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Alert fatigue is a common issue with off-the-shelf clinical decision support. Most warnings for drug-drug interactions (DDIs) are overridden or ignored, likely because they lack relevance to the patient's clinical situation. Existing alerting systems for DDIs are often simplistic in nature or do not take the specific patient context into consideration, leading to overly sensitive alerts. The objective of this study is to develop, validate, and test DDI alert algorithms [...]

To propose a visual display-the probability threshold plot (PTP)-that transparently communicates a predictive models' measures of discriminative accuracy along the range of model-based predicted probabilities (Pt).

Fertility is becoming increasingly supported by consumer health technologies, especially mobile apps that support self-tracking activities. However, it is not clear whether the apps support the variety of goals and life events of those who menstruate, especially during transitions between them.

Concerns about patient privacy have limited access to COVID-19 datasets. Data synthesis is one approach for making such data broadly available to the research community in a privacy protective manner.

Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information.

To construct and publicly release a set of medical concept embeddings for codes following the ICD-10 coding standard which explicitly incorporate hierarchical information from medical codes into the embedding formulation.

Trauma quality improvement programs and registries improve care and outcomes for injured patients. Designated trauma centers calculate injury scores using dedicated trauma registrars; however, many injuries arrive at nontrauma centers, leaving a substantial amount of data uncaptured. We propose automated methods to identify severe chest injury using machine learning (ML) and natural language processing (NLP) methods from the electronic health record (EHR) for quality reporting.

Research & Exploratory Analysis Driven Time-data Visualization (read-tv) is an open source R Shiny application for visualizing irregularly and regularly spaced longitudinal data. read-tv provides unique filtering and changepoint analysis (CPA) features. The need for these analyses was motivated by research of surgical work-flow disruptions in operating room settings. Specifically, for the analysis of the causes and characteristics of periods of high disruption-rates, which are associated with adverse surgical outcomes.

We developed a digital scribe for automatic medical documentation by utilizing elements of patient-centered communication. Excessive time spent on medical documentation may contribute to physician burnout. Patient-centered communication may improve patient satisfaction, reduce malpractice rates, and decrease diagnostic testing expenses. We demonstrate that patient-centered communication may allow providers to simultaneously talk to patients and efficiently document relevant information.

Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data.