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Changes in postoperative opioid prescribing across three diverse healthcare systems, 2010-2020.

Coquet J, Zammit A, Hajouji OE, Humphreys K, Asch SM, Osborne TF, Curtin CM, Hernandez-Boussard T. Changes in postoperative opioid prescribing across three diverse healthcare systems, 2010-2020. Frontiers in digital health. 2022 Dec 6; 4:995497.

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OBJECTIVE: The opioid crisis brought scrutiny to opioid prescribing. Understanding how opioid prescribing patterns and corresponding patient outcomes changed during the epidemic is essential for future targeted policies. Many studies attempt to model trends in opioid prescriptions therefore understanding the temporal shift in opioid prescribing patterns across populations is necessary. This study characterized postoperative opioid prescribing patterns across different populations, 2010-2020. DATA SOURCE: Administrative data from Veteran Health Administration (VHA), six Medicaid state programs and an Academic Medical Center (AMC). DATA EXTRACTION: Surgeries were identified using the Clinical Classifications Software. STUDY DESIGN: Trends in average daily discharge Morphine Milligram Equivalent (MME), postoperative pain and subsequent opioid prescription were compared using regression and likelihood ratio test statistics. PRINCIPAL FINDINGS: The cohorts included 595,106 patients, with populations that varied considerably in demographics. Over the study period, MME decreased significantly at VHA (37.5-30.1; ? = 0.002) and Medicaid (41.6-31.3; ? = 0.019), and increased at AMC (36.9-41.7; ? < 0.001). Persistent opioid users decreased after 2015 in VHA (? < 0.001) and Medicaid (? = 0.002) and increase at the AMC (? = 0.003), although a low rate was maintained. Average postoperative pain scores remained constant over the study period. CONCLUSIONS: VHA and Medicaid programs decreased opioid prescribing over the past decade, with differing response times and rates. In 2020, these systems achieved comparable opioid prescribing patterns and outcomes despite having very different populations. Acknowledging and incorporating these temporal distribution shifts into data learning models is essential for robust and generalizable models.

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