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This work aims to provide a specialized template for integrating diverse study data types with varying sharing restrictions specific to NIH HEAL Initiative chronic pain and substance use research.

Telemedicine for specialty medical care is evolving from a COVID-19 pandemic-era requirement to an option for patients and clinicians alike, requiring evidence to guide optimal use of virtual specialty care. Heterogeneity across medical specialties complicates this evidence generation. To address this gap in the literature, we present an approach to evaluation of telehealth across specialties with the potential to generate findings generalizable across specialties and health systems.

Congenital heart disease (CHD) patients with single ventricle physiology (SVP) have heterogeneous characteristics that challenge cohort classification. We aim to develop a phenotyping algorithm that accurately identifies SVP patients using electronic health record (EHR) data.

Diagnosis codes documented in electronic health records (EHR) are often relied upon to clinically phenotype patients for biomedical research. However, these diagnoses can be incomplete and inaccurate, leading to false negatives when searching for patients with phenotypes of interest. This study aims to determine whether PheMAP, a comprehensive knowledgebase integrating multiple clinical terminologies beyond diagnosis to capture phenotypes, can effectively identify patients lacking relevant EHR diagnosis codes.

Falls are a leading cause of morbidity and mortality among older adults. Common methods for identifying fall-related ED visits within both claims and electronic health record datasets rely on diagnosis code-based definitions, which underestimate the true prevalence of falls. This study applies a natural language processing (NLP) algorithm to ED provider notes to identify patients presenting due to falls and compares the characteristics of NLP-identified cases to those identified through [...]

While most health-care providers now use electronic health records (EHRs) to document clinical care, many still treat them as digital versions of paper records. As a result, documentation often remains unstructured, with free-text entries in progress notes. This limits the potential for secondary use and analysis, as machine-learning and data analysis algorithms are more effective with structured data.

To develop and apply a reproducible methodology for evaluating generative artificial intelligence (AI) powered systems in health care, addressing the gap between theoretical evaluation frameworks and practical implementation guidance.

Despite growing recognition of the critical role of nutrition in promoting population health, clinicians lack access to point-of-care resources to support culturally relevant nutrition services. This study aims to (1) evaluate Registered Dietitian Nutritionists' (RDN) likelihood of using a web-based tool to provide culturally- and medically tailored nutrition services, (2) identify needed or preferred features, and (3) examine concerns related to the development or implementation of a web-based tool.

The main goal is to develop an automated process for connecting Research Electronic Data Capture (REDCap) instances in a clinical trial network to allow for deidentified transfer of research surveys to cloud computing data commons for discovery.

This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias was assessed across 5 subgroups: cancer registry, race, Hispanic ethnicity, age at diagnosis, and socioeconomic status.