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Integrating Nonpharmacological Options Into Perioperative Pain Care: A Cluster Randomized Clinical Trial

In brief

EHR pain-care intervention cuts postoperative opioid exposure by about 10%

In a cluster randomized trial covering 68,141 surgeries, an electronic health record intervention promoting non-drug pain care lowered postoperative opioid exposure by about 10%, or roughly 260 morphine milligram equivalents per patient. Pain interference and physical function were unchanged, as were anxiety, adverse events, and health care use; the findings suggest opioid exposure can fall without worse reported recovery, but the intervention did not improve function or pain.

Journal
JAMA network open (Q1)
Published
1 September 2026
Study design
Randomized controlled trial
Evidence level
Level 1, High (CEBM 1b)
Authors
Andrea L Cheville, Jeph Herrin, Sarah Minteer, Veronica Grzegorczyk, Jon Tilburt
PMID
42789267
DOI
10.1001/jamanetworkopen.2026.35900

Why clinicians should know about it

Abstract

IMPORTANCE: Postoperative pain is commonly treated with opioids, which may contribute to unnecessary opioid-related harms. Scalable approaches to integrate recommended nonpharmacologic pain care could reduce opioid exposure while improving or at least preserving functional recovery outcomes. OBJECTIVE: To evaluate whether an electronic health record (EHR)-embedded intervention promoting nonpharmacologic pain care improves postoperative pain interference and physical function while reducing opioid exposure. DESIGN, SETTING, AND PARTICIPANTS: This cluster randomized clinical trial was conducted from October 16, 2020, through April 30, 2024. Participants included patients undergoing surgery at 22 surgical practices in 6 surgical centers in Minnesota, Wisconsin, Florida, and Arizona. INTERVENTION: A multicomponent EHR-based intervention including a patient-facing Healing After Surgery educational guide, clinical decision support, and nonpharmacologic pain care support materials integrated into routine perioperative touchpoints and workflows. MAIN OUTCOMES AND MEASURES: Co-primary outcomes were Patient-Reported Outcomes Measurement Information System (PROMIS) Pain Interference and Physical Function T scores measured preoperatively and at 1, 2, and 3 months after surgery. Secondary outcomes included opioid prescribing and administration (morphine milligram equivalents [MMEs]), PROMIS Anxiety scores, and health care utilization for 3 months after surgery. Mixed-effects models were used to account for correlation of outcomes within surgery, patient, and cluster, as appropriate for each model. RESULTS: Among 68 141 included surgical procedures (40 892 male patients [60.0%]; mean [SD] age, 59.27 [16.26] years), 43 053 occurred during the intervention period, and 25 088 occurred during the usual care period. The intervention did not change PROMIS pain interference (adjusted mean difference, 0.09; 95% CI, -0.18 to 0.36; P = .51) or PROMIS physical function (adjusted mean difference, 0.15; 95% CI, -0.11 to 0.41; P = .27) in the intervention compared with control surgical procedures. However, total postoperative opioid exposure was lower during intervention periods (rate ratio, 0.90; 95% CI, 0.82 to 0.99; P = .03), corresponding to an approximately 10% relative reduction and an adjusted mean difference of approximately 260 MMEs per patient, with no changes in anxiety, adverse events, or health care utilization. This reduction was driven primarily by lower opioid administration during hospitalization (adjusted rate ratio, 0.94; 95% CI, 0.90 to 0.97; P = .001), with an adjusted mean difference of approximately 85 MMEs. CONCLUSIONS AND RELEVANCE: In this cluster randomized clinical trial of patients undergoing surgery, an EHR-embedded perioperative pain management intervention promoting nonpharmacologic pain care reduced opioid exposure without changes in patient-reported outcomes, suggesting potential for scalable EHR-based strategies to support safer postoperative pain management. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04570371.

Abstract as published, via PubMed.

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For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.