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The growing use of machine learning (ML) in healthcare raises concerns about how data biases affect real-world model performance. While existing frameworks evaluate algorithmic fairness, they often overlook the impact of bias on generalizability and clinical utility, which are critical for safe deployment. Building on prior methods, this study extends bias analysis to include clinical utility, addressing a key gap between fairness evaluation and decision-making.

While timely interventions can improve medication adherence, it is challenging to identify which patients are at risk of nonadherence at point-of-care. We aim to develop and validate flexible machine learning (ML) models to predict a continuous measure of adherence to guideline-directed medication therapies (GDMTs) for heart failure (HF).

To evaluate the efficacy of digital twins developed using a large language model (LLaMA-3), fine-tuned with Low-Rank Adapters (LoRA) on intensive care units (ICU) physician notes, and to determine whether specialty-specific training enhances treatment recommendation accuracy compared to other ICU specialties or zero-shot baselines.

Demonstrate the ability to encapsulate clinical-grade genomics data normalization algorithms within a FHIR Genomics reference implementation.

To develop a natural language processing (NLP) pipeline for unstructured electronic health record (EHR) data to identify symptoms and functional impacts associated with Long COVID in children.

Quantify the effect of ambient artificial intelligence (AI) scribe technology on work experience, clinical operations, and patient experience in pediatric primary care.We conducted a 12-week study of 39 clinicians within a large pediatric primary care network. Clinician experience was measured using a custom survey instrument which included a combination of discrete and free-text responses. Qualitative analysis of free-text responses provided additional context and identified key facilitators and barriers to optimal [...]

Clinical decision support systems (CDSS) are central to modern health care, but their effectiveness is compromised during system downtimes, which affect 96% of health care organizations. During these failures, clinicians lose access to critical decision-making tools like order sets, increasing the risk of medical errors. Traditional downtime solutions, such as paper-based protocols, are often impractical and difficult to maintain.This study introduces and evaluates Offsite Repository for Clinical Assets (ORCA), a [...]

Rare B-rapidly accelerated fibrosarcoma gene (BRAF) mutations in advanced melanoma, and other malignancies, represent a significant clinical challenge due to sparse evidence on the efficiency of targeted therapy. Conventional genomic databases do not integrate detailed outcome data on treatments for patients with these mutations, requiring innovative informatics approaches.For the use case of patients with rare BRAF-mutated melanoma, we developed a "Treatment Outcome Tool" as a web-based database on rare cancers [...]

To implementation an automated multi-institutional pipeline that delivers breast-cancer risk integrated with polygenic risk scores, monogenic variants, family history, and clinical factors, emphasizing operational challenges and their solutions.

Our objective was to identify barriers to implementing a custom clinical decision support (CDS) alert to randomize individuals in a pragmatic study, specifically those with a positive antinuclear antibody (ANA) test.We integrated a validated logistic regression model into the electronic health record to predict the risk of developing autoimmune disease for individuals with a positive ANA (titer ≥ 1:80). A custom CDS alert was created to randomize eligible individuals into [...]