Objective Predictive analytics create opportunities to incorporate personalized risk estimates
into clinical decision support. Models must be well calibrated to support decision-making, yet …

Introduction Pharmacovigilance programs protect patient health and safety by identifying
adverse event signals through postmarketing surveillance of claims data and spontaneous …

Objective Clinical prediction models require updating as performance deteriorates over time.
We developed a testing procedure to select updating methods that minimizes overfitting …

Abstract Model calibration, critical to the success and safety of clinical prediction models,
deteriorates over time in response to the dynamic nature of clinical environments. To support …

Advanced regression and machine learning models can provide personalized risk
predictions to support clinical decision-making. We aimed to understand whether modeling …

Patient portal adoption has rapidly increased, and portal usage has been associated with
patients' sociodemographics, health literacy, and education. Research on patient portals has …

Background: Patient portal adoption has increased over the last two decades. Most research
about patient portals has focused on adult populations in the primary care and medical …

Background Use of secure messaging through patient portals has risen substantially in
recent years due to provider incentives and consumer demand. Secure messaging may …

Abstract Objective Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury
(AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping …

Patient portal research has focused on medical outpatient settings, with little known about
portal use during hospitalizations or by surgical patients. We measured portal adoption …