Impact of the RESPOND Toolkit on community pharmacists' opioid safety attitudes, self-efficacy, and knowledge.

OBJECTIVE: Pharmacists are well positioned to reduce risks from opioid-prescribing but often lack resources and training to effectively support these activities. The Resources Encouraging Safe Prescription Opioid and Naloxone Dispensing (RESPOND) Toolkit is an educational package developed to provide community pharmacists with a comprehensive education program and practice resources on prescription drug misuse, prescription drug monitoring programs (PDMPs), and naloxone dispensing. Our objective was to evaluate the effectiveness of the RESPOND Toolkit to improve pharmacists' knowledge and assess changes in pharmacists' attitudes and beliefs toward opioid use disorder (OUD) and PDMPs across a diverse pool of Oregon community pharmacists. METHODS: Pharmacists were recruited using an electronic mailing list of Oregon-licensed pharmacists. Pharmacists were asked to complete a preintervention survey, 3 online educational modules with pre- and post-module quizzes (optional), and a postintervention survey. Data were analyzed using paired t tests, chi-square analyses, and effect size calculations (Cohen's d). RESULTS: A total of 131 pharmacists completed the 3 educational modules and postintervention survey. Respondents were aged 37.6 ± 11.0 (mean ± SD) years and mostly frontline pharmacy staff (n = 86; 65.6%) with 10.5 ± 11.6 years of pharmacy experience. Pharmacists' knowledge and attitudes toward OUD, perceived behavioral control to address OUD, resources to address OUD, and perceptions regarding PDMP-associated difficulties improved significantly as a result of the intervention (all P < 0.001). In addition, 120 pharmacists completed the optional module quizzes, and aggregate knowledge assessment scores improved significantly (P < 0.001). CONCLUSION: The RESPOND Toolkit is an effective and scalable training resource for community pharmacists, with the potential to promote behavioral shifts that support opioid safety among patients. The results demonstrated improved attitudes, knowledge, and perceived behavioral control. Future work on the RESPOND Toolkit should evaluate the effect of implementation on pharmacist clinical activities and dispensing outcomes.

Development and pilot of a prescription drug monitoring program and communication intervention for pharmacists.

BACKGROUND: Pharmacists' role in addressing the opioid crisis continues to expand, but lack of training specifically related to standardized prescription drug monitoring program (PDMP) use and communication strategies for provider and patient interactions remains a significant issue. We developed the Resources Encouraging Safe Prescription Opioid and Naloxone Dispensing (RESPOND) Toolkit to enhance community pharmacists' understanding of their role in addressing opioid safety; improve integration of PDMP into daily workflow; and enhance communication between pharmacists, prescribers, and patients. OBJECTIVE: To describe the development of RESPOND Toolkit and summarize findings from initial pilot testing. METHODS: RESPOND development was informed by focus groups with patients, prescribers, and pharmacists and an external advisory committee. Materials developed include a patient screening & communication algorithm, a provider communication checklist and an online continuing education course with three distinct modules. The RESPOND Toolkit was pilot tested in six community pharmacies in Oregon across two 6-month intervention phases. Pilot data collection included a pre-post intervention survey, pre-post knowledge assessment quizzes within the online course, and post-intervention semi-structured interviews. Interview feedback informed revisions after each phase to shape the final content, flow, and delivery of RESPOND. RESULTS: Sixteen of 21 pharmacists completed the online training, revealing a large, significant effect on knowledge gain across the three training modules (pre-score 57, post-score 84; p < 0.001; Cohen's d = 1.85). Of these participants, 10 also completed the baseline and post intervention survey and showed non-significant moderate improvements in knowledge, perceived behavioral control, and self-efficacy to address opioid safety issues. CONCLUSION: The RESPOND Toolkit has promise as an effective and scalable approach to providing community pharmacist-tailored training, especially in the areas of effective communication and workflow integration, to promote behavioral shifts supporting opioid safety for patients. Further development and testing in a larger sample is warranted.

Opioid-Prescribing Continuity and Risky Opioid Prescriptions.

We aimed to better understand the association between opioid-prescribing continuity, risky prescribing patterns, and overdose risk. For this retrospective cohort study, we included patients with long-term opioid use, pulling data from Oregon's Prescription Drug Monitoring Program (PDMP), vital records, and hospital discharge registry. A continuity of care index (COCI) score was calculated for each patient, and we defined metrics to describe risky prescribing and overdose. As prescribing continuity increased, likelihood of filling risky opioid prescriptions and overdose hospitalization decreased. Prescribing continuity is an important factor associated with opioid harms and can be calculated using administrative pharmacy data.

Use of prescription opioids before and after an operation for chronic pain (lumbar fusion surgery).

Lumbar fusion surgery is usually prompted by chronic back pain, and many patients receive long-term preoperative opioid analgesics. Many expect surgery to eliminate the need for opioids. We sought to determine what fraction of long-term preoperative opioid users discontinue or reduce dosage postoperatively; what fraction of patients with little preoperative use initiate long-term use; and what predicts long-term postoperative use. This retrospective cohort study included 2491 adults undergoing lumbar fusion surgery for degenerative conditions, using Oregon's prescription drug monitoring program to quantify opioid use before and after hospitalization. We defined long-term postoperative use as ≥4 prescriptions filled in the 7 months after hospitalization, with at least 3 occurring >30 days after hospitalization. Overall, 1045 patients received long-term opioids preoperatively, and 1094 postoperatively. Among long-term preoperative users, 77.1% continued long-term postoperative use, and 13.8% had episodic use. Only 9.1% discontinued or had short-term postoperative use. Among preoperative users, 34.4% received a lower dose postoperatively, but 44.8% received a higher long-term dose. Among patients with no preoperative opioids, 12.8% became long-term users. In multivariable models, the strongest predictor of long-term postoperative use was cumulative preoperative opioid dose (odds ratio of 15.47 [95% confidence interval 8.53-28.06] in the highest quartile). Cumulative dose and number of opioid prescribers in the 30-day postoperative period were also associated with long-term use. Thus, lumbar fusion surgery infrequently eliminated long-term opioid use. Opioid-naive patients had a substantial risk of initiating long-term use. Patients should have realistic expectations regarding opioid use after lumbar fusion surgery.

Association of Prescription Drug Monitoring Program Use With Opioid Prescribing and Health Outcomes: A Comparison of Program Users and Nonusers.

Prescription drug monitoring programs (PDMPs) are a response to the prescription opioid epidemic, but their effects on prescribing and health outcomes remain unclear, with conflicting reports. We sought to determine if prescriber use of Oregon's PDMP led to fewer high-risk opioid prescriptions or overdose events. We conducted a retrospective cohort study from October 2011 through October 2014, using statewide PDMP data, hospitalization registry, and vital records. Early PDMP registrants (n = 927) were matched with clinicians who never registered during the study period, using baseline prescribing metrics in a propensity score. Generalized estimating equations were used to examine prescribing trends after PDMP registration, using 2-month intervals. We found a statewide decline in measures of per capita opioid prescribing. However, compared with nonregistrants, PDMP registrants did not subsequently have significantly fewer patients receiving high-dose prescriptions, overlapping opioid and benzodiazepine prescriptions, inappropriate prescriptions, prescriptions from multiple prescribers, or overdose events. At baseline, frequent PDMP users wrote fewer high-risk opioid prescriptions than infrequent users; this persisted during follow-up with few significant group differences in trend. Thus, although opioid prescribing declined statewide after implementing the PDMP, registrants did not show greater declines than nonregistrants. PERSPECTIVE: Factors other than PDMP use may have had greater influence on prescribing trends. Refinements in the PDMP program and related policies may be necessary to increase PDMP effects.

High-risk prescribing and opioid overdose: prospects for prescription drug monitoring program-based proactive alerts.

To develop a simple, valid model to identify patients at high risk of opioid overdose-related hospitalization and mortality, Oregon prescription drug monitoring program, Vital Records, and Hospital Discharge data were linked to estimate 2 logistic models; a first model that included a broad range of risk factors from the literature and a second simplified model. Receiver operating characteristic curves, sensitivity, and specificity of the models were analyzed. Variables retained in the final model were categories such as older than 35 years, number of prescribers, number of pharmacies, and prescriptions for long-acting opioids, benzodiazepines or sedatives, or carisoprodol. The ability of the model to discriminate between patients who did and did not overdose was reasonably good (area under the receiver operating characteristic curve = 0.82, Nagelkerke R = 0.11). The positive predictive value of the model was low. Computationally simple models can identify high-risk patients based on prescription history alone, but improvement of the predictive value of models may require information from outside the prescription drug monitoring program. Patient or prescription features that predict opioid overdose may differ from those that predict diversion.