Journal of opioid management · 2025
Pharmaceutical Automated Reporting: An opioid stewardship tool.
СтатьяНазвание и аннотация: Перевод в очереди
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Turner Dylan, Gottselig Paul, Sommer Leland, Dumont Kelsey, Berry Warren, Phillips Casey
Аннотация
OBJECTIVE: To develop and implement a customized clinical decision support system (CDSS) in an under-resourced health region aimed at promoting appropriate and safe opioid prescribing.
DESIGN: The Pharmaceutical Automated Reporting (PAR) tool integrates inpatient prescription data from BDM Pharmacy (version 10) and categorizes patient information using predefined logic. It operates with Python (version 3.10) and Microsoft Excel®, functioning as decision trees. Nine risk factors (absence of naloxone prescription with an opioid prescription, naloxone administration, high-frequency opioid dosing, multiple opioids prescribed, concurrent benzodiazepine and opioid coprescribed, over 7 days of intravenous route opioid use, morphine equivalent dose received over or equal to 90, possible opioid agonist therapy, possible alcohol withdrawal therapy) are assessed through a decision matrix to classify patients for opioid-related risk.
RESULTS: Over 7 months, the PAR tool detected one opioid-related risk factor in 98.9 percent (n = 10,450) of patients prescribed an opioid and multiple risk factors in 62.4 percent (n = 6,590). The tool identified areas where data-driven interventions by the Opioid Stewardship Program could promote appropriate prescribing practices and will be used to track and promote stewardship interventions, inform policy change, and evaluate the impact on quality indicators.
CONCLUSION: Small, resource-scarce health systems can use open-source programming methodologies to create an internal CDSS to assist in addressing opioid-related risk factors within their healthcare facilities.
DESIGN: The Pharmaceutical Automated Reporting (PAR) tool integrates inpatient prescription data from BDM Pharmacy (version 10) and categorizes patient information using predefined logic. It operates with Python (version 3.10) and Microsoft Excel®, functioning as decision trees. Nine risk factors (absence of naloxone prescription with an opioid prescription, naloxone administration, high-frequency opioid dosing, multiple opioids prescribed, concurrent benzodiazepine and opioid coprescribed, over 7 days of intravenous route opioid use, morphine equivalent dose received over or equal to 90, possible opioid agonist therapy, possible alcohol withdrawal therapy) are assessed through a decision matrix to classify patients for opioid-related risk.
RESULTS: Over 7 months, the PAR tool detected one opioid-related risk factor in 98.9 percent (n = 10,450) of patients prescribed an opioid and multiple risk factors in 62.4 percent (n = 6,590). The tool identified areas where data-driven interventions by the Opioid Stewardship Program could promote appropriate prescribing practices and will be used to track and promote stewardship interventions, inform policy change, and evaluate the impact on quality indicators.
CONCLUSION: Small, resource-scarce health systems can use open-source programming methodologies to create an internal CDSS to assist in addressing opioid-related risk factors within their healthcare facilities.