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Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that disproportionately affects women and racial/ethnic minority groups. Predicting disease flares is essential for improving patient outcomes, yet few studies integrate both clinical and social determinants of health (SDoH). We therefore developed FLAME (FLAre Machine learning prediction of SLE), a machine learning pipeline that uses electronic health records (EHRs) and contextual-level SDoH to predict 3-month flare risk, emphasizing explainability and fairness.

Our objective was to determine how social vulnerabilities, broadband access, and rurality relate to telemedicine use across the United States through large-scale analysis of real-world telemedicine data.

To develop and evaluate machine learning (ML) models for predicting length of stay (LOS) in elective spine surgery, with a focus on the benefits of temporal modeling and model interpretability.

Recent advancements of Artificial Intelligence (AI) are rapidly transforming clinical research. While this technology offers exciting opportunities, it amplifies existing concerns regarding the need for transparent methodology that fosters patient engagement, and introduces new challenges. PCORI's Improving Methods portfolio has invested in methodological research to enhance rigor and transparency via patient-centered approaches in AI.

To compare different machine learning models of loss to follow-up among people with HIV (PWH).

Federated analysis is a method that allows data analysis to be performed on similar datasets without exchanging any data, thus facilitating international research collaboration while adhering to strict privacy laws. This study aimed to evaluate the feasibility of using federated analysis to benchmark mortality in 2 critical care quality registry databases converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), describing challenges to and recommendations for performing [...]

To investigate clinicians' attitudes towards current automated interpretation of ECG and novel AI technologies and their perception of computer-assisted interpretation.

Mobile and ubiquitous devices enable health data collection "in a free-living environment" to support applications such as remote patient monitoring and adaptive digital interventions using machine learning (ML). Despite their potential, significant data collection challenges persist, including issues related to user compliance with reporting data, passive data consistency, and authorization. This scoping review identifies and analyzes these challenges, focusing on barriers to effective data collection.

Artificial intelligence (AI) scribes may reduce the documentation burden and improve clinician experience through generative AI automatically producing provider note sections from recordings of patient-provider encounters.We aimed to examine the impact of AI scribes on clinician experience, clinician efficiency, and business efficiency measures among pediatric subspecialty physicians.We randomized pediatric subspecialty providers with ≥0.5 clinical full-time equivalent and stable electronic health record (EHR) log metrics to use Microsoft/Nuance Digital Ambient eXperience [...]