When it comes to writing prescriptions for opioids, nearly two-thirds of emergency department physicians underestimate how often they prescribe the highly addictive painkillers for patients. However, ED doctors who were made aware of their prescribing patterns using electronic health record data reduced prescription rates.
That’s the finding of a year-long prospective, multi‐center randomized trial conducted by researchers at the University of Colorado Anschutz Medical Campus and the University of Massachusetts Medical School.
Published earlier this month in the journal Academic Emergency Medicine, the study involved surveying 109 providers at four different hospital EDs in the University of Massachusetts healthcare system about their perceived opioid prescribing rates compared to their peers—and, then showing them the actual data on prescriptions extracted the EHR.
Also See: EHR data helps predict opioid use
“The number of providers who had inaccurate self-perceptions was striking to us,” says Sean Michael, MD, an author of the study and assistant professor of emergency medicine at the University of Colorado School of Medicine. “Most people think that they’re doing the right thing. But, they didn’t have a quantitative understanding of how they were actually doing. Most people’s gut feel didn’t match with the real data.”
Michael notes that 65 percent of those providers surveyed prescribed more opioids that they thought they had. Over the course of the 12-month study, they wrote 75,203 prescriptions—of which 15,124, or about 20 percent, were for opioids.
Nonetheless, after reviewing their prescribing data from the EHR, providers with inaccurate self-perceptions—on average—had 2.1 fewer opioid prescriptions per 100 patients six months later, and 2.2 percent fewer prescriptions per 100 patients at 12 months.
“When we showed them their real data, they actually did start prescribing less, and they actually had bigger decreases in prescribing than people who didn’t have underestimation or who didn’t get their data at all,” observes Michael. “Showing people their data and letting them have that a-ha moment seemed to actually change their behavior.”
He contends that the problem with clinicians on the frontlines of healthcare, such as emergency medicine physicians, is that most of the time they don’t have robust EHR systems to understand quantitatively how they are doing when it comes to writing opioid prescriptions—or relative to their peers.
“Most of our systems have the ability to collect that data, but we don’t really have the mechanism to show it to front-line providers,” Michael concludes. “If we can build in some of those mechanisms to help people understand how they are doing and how their peers are doing, then we can actually drive some meaningful improvements. As an informatics community, we should be thinking about ways to do peer comparisons that are easier and don’t require a big research investment.”
At the same time, Michael points out that only about 5 percent to 10 percent of all opioid prescriptions are written by ED physicians in the United States. Nonetheless, he believes the lessons learned from the study apply more broadly to other healthcare professionals.
“Despite making progress on the opioid epidemic, we can’t assume providers are behaving optimally and have all the information they need to do what we are asking of them,” adds Michael. “Most believe they are doing the right thing, but we need to directly address this thinking to be sure they are not part of the problem.”
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