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To enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms.

Telehealth or remote care has been widely leveraged to provide health care support and has achieved tremendous developments and positive results, including in low- and middle-income countries (LMICs). Social networking platform, as an easy-to-use tool, has provided users with simplified means to collect data outside of the traditional clinical environment. WeChat, one of the most popular social networking platforms in many countries, has been leveraged to conduct telehealth and hosted [...]

[This corrects the article DOI: 10.1093/jamiaopen/ooae029.].

[This retracts the article DOI: 10.1093/jamiaopen/ooad090.].

Diagnosing rare diseases is an arduous and challenging process in clinical settings, resulting in the late discovery of novel variants and referral loops. To help clinicians, we built IDeRare pipelines to accelerate phenotype-genotype analysis for patients with suspected rare diseases.

The generation of structured documents for clinical trials is a promising application of large language models (LLMs). We share opportunities, insights, and challenges from a competitive challenge that used LLMs for automating clinical trial documentation.

The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline.

Natural language processing (NLP) can enhance research on activities of daily living (ADL) by extracting structured information from unstructured electronic health records (EHRs) notes. This review aims to give insight into the state-of-the-art, usability, and performance of NLP systems to extract information on ADL from EHRs.

Numerous studies have identified information overload as a key issue for electronic health records (EHRs). This study describes the amount of text data across all notes available to emergency physicians in the EHR, trended over the time since EHR establishment.

To validate and demonstrate the clinical discovery utility of a novel patient-mediated, medical record collection and data extraction platform developed to improve access and utilization of real-world clinical data.