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Complications are often under-reported at surgical morbidity and mortality (MM) conferences due to the sole reliance on voluntary case submission. While most institutions have databases used for targeted initiatives in quality improvement, these are not routinely used for MM. We aimed to increase case capture for MM conferences by developing a novel system that augments the existing case submission system with cases representing complications from quality improvement databases and the electronic health record (EHR).We developed and implemented a novel system for increasing the capture rate of complications for MM conferences by developing custom software that combines data from the following sources: an existing voluntary case submission system for MM, local quality databases-National Surgical Quality Improvement Program and Vizient, and an EHR-based case capture tool. We evaluated this system on a retrospective cohort of all postoperative complications at a single center in a 32-mo period and in a prospective cohort over a 4-mo period after system implementation.In the retrospective cohort, we identified 433 complications among all data sources. Inclusion of the new system introduced 280 new potential cases for MM review over the 32-mo period. After implementation, the system provided 31% of cases presented at MM conference that would have otherwise been omitted.A novel system that includes complications identified in the EHR and quality improvement databases increased the case capture volume for surgical MM conference, which provides an objective case referral system that can identify complementary quality improvement opportunities.

Despite adoption of the emergency general surgery (EGS) service by hospitals nationally, quality improvement (QI) and research for this patient population are challenging because of the lack of population-specific registries. Past efforts have been limited by difficulties in identifying EGS patients within institutions and labor-intensive approaches to data capture. Thus, we created an automated electronic health record (EHR)-linked registry for EGS.We built a registry within the Epic EHR at University of California San Diego for the EGS service. Existing EHR labels that identified patients seen by the EGS team were used to create our automated inclusion rules. Registry validation was performed using a retrospective cohort of EGS patients in a 30-month period and a 1-month prospective cohort. We created quality metrics that are updated and reported back to clinical teams in real time and obtained aggregate data to identify QI and research opportunities. A key metric tracked is clinic schedule rate, as we care that discontinuity postdischarge for the EGS population remains a challenge.Our registry captured 1,992 patient encounters with 1,717 unique patients in the 30-month period. It had a false-positive EGS detection rate of 1.8%. In our 1-month prospective cohort, it had a false-positive EGS detection rate of 0% and sensitivity of 85%. For quality metrics analysis, we found that EGS patients who were seen as consults had significantly lower clinic schedule rates on discharge compared with those who were admitted to the EGS service (85% vs. 60.7%, p0.001).An EHR-linked EGS registry can reliably conduct capture data automatically and support QI and research.Prognostic and epidemiological, level III.

IntroductionPatient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive models. While an EHR ML model has been developed to predict clinical deterioration, it has yet to be validated for use in trauma. We hypothesized that the Epic Deterioration Index (EDI) would predict mortality and unplanned intensive care unit (ICU) admission in trauma patients.MethodsA retrospective analysis of a trauma registry was used to identify patients admitted to a level 1 trauma center for >24 hours from October 2019 to July 2020. We evaluated the performance of the EDI, which is constructed from 125 objective patient measures within the EHR, in predicting mortality and unplanned ICU admissions. We performed a 5 to 1 match on age because it is a major component of EDI, then examined the area under the receiver operating characteristic curve (AUROC), and benchmarked it against Injury Severity Score (ISS) and new injury severity score (NISS).ResultsThe study cohort consisted of 1,325 patients admitted with a mean age of 52.5 years and 91% following blunt injury. The in-hospital mortality rate was 2%, and unplanned ICU admission rate was 2.6%. In predicting mortality, the maximum EDI within 24 hours of admission had an AUROC of 0.98 compared with 0.89 of ISS and 0.91 of NISS. For unplanned ICU admission, the EDI slope within 24 hours of ICU admission had a modest performance with an AUROC of 0.66.ConclusionEpic Deterioration Index appears to perform strongly in predicting in-patient mortality similarly to ISS and NISS. In addition, it can be used to predict unplanned ICU admissions. This study helps validate the use of this real-time EHR ML-based tool, suggesting that EDI should be incorporated into the daily care of trauma patients.Level of evidencePrognostic, level III.

Adherence to opioid prescribing protocols after operations remains challenging despite published guidelines. Integration of these guidelines with the electronic health record could potentially improve their adoption. We hypothesize that implementing an electronic health record order set containing prepopulated tablet quantities tailored to surgical procedures based on published guidelines will decrease postoperative opioid prescription.We conducted a 12-month prepost intervention study on adult patients who underwent appendectomy, cholecystectomy, inguinal or umbilical hernia repair, thyroidectomy, or parathyroidectomy at a single institution. An electronic health record order set was developed with prepopulated opioid tablet quantities reflecting the upper limit of published recommendations. The primary endpoint was change in morphine milligram equivalent prescribed postintervention and was analyzed using linear regression adjusting for age, race, procedure, and prescriber training level. Secondary endpoints were emergency department visits for pain-related issues and opioid refill rates.We identified 524 patients (mean age = 53, 51% male) in our baseline cohort and 433 patients (mean age = 52, 58% male) in our postintervention group. The mean morphine milligram equivalent prescribed was 62.6 and 50.4 for the preintervention and postintervention cohorts, respectively (P = .049). Thyroidectomies and parathyroidectomies achieved the largest decrease after intervention, which decreased to 42.6 morphine milligram equivalent from 79.7 morphine milligram equivalent preintervention (P.001). Refill rate was 1.6% postintervention compared to 3.1% preintervention (P = .20), and emergency department visit for pain control rate was 0.2% post intervention and 2.5% preintervention (P = .005).An electronic health record tailored order set based on prescription guidelines is a safe, effective, and scalable intervention for decreasing opioid prescriptions after operations.

BACKGROUND: The extent to which obesity and genetics determine post-operative complications is incompletely understood. METHODS: We performed a retrospective study using two population cohorts with electronic health record (EHR) data. The first included 736,726 adults with body mass index (BMI) recorded between 1990-2017 at Vanderbilt University Medical Center. The second cohort consisted of 65,174 individuals from 12 institutions contributing EHR and genome-wide genotyping data to the Electronic Medical Records & Genomics (eMERGE) Network. Pairwise logistic regression analyses were used to measure the association of BMI categories with postoperative complications derived from International Classification of Disease-9 codes, including postoperative infection, incisional hernia, and intestinal obstruction. A genetic risk score (GRS) was constructed from 97 obesity-risk single nucleotide polymorphisms for a Mendelian randomization study to determine the association of genetic risk for obesity on postoperative complications. Logistic regression analyses were adjusted for sex, age, site, and race/principal components. RESULTS: Individuals with overweight or obese BMI (≥25 kg/m(2)) had increased risk for incisional hernia (Odds ratio [OR] 1.7-5.5, p