Journal of hospital medicine : an official publication of the Society of Hospital Medicine
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Diagnostic uncertainty, when unrecognized or poorly communicated, can result in diagnostic error. However, diagnostic uncertainty is challenging to study due to a lack of validated identification methods. This study aims to identify distinct linguistic patterns associated with diagnostic uncertainty in clinical documentation. ⋯ Expert labeling, natural language processing, and machine learning methods combined with human validation resulted in highly predictive models to detect diagnostic uncertainty in clinical documentation and represent a promising approach to detecting, studying, and ultimately mitigating diagnostic uncertainty in clinical practice.
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Identifying COVID-19 patients at the highest risk of poor outcomes is critical in emergency department (ED) presentation. Sepsis risk stratification scores can be calculated quickly for COVID-19 patients but have not been evaluated in a large cohort. ⋯ Sepsis severity scores at presentation have low discriminative power to predict outcomes in COVID-19 patients and are not reliable for clinical use. Severity scores should be developed using features that accurately predict poor outcomes among COVID-19 patients to develop more effective risk-based triage.
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Acute heart failure (AHF) exacerbations are a leading cause of hospitalization in the United States. Despite the frequency of AHF hospitalizations, there are inadequate data or practice guidelines on how quickly diuresis should be achieved. ⋯ Aggressive net fluid targets within the first 48 h are associated with effective relief of patient self-reported dyspnea and improved long-term outcomes without adversely affecting renal function.