inefficiency and ultimately impeded the fight against opioid abuse.
Providers like Davies Award recipients Ochsner Health System, Yale New Haven Hospital, Atrium Health, and Mercy Health (Cincinnati) along with systems like Partners Healthcare and Yavapai Regional Medical Center have adopted a PDMP system to enable access to PDMP data from within the EHR, eliminating the need to have to log into the PDMP separately and perform a manual search for patient information. According to a time study conducted at Yavapai Regional, it now only takes 10 seconds for providers to access PDMP data.
Other healthcare systems are taking initiative in the management of opioids by employing technology to create proprietary platforms capable of tracking opioid prescribing and analyzing data in order to predict the likelihood of patients becoming addicted. Even so, the key may still lie within increasing interoperability between EHRs and PDMPs, as demonstrated by an endeavor of the Office of the National Coordinator for Health Information Technology to develop standards for improving EHR-PDMP interface functionality.
When clinical decision support is available at the start of care, high-risk patients are more easily identified, affording providers an opportunity to initiate interventions ranging from simple discussions about risks associated with opioid use to pain management strategies involving opioid tapering, and when necessary, referral to counseling or opioid dependency treatment facilities. It is imperative for providers to then use a population health management approach and continue to monitor high-risk patients as well as follow up with those receiving treatment in an effort to foster better outcomes, which is the ultimate goal.
The views and opinions expressed in this content or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.
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