The International journal on drug policy · 2022
How do patient, pharmacist and medication characteristics and prescription drug monitoring program alerts influence pharmacists' decisions to dispense opioids? A randomised controlled factorial experiment.
Рандомизированное исследованиеСтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Picco Louisa, Sanfilippo Paul, Xia Ting, Lam Tina, Nielsen Suzanne
Аннотация
BACKGROUND: Prescription drug monitoring programs (PDMP) are electronic databases that track the prescribing and dispensing of high-risk medicines such as opioids. They have the ability to provide clinicians with alerts, which identify medication-related risks, and are used to help inform decisions to supply. This study aimed to determine to what extent patient, pharmacist, and medication related characteristics and PDMP alerts influence decisions to dispense opioids and take other action, using a randomised controlled factorial design.
METHODS: Pharmacists completed an online factorial experiment, comprising six randomly generated vignettes, describing a hypothetical pharmacy patient. Pharmacists ranked the likelihood of dispensing an opioid prescription and indicated other actions, if any, they would make. Mixed-effects linear and logistical models were used to examine the association between the vignette (patient, medication and alerts), and pharmacist characteristics and the likelihood to dispense and take other actions.
RESULTS: 241 pharmacists were included in the analysis (n = 1353 vignettes). The PDMP alert for high dose and multiple prescriber episodes were significant predicators of reduced likelihood to dispense, with a respective 2.73- and 4.1-unit decrease in likelihood to dispense (p < 0.001). Alerts had the strongest association with other actions such as contacting the prescriber, talking to the patient and recommending naloxone, though patient and medication characteristics including age, opioid dose, benzodiazepine use and co-morbidity were also associated with increased odds of engaging in some actions.
CONCLUSION: PDMP alerts were the most significant predictor of reduced likelihood to dispense and were associated with the greatest odds of taking other actions. Well-established risk factors such as high dose and high-risk drug combinations, in the absence of PDMP alerts, were associated with some actions, though to a lesser degree than PDMP alerts. These findings have significant policy implications and suggest PDMP alerts are a greater driver of decisions to dispense opioids and take other actions, compared with other known clinical risk factors.
METHODS: Pharmacists completed an online factorial experiment, comprising six randomly generated vignettes, describing a hypothetical pharmacy patient. Pharmacists ranked the likelihood of dispensing an opioid prescription and indicated other actions, if any, they would make. Mixed-effects linear and logistical models were used to examine the association between the vignette (patient, medication and alerts), and pharmacist characteristics and the likelihood to dispense and take other actions.
RESULTS: 241 pharmacists were included in the analysis (n = 1353 vignettes). The PDMP alert for high dose and multiple prescriber episodes were significant predicators of reduced likelihood to dispense, with a respective 2.73- and 4.1-unit decrease in likelihood to dispense (p < 0.001). Alerts had the strongest association with other actions such as contacting the prescriber, talking to the patient and recommending naloxone, though patient and medication characteristics including age, opioid dose, benzodiazepine use and co-morbidity were also associated with increased odds of engaging in some actions.
CONCLUSION: PDMP alerts were the most significant predictor of reduced likelihood to dispense and were associated with the greatest odds of taking other actions. Well-established risk factors such as high dose and high-risk drug combinations, in the absence of PDMP alerts, were associated with some actions, though to a lesser degree than PDMP alerts. These findings have significant policy implications and suggest PDMP alerts are a greater driver of decisions to dispense opioids and take other actions, compared with other known clinical risk factors.