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To provide a scoping review of papers on clinical natural language processing (NLP) shared tasks that use publicly available electronic health record data from a cohort of patients.

The predictive modeling literature for biomedical applications is dominated by biostatistical methods for survival analysis, and more recently some out of the box machine learning approaches. In this article, we show a presentation of a machine learning method appropriate for time-to-event modeling in the area of prostate cancer long-term disease progression. Using XGBoost adapted to long-term disease progression, we developed a predictive model for 118 788 patients with localized prostate cancer [...]

To understand and synthesize factors influencing user acceptance of digital interventions used for antimicrobial prescribing and monitoring in hospitals.

Surviving Sepsis guidelines recommend blood cultures before administration of intravenous (IV) antibiotics for patients with sepsis or moderate to high risk of bacteremia. Clinical decision support (CDS) that reminds emergency department (ED) providers to obtain blood cultures when ordering IV antibiotics may lead to improvements in this process measure.

Recent legislation ensuring patient access to their electronic health records represents a promising national commitment to patient empowerment. Access and interoperability rules seek to empower individuals as well as increase opportunities for data sharing by hospitals, apps, and other parties for research and innovation. However, there are trade-offs between data accessibility and oversight. Some third-party apps may not be covered by federal regulations, and receiving records directly from individuals may [...]

Early identification of infection improves outcomes, but developing models for early identification requires determining infection status with manual chart review, limiting sample size. Therefore, we aimed to compare semi-supervised and transfer learning algorithms with algorithms based solely on manual chart review for identifying infection in hospitalized patients.

We study the association between payment parity policies and telehealth utilization at community health centers (CHCs) before, during, and after the onset of the pandemic.