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BACKGROUND: Ambient clinical documentation tools are increasingly used to reduce administrative burden and improve provider experience. However, evidence describing their effects at scale across large, multisite health systems remains limited.

Patients with substance misuse are at high risk for clinical deterioration, and pre-hospital encounters constitute important risk factors. We sought to incorporate these risk factors into a novel prediction model using linked electronic health record, emergency medical services (EMSs), and claims data.

To evaluate natural language processing (NLP) and machine learning (ML) approaches for identifying social needs in electronic health record (EHR) notes, using balanced versus real-world imbalanced datasets.

To examine how algorithmic fairness is measured, operationalized, and reported in machine learning (ML) models designed to predict or support secondary prevention of cardiovascular disease (CVD) outcomes including progression, recurrence, readmission, and post-index mortality in racialized populations.

Investigate how patients at a Federally-Qualified Health Center (FQHC) perform with, and perceive, complex telehealth tasks. Determine which complexity dimensions most affect patients' performance and perceptions, and compare this to researcher-evaluators' complexity walkthrough results.

Substance use disorder (SUD) care continues to be hindered by persistent gaps in health information exchange (HIE). This study examined people, organizational, regulatory, and technical barriers to SUD data sharing from the provider perspective and identified key opportunities to improve care through health record interoperability.

To evaluate the comparative effectiveness of ambient documentation tools (ADTs) with distinct architectures (Tablet-Based Virtual Human-Assisted Ambient [Tool A], EHR-Integrated Ambient [Tool B], and Standalone Ambient [Tool C]) on provider efficiency, documentation burden, and productivity in primary care.

Machine learning (ML) models are increasingly being developed to support healthcare delivery. However, concerns remain about their potential to perpetuate existing biases rooted in the data used to develop them. We aim to assess the impact of using race in predicting hospital admission probabilities for patients visiting the emergency department (ED).

Disease profiles and access to care differ between rural and urban populations. The aim of this study is to examine the association of rurality with electronic health record-derived disease profiles and heart failure risk.

Childhood obesity and respiratory tract infections (RTIs) are 2 major global public health issues that frequently co-occur and are closely interrelated. Early detection of children with prior RTIs who are at high obesity risk is crucial for targeted interventions. This study integrates interpretable machine learning (ML) models and a deep learning network to develop an obesity risk prediction model in a large pediatric cohort.