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To develop a large-language-model (LLM)-centric workflow flow extraction and migration of clinician-documented colonoscopy recall recommendations from unstructured reports and letters during an enterprise-wide electronic health record (EHR) transition.

Electronic Health Record (EHR) data are increasingly used in cancer research, yet the fidelity of this data when exchanged between systems remains poorly quantified. This study investigated the agreement in essential biomarker data after they are passed from the EHR into the cancer registry and Fast Healthcare Interoperability Resources (FHIR) extracts.

In the absence of standardized measures, researchers have struggled to define meaningful use of telehealth (care received via video or telephone). We evaluated a novel Telehealth Engagement Measure (TEM) that quantifies an individual's telehealth use, including telephone (TEM-t) and video (TEM-v), as a percentage of total care received.

To use bibliometric methods to deeply analyze the application of WeChat in China's mHealth field, outline its research panorama, and clarify research frontiers.

Patient portals support health management, yet enrollment remains low. This study evaluated whether timely email nudges increase patient portal enrollment compared with usual system portal invitations, whether message framing influences enrollment, and whether patients nudged to enroll go on to engage with the portal at similar rates as those who enroll organically.

Reuse of shared datasets is often limited by incomplete or inconsistent documentation, hindering interpretation and secondary analysis. We developed DataAtlas, an open-source system for automated generation of data dictionaries from tabular datasets.

To analyze social needs documentation across structured electronic sources in Johns Hopkins Health System, describing practices, patient characteristics, and utility for care, research, and population health.

To describe the Research Electronic Data Capture (REDCap) Consortium's initial implementation of generative artificial intelligence (AI) within the REDCap platform using a minimum viable product (MVP) strategy.

To compare the performance of task-specific generative AI, with general-purpose large language models (LLMs) in generating more readable lay abstracts and summaries (LASs) of Oncology research.

To conduct a systematic review of studies assessing the accuracy of International Classification of Diseases (ICD) and Diagnostic and Statistical Manual of Mental Disorders (DSM) codes in administrative data for ascertaining health conditions when compared to a reference standard.