OBJECTIVE: Event capture in clinical trials is resource-intensive, and electronic medical records (EMRs) offer a potential solution. This study develops algorithms for EMR-based death and hospitalization capture and compares them with traditional event capture methods. MATERIALS AND METHODS: We compared the effectiveness of EMR-based event capture and site-captured events adjudicated by a clinical endpoint committee in the multi-center INfluenza Vaccine to Effectively Stop cardio Thoracic Events and Decompensated heart failure (INVESTED) trial for participants from the Veterans Affairs healthcare system. Varying time windows around event dates were used to optimize events matching. The algorithms were externally validated for heart failure hospitalizations in the Medical Information Mart for Intensive Care (MIMIC)-IV database. RESULTS: We observed 100% sensitivity for death events with a 1-day window. Sensitivity for cardiovascular, heart failure, pulmonary, and nonspecific cardiopulmonary hospitalizations using discharge diagnosis codes varied between 75% and 95%. Including Centers for Medicare & Medicaid Services data improved sensitivity with no meaningful decrease in specificity. The MIMIC-IV analysis showed 82% sensitivity and 99% specificity for heart failure hospitalizations. DISCUSSION: EMR-based method accurately identifies all-cause mortality and demonstrates high accuracy for cardiopulmonary hospitalizations. This study underscores the importance of optimal time windows, data completeness, and domain variability in EMR systems. CONCLUSION: EMR-based methods are effective strategies for capturing death and hospitalizations in clinical trials; however, their effectiveness may be influenced by the complexity of events and domain variability across different EMR systems. Nonetheless, EMR-based methods can serve as a valuable complement to traditional methods.

Medical systems in general, and patient treatment decisions and outcomes in particular, can be affected by bias based on gender and other demographic elements. As language models are increasingly applied to medicine, there is a growing interest in building algorithmic fairness into processes impacting patient care. Much of the work addressing this question has focused on biases encoded in language models—statistical estimates of the relationships between concepts derived from distant reading of corpora. Building on this work, we investigate how differences in gender-specific word frequency distributions and language models interact with regards to bias. We identify and remove gendered language from two clinical-note datasets and describe a new debiasing procedure using BERT-based gender classifiers. We show minimal degradation in health condition classification tasks for low- to medium-levels of dataset bias removal via data augmentation. Finally, we compare the bias semantically encoded in the language models with the bias empirically observed in health records. This work outlines an interpretable approach for using data augmentation to identify and reduce biases in natural language processing pipelines.

Journal of the American Medical Informatics Association, 27(11), 2020, 1716–1720, doi:10.1093/jamia/ocaa162 In the originally published version of this article, there was an error in author Stephen J Miller’s name. This has now been corrected.

OBJECTIVE: Reducing risk of coronavirus disease 2019 (COVID-19) infection among healthcare personnel requires a robust occupational health response involving multiple disciplines. We describe a flexible informatics solution to enable such coordination, and we make it available as open-source software. MATERIALS AND METHODS: We developed a stand-alone application that integrates data from several sources, including electronic health record data and data captured outside the electronic health record. RESULTS: The application facilitates workflows from different hospital departments, including Occupational Health and Infection Control, and has been used extensively. As of June 2020, 4629 employees and 7768 patients and have been added for tracking by the application, and the application has been accessed over 46 000 times. DISCUSSION: Data captured by the application provides both a historical and real-time view into the operational impact of COVID-19 within the hospital, enabling aggregate and patient-level reporting to support identification of new cases, contact tracing, outbreak investigations, and employee workforce management. CONCLUSIONS: We have developed an open-source application that facilitates communication and workflow across multiple disciplines to manage hospital employees impacted by the COVID-19 pandemic.