RESUMO
PURPOSE: Real-time prescription benefits (RTPB) shows prescribers patient-, medication-, and pharmacy-specific information on medication pricing, prior authorization requirements, and lower-cost alternatives. RTPB is intended to improve patient satisfaction and prescription fill rates by decreasing out-of-pocket costs for prescriptions. Therefore, we evaluated how RTPB affects prescribing patterns by examining acceptance and subsequent fill rates for RTPB alternative suggestions. METHODS: RTPB was implemented in February 2022 using external vendor interfaces. Prescribing data from March 2022 to March 2023 were analyzed. RTPB displayed alerts for medications requiring prior authorization or when alternative medications would result in cost savings. Patients were included if their prescription received an RTPB response and they had a subsequent encounter with pharmacy fill data. Primary outcomes were alert acceptance rates and prescription fill rates across RTPB alert groups, with a secondary outcome of monthly copay savings for accepted alerts. RESULTS: RTPB requests received a response for 88% of prescriptions, with price estimates provided for 77.9% of them. Lower-cost alternatives accounted for 67.2% of alerts, while prior authorization requirements represented 15% of alerts. Prescribers selected a lower-cost alternative 32% of the time. For those with an RTPB alert, patients filled prescriptions 68% of the time when an alternative was chosen, compared to 59% of the time when the original prescription was retained (odds ratio, 1.5; 95% confidence interval, 1.5-1.6; P < 0.001). Patients saved an average of $27.77 per month on copay costs when alternatives were selected. CONCLUSION: Implementation of RTPB was found to result in significant improvements in prescription fill rates and decrease patient copay costs, despite low alert acceptance rates.
RESUMO
BACKGROUND: Patients taking high doses of opioids, or taking opioids in combination with other central nervous system depressants, are at increased risk of opioid overdose. Coprescribing the opioid-reversal agent naloxone is an essential safety measure, recommended by the surgeon general, but the rate of naloxone coprescribing is low. Therefore, we set out to determine whether a targeted clinical decision support alert could increase the rate of naloxone coprescribing. METHODS: We conducted a before-after study from January 2019 to April 2021 at a large academic health system in the Southeast. We developed a targeted point of care decision support notification in the electronic health record to suggest ordering naloxone for patients who have a high risk of opioid overdose based on a high morphine equivalent daily dose (MEDD) ≥90 mg, concomitant benzodiazepine prescription, or a history of opioid use disorder or opioid overdose. We measured the rate of outpatient naloxone prescribing as our primary measure. A multivariable logistic regression model with robust variance to adjust for prescriptions within the same prescriber was implemented to estimate the association between alerts and naloxone coprescribing. RESULTS: The baseline naloxone coprescribing rate in 2019 was 0.28 (95% confidence interval [CI], 0.24-0.31) naloxone prescriptions per 100 opioid prescriptions. After alert implementation, the naloxone coprescribing rate increased to 4.51 (95% CI, 4.33-4.68) naloxone prescriptions per 100 opioid prescriptions (P < .001). The adjusted odds of naloxone coprescribing after alert implementation were approximately 28 times those during the baseline period (95% CI, 15-52). CONCLUSIONS: A targeted decision support alert for patients at risk for opioid overdose significantly increased the rate of naloxone coprescribing and was relatively easy to build.