Technology, Machine Learning, and Bias in Emergency Care
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Item Work-Related Sleep Patterns in Orthopedic Surgeons(2025-01-07) Marquardt, Matthew; Kang, Ellison; Leahy, Nicholas; Emerson, Angela; Orr, Morgan; Hagen, Joshua; Quatman, CarmenObjective: To objectively capture sleep habits, quality, and digital biomarkers in orthopedic trauma surgeons. Methods: Ten orthopedic trauma surgeons (3 attendings, 7 residents) were enrolled in a longitudinal observational study lasting 14 days. Subjects wore a continuous glucose monitor and Oura ring (wearable sleep tracker) throughout the study period. Results: Sleep data was captured for 95 (67%) out of the total 140 nights and revealed that operative days were associated with statistically significant changes to sleep wake up time consistency but not bedtime consistency. While changes to heart rate variability (HRV) were not observed for operative vs nonoperative days, worse sleep consistency was associated with elevated fasting glucose. Conclusions: Wearable sleep trackers are a feasible method for tracking surgeon sleep habits and provide important insights into how operating schedules influence sleep consistency, HRV, and fasting glucose.Item Head Motion Analysis as an Objective Measure of Point-of-Care Ultrasound Procedural Guidance Competency(2025-01-07) Walsh, Carrie; Ebnali, Mahdi; Dias, Roger; Duggan, Nicole M.; Cash, Rebecca E.; Lee, David; Borges, Paulo; Plevek, Phillip; Walsh, Lindsay V.; Driver, Lachlan; Fischetti, Chanel; Goldsmith, AndrewBackground:. Currently, there is no standardization in ED-based-Ultrasound-guided-nerve-blocks (UGNB) training, credentialing, or procedural quality assurance nation-wide. This study aims to investigate objective measures of procedural competency for UGNB. Methods: Novice and expert users performed UGNB in a simulation-based setting. Participants were fitted with a head-motion tracking headband and their performance graded with a traditional OSCE. Head motion metrics were captured using both accelerometer and gyroscope sensors. Results: 11 novices and 7 experts were recruited. OSCE scores demonstrated a statistically significant difference between groups. Significant correlations were observed for accelerometer data in the z-axis and gyroscope data in the y-and z-axes, with positive correlation between accelerometer z-axis RMS values and OSCE scores(r=0.36), and gyroscope z-axis RMS values and OSCE scores(r=0.29). Conclusion: Objective measures of head motion in the z-axis demonstrated significant differences between novice and expert POCUS. Our data suggests that computer-based metrics may be reliable measures of procedural competency.Item Predicting the Risk of Asthma Attacks in New Zealand Using Machine Learning(2025-01-07) Jayamini, Widana Kankanamge; Mirza, Farhaan; Naeem, Asif; Tomlin, Andrew; Tibble, Holly; Beyene, Kebede; Chan, AmyExploring factors that increase the risk of asthma attacks is crucial for timely patient management. Machine learning (ML) techniques are increasingly used for risk prediction. This study aimed to identify risk factors for asthma attacks in New Zealand and evaluate ML algorithms' performance in predicting these risks. National health datasets from 355,113 patients aged 6 years and older with asthma were analyzed from 2008 to 2016. The outcome was the occurrence of an asthma attack within 3 months. Two ML models, XGBoost and Random Forest, and a statistical model, Logistic Regression (LR), were developed and performance compared. Key risk predictors included prior asthma attacks, length of winter exposure, and the number of ICS and SABA inhalers. XGB with random under-sampling performed slightly better (AUROC=0.76, F1 score=0.33). ML models performed slightly better than LR-RUS (AUROC=0.75, F1 score=0.32) in predicting asthma attacks. Future research should explore other ML and data imbalance handling techniques to enhance risk prediction.Item Designing and Evaluating an Agile Healthcare Performance Index for Guiding Quality Improvement in Healthcare Operations(2025-01-07) Alain, Gabriel; Quatman, Carmen; Quatman-Yates, CatherineTraditional metrics fall short in capturing the complexities of healthcare delivery, leading to misallocation of resources based on oversimplified metrics and fluctuating performance rankings. This study introduced the Agile Healthcare Performance Index (AHPI), designed to offer a comprehensive metric that integrates operational factors, aiming to enhance healthcare quality improvement initiatives through better resource allocation and operational decision-making. Employing synthetic data for 50,000 care episodes across four distinct hospital service lines, the construction of the AHPI involves weighting service lines by operational priorities, with a comparison to a static unweighted index to illustrate the benefits of this approach. Use of the AHPI demonstrated superior performance measurement within healthcare operations, showing adaptability and temporal sensitivity vital for healthcare planning and evaluation. The comparison with the unweighted index highlights the ability of the AHPI to reflect operational dynamics accurately.Item Introduction to the Minitrack on Technology, Machine Learning, and Bias in Emergency Care(2025-01-07) Duggan, Nicole M.; Hasdianda, Mohammad Adrian; Boyer, Edward; Jambaulikar, Guruprasad