Clinical Decision Support to Reduce Opioid Prescribing at Discharge for Inpatients Undergoing Surgery (LESS Study): An Interrupted Time Series Analysis

BACKGROUND: Unnecessary opioid prescribing following surgery is wasteful and expands the reservoir for non-medical use, thereby contributing to preventable morbidity and mortality. We used an interrupted time series design to evaluate whether implementing a system-wide clinical decision support (CDS) intervention reduced opioid prescribing at discharge. METHODS: We included adult surgical patients hospitalized for at least 24 hours who had not received any opioids in the 24 hours prior to discharge, as prescriptions in this cohort are more likely to represent unnecessary opioid prescribing. The pre- and post-intervention two-year periods were 2/13/21-2/12/23 and 2/13/23-2/12/25, respectively. Our primary outcome was discharge oxycodone in morphine milligram equivalents (MME). Secondary outcomes included whether any opioids were prescribed, and if so, how much. Segmented regression models adjusted for confounders were used to assess the immediate and trend-level effects of the intervention. RESULTS: Analyzed data included 10,422 pre- and 11,795 post-intervention discharges. Total oxycodone prescribed per discharge was 27.4 MME pre-intervention and 16.5 MME post-intervention. Oxycodone MME prescribed at discharge was significantly lower post-intervention, with a ratio of geometric means of 0.83 (95% CI: 0.76, 0.90; 1-tailed superiority P <0.001) for the level change but no difference in slopes (P=0.919). For secondary outcomes, 21% of discharges were prescribed any oxycodone pre-intervention versus 18% post-intervention, with a relative risk of 0.87 (95% CI: 0.79, 0.96) assessing the level change. For the discharges with a prescription, median MME [Q1, Q3] was lower after intervention than before (75 [38, 112] vs 112 [75, 150]), with a ratio of geometric means of 0.70 (95% CI: 0.65, 0.75). CONCLUSIONS: An automated real-time clinical decision support tool resulted in clinically significant reductions in oxycodone prescribed at discharge. Incorporation of similarly simple decision support tools in electronic health record systems may significantly reduce unnecessary opioid prescriptions at scale and better align with guideline-concordant care.

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Journal
Anesthesiology
Published
2026-05-21
DOI
https://doi.org/10.1097/aln.0000000000006174
Primary Topic
Opioid Use Disorder Treatment
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article
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article

Clinical Decision Support to Reduce Opioid Prescribing at Discharge for Inpatients Undergoing Surgery (LESS Study): An Interrupted Time Series Analysis

Craig Reha, Megan L. Rolfzen, Mark C. Bicket, Kristin Daniel et al.
Anesthesiology
Opioid Use Disorder Treatment
article

Clinical Decision Support to Reduce Opioid Prescribing at Discharge for Inpatients Undergoing Surgery (LESS Study): An Interrupted Time Series Analysis

Craig Reha, Megan L. Rolfzen, Mark C. Bicket, Kristin Daniel, Edward J. Mascha, Jennifer F. Waljee, Stephan Frangakis, Grace E. Majeres, Matt Muellner, K Bartels, Amy S Bohnert, Chad M Brummett, Karan Shah
article en

Abstract

BACKGROUND: Unnecessary opioid prescribing following surgery is wasteful and expands the reservoir for non-medical use, thereby contributing to preventable morbidity and mortality. We used an interrupted time series design to evaluate whether implementing a system-wide clinical decision support (CDS) intervention reduced opioid prescribing at discharge. METHODS: We included adult surgical patients hospitalized for at least 24 hours who had not received any opioids in the 24 hours prior to discharge, as prescriptions in this cohort are more likely to represent unnecessary opioid prescribing. The pre- and post-intervention two-year periods were 2/13/21-2/12/23 and 2/13/23-2/12/25, respectively. Our primary outcome was discharge oxycodone in morphine milligram equivalents (MME). Secondary outcomes included whether any opioids were prescribed, and if so, how much. Segmented regression models adjusted for confounders were used to assess the immediate and trend-level effects of the intervention. RESULTS: Analyzed data included 10,422 pre- and 11,795 post-intervention discharges. Total oxycodone prescribed per discharge was 27.4 MME pre-intervention and 16.5 MME post-intervention. Oxycodone MME prescribed at discharge was significantly lower post-intervention, with a ratio of geometric means of 0.83 (95% CI: 0.76, 0.90; 1-tailed superiority P <0.001) for the level change but no difference in slopes (P=0.919). For secondary outcomes, 21% of discharges were prescribed any oxycodone pre-intervention versus 18% post-intervention, with a relative risk of 0.87 (95% CI: 0.79, 0.96) assessing the level change. For the discharges with a prescription, median MME [Q1, Q3] was lower after intervention than before (75 [38, 112] vs 112 [75, 150]), with a ratio of geometric means of 0.70 (95% CI: 0.65, 0.75). CONCLUSIONS: An automated real-time clinical decision support tool resulted in clinically significant reductions in oxycodone prescribed at discharge. Incorporation of similarly simple decision support tools in electronic health record systems may significantly reduce unnecessary opioid prescriptions at scale and better align with guideline-concordant care.

Anesthesiology
Nebraska Medical Center (US), Cleveland Clinic (US), University of Michigan (US), VA Center for Clinical Management Research (US), Indiana University Bloomington (US), University of Nebraska Medical Center (US)
Good health and well-being
Openalex Percentile: Top 5%
Opioid Use Disorder Treatment
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