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Context.— Pathology on-call experiences help prepare trainees for successful transition from residency to independent practice, and as such are an integral component of training. However, few data exist on anatomic pathology resident on-call workload and experience. Objective.— To obtain an overall picture of the anatomic pathology on-call experience to inform and improve resident education. Design.— Retrospective and prospective review of daily anatomic pathology on-call summaries from July 2016 to June 2020. Results.— During the first 2 years of the study (ie, retrospective portion), only 19% of on-call summaries (138 of 730) were available for review. After interventions, the on-call summary submission rate jumped to 98% (716 of 731). After-hours calls were most frequent on weekdays from 5 to 8 pm. The most frequent requests were for frozen sections (55%; 619 of 1125 calls), inquiries regarding disposition of fresh placentas (13%; 148 of 1125 calls), and inquiries regarding disposition of various other specimens (6%; 68 of 1125 calls). After-hours frozen section requests were most frequent for gynecologic and head and neck specimens. Notably, a significant number of after-hours calls were recurring preanalytic issues amenable to system-level improvements. We were able to eliminate the most common of these recurring preanalytic calls with stepwise interventions. Conclusions.— To our knowledge, this is the first study analyzing the anatomic pathology resident on-call experience. In addition to obtaining a broad overview of the residents' clinical exposure on this service, we identified and resolved issues critical to optimal patient care (eg, inconsistent “patient hand-off”) and improved the resident on-call experience (eg, fewer preanalytic calls increased resident time for other clinical, educational, or wellness activities).

Context.— Delayed recognition of acute kidney injury (AKI) results in poor outcomes in military and civilian burn-trauma care. Poor predictive ability of urine output (UOP) and creatinine contribute to the delayed recognition of AKI. Objective.— To determine the impact of point-of-care (POC) AKI biomarker enhanced by machine learning (ML) algorithms in burn-injured and trauma patients. Design.— We conducted a 2-phased study to develop and validate a novel POC device for measuring neutrophil gelatinase-associated lipocalin (NGAL) and creatinine from blood samples. In phase I, 40 remnant plasma samples were used to evaluate the analytic performance of the POC device. Next, phase II enrolled 125 adults with either burns that were 20% or greater of total body surface area or nonburn trauma with suspicion of AKI for clinical validation. We applied an automated ML approach to develop models predicting AKI, using a combination of NGAL, creatinine, and/or UOP as features. Results.— Point-of-care NGAL (mean [SD] bias: 9.8 [38.5] ng/mL, P = .10) and creatinine results (mean [SD] bias: 0.28 [0.30] mg/dL, P = .18) were comparable to the reference method. NGAL was an independent predictor of AKI (odds ratio, 1.6; 95% CI, 0.08–5.20; P = .01). The optimal ML model achieved an accuracy, sensitivity, and specificity of 96%, 92.3%, and 97.7%, respectively, with NGAL, creatinine, and UOP as features. Area under the receiver operator curve was 0.96. Conclusions.— Point-of-care NGAL testing is feasible and produces results comparable to reference methods. Machine learning enhanced the predictive performance of AKI biomarkers including NGAL and was superior to the current techniques.