Aman Mahajan

BACKGROUND: The United States is in the midst of an opioid epidemic, and opioid use disorder often begins with a prescription for acute pain. The perioperative period represents an important opportunity to prevent chronic opioid use, and recently there has been a paradigm shift toward implementation of enhanced recovery after surgery (ERAS) protocols that promote opioid-free and multimodal analgesia. The objective of this study was to assess the impact of an ERAS intervention for colorectal surgery on discharge opioid prescribing practices. METHODS: We conducted a historical-prospective quality improvement study of an ERAS protocol implemented for patients undergoing colorectal surgery with a focus on the opioid-free and multimodal analgesia components of the pathway. We compared patients undergoing colorectal surgery 1 year before implementation (June 15, 2015, to June 14, 2016) and 1 year after implementation (June 15, 2016, to June 14, 2017). RESULTS: Before the ERAS intervention, opioids at discharge were not significantly increasing (1% per month; 95% confidence interval [CI], -1% to 3%; P = .199). Immediately after the ERAS intervention, opioid prescriptions were not significantly lower (13%; 95% CI, -30% to 3%; P = .110). After the intervention, the rate of opioid prescriptions at discharge did not decrease significantly 1% (95% CI, -3% to 1%) compared to the pre-period rate (P = .399). Subgroup analysis showed that in patients with a combination of low discharge pain scores, no preoperative opioid use, and low morphine milligram equivalents consumption before discharge, the rate of discharge opioid prescription was 72% (95% CI, 61%-83%). CONCLUSIONS: This study is the first to report discharge opioid prescribing practices in an ERAS setting. Although an ERAS intervention for colorectal surgery led to an increase in opioid-free anesthesia and multimodal analgesia, we did not observe an impact on discharge opioid prescribing practices. The majority of patients were discharged with an opioid prescription, including those with a combination of low discharge pain scores, no preoperative opioid use, and low morphine milligram equivalents consumption before discharge. This observation in the setting of an ERAS pathway that promotes multimodal analgesia suggests that our findings are very likely to also be observed in non-ERAS settings and offers an opportunity to modify opioid prescribing practices on discharge after surgery. For opioid-free anesthesia and multimodal analgesia to influence the opioid epidemic, the dose and quantity of the opioids prescribed should be modified based on the information gathered by in-hospital pain scores and opioid use as well as pain history before admission.

OBJECT: To date, health care providers have devoted significant efforts to improve performance regarding patient safety and quality of care. To address the lagging involvement of health care providers in the cost component of the value equation, UCLA Health piloted the implementation of time-driven activity-based costing (TDABC). Here, the authors describe the implementation experiment, share lessons learned across the care continuum, and report how TDABC has actively engaged health care providers in costing activities and care redesign. METHODS: After the selection of pilots in neurosurgery and urology and the creation of the TDABC team, multidisciplinary process mapping sessions, capacity-cost calculations, and model integration were coordinated and offered to engage care providers at each phase. RESULTS: Reviewing the maps for the entire episode of care, varying types of personnel involved in the delivery of care were noted: 63 for the neurosurgery pilot and 61 for the urology pilot. The average cost capacities for care coordinators, nurses, residents, and faculty were $0.70 (range $0.63-$0.75), $1.55 (range $1.28-$2.04), $0.58 (range $0.56-$0.62), and $3.54 (range $2.29-$4.52), across both pilots. After calculating the costs for material, equipment, and space, the TDABC model enabled the linking of a specific step of the care cycle (who performed the step and its duration) and its associated costs. Both pilots identified important opportunities to redesign care delivery in a costconscious fashion. CONCLUSIONS: The experimentation and implementation phases of the TDABC model have succeeded in engaging health care providers in process assessment and costing activities. The TDABC model proved to be a catalyzing agent for cost-conscious care redesign.

Importance: Identifying patients at high risk of adverse outcomes prior to surgery may allow for interventions associated with improved postoperative outcomes; however, few tools exist for automated prediction. Objective: To evaluate the accuracy of an automated machine-learning model in the identification of patients at high risk of adverse outcomes from surgery using only data in the electronic health record. Design, Setting, and Participants: This prognostic study was conducted among 1 477 561 patients undergoing surgery at 20 community and tertiary care hospitals in the University of Pittsburgh Medical Center (UPMC) health network. The study included 3 phases: (1) building and validating a model on a retrospective population, (2) testing model accuracy on a retrospective population, and (3) validating the model prospectively in clinical care. A gradient-boosted decision tree machine learning method was used for developing a preoperative surgical risk prediction tool. The Shapley additive explanations method was used for model interpretability and further validation. Accuracy was compared between the UPMC model and National Surgical Quality Improvement Program (NSQIP) surgical risk calculator for predicting mortality. Data were analyzed from September through December 2021. Exposure: Undergoing any type of surgical procedure. Main Outcomes and Measures: Postoperative mortality and major adverse cardiac and cerebrovascular events (MACCEs) at 30 days were evaluated. Results: Among 1 477 561 patients included in model development (806 148 females [54.5%; mean [SD] age, 56.8 [17.9] years), 1 016 966 patient encounters were used for training and 254 242 separate encounters were used for testing the model. After deployment in clinical use, another 206 353 patients were prospectively evaluated; an additional 902 patients were selected for comparing the accuracy of the UPMC model and NSQIP tool for predicting mortality. The area under the receiver operating characteristic curve (AUROC) for mortality was 0.972 (95% CI, 0.971-0.973) for the training set and 0.946 (95% CI, 0.943-0.948) for the test set. The AUROC for MACCE and mortality was 0.923 (95% CI, 0.922-0.924) on the training and 0.899 (95% CI, 0.896-0.902) on the test set. In prospective evaluation, the AUROC for mortality was 0.956 (95% CI, 0.953-0.959), sensitivity was 2148 of 2517 patients (85.3%), specificity was 186 286 of 203 836 patients (91.4%), and negative predictive value was 186 286 of 186 655 patients (99.8%). The model outperformed the NSQIP tool as measured by AUROC (0.945 [95% CI, 0.914-0.977] vs 0.897 [95% CI, 0.854-0.941], for a difference of 0.048), specificity (0.87 [95% CI, 0.83-0.89] vs 0.68 [95% CI, 0.65-0.69]), and accuracy (0.85 [95% CI, 0.82-0.87] vs 0.69 [95% CI, 0.66, 0.72]). Conclusions and Relevance: This study found that an automated machine learning model was accurate in identifying patients undergoing surgery who were at high risk of adverse outcomes using only preoperative variables within the electronic health record, with superior performance compared with the NSQIP calculator. These findings suggest that using this model to identify patients at increased risk of adverse outcomes prior to surgery may allow for individualized perioperative care, which may be associated with improved outcomes.