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We describe an automated transcription system that addresses many documentation problems and fits within scheduled clinical hours.

Present an artificial intelligence-enabled pipeline for estimating the prevalence of depression and general anxiety among pregnant women using texts from their social media posts. Use said pipeline to analyze mental health trends on subreddits frequented by pregnant women and report on interesting insights that could be helpful for policy-makers, clinicians, etc.

Remote monitoring of women experiencing hypertensive disorders of pregnancy (HDP) can provide timely life-saving data, particularly if these data are integrated into existing patient and clinical workflows. This pilot intervention of a smartphone application (app) for postpartum monitoring of hypertensive disorders integrates patient-contributed data into electronic health records (EHRs) to support monitoring and clinical decision-making. Results from the evaluation of the pilot highlight the resources needed when implementing the app [...]

Developing accurate phenotype definitions is critical in obtaining reliable and reproducible background rates in safety research. This study aims to illustrate the differences in background incidence rates by comparing definitions for a given outcome.

To investigate how missing data in the patient problem list may impact racial disparities in the predictive performance of a machine learning (ML) model for emergency department (ED) triage.

To assess the impact of potential errors in natural language processing (NLP) on the results of epidemiologic studies.

Worldwide, there is an estimated 40.3 million victims trapped in modern day slavery, including 24.9 million in forced labor and 15.4 million in forced marriage. A majority of labor and sex trafficking survivors report at least one healthcare encounter during their victimization. An approach to an informatics technology solution for identifying trafficked persons in real time, in the hospital / emergency department settings is the primary focus of this paper.

This article provides insight into our process and considerations for selecting patient-reported outcome measures (PROMs) designed for self-reporting symptoms and quality-of-life among breast cancer (BCA) patients undergoing oral anticancer agent treatment via a patient-facing technology (PFT) platform.

To describe the reusable transformation process of electronic health records (EHR), claims, and prescriptions data into Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM), together with challenges faced and solutions implemented.

To understand whether hospitals had electronic access to information needed to treat COVID-19 patients and identify factors contributing to differences in information availability.