Search AMIA.org

Integrating electronic health record (EHR) data with other resources is essential in rare disease research due to low disease prevalence. Such integration is dependent on the alignment of ontologies used for data annotation. The international classification of diseases (ICD) is used to annotate clinical diagnoses, while the human phenotype ontology (HPO) is used to annotate phenotypes. Although these ontologies overlap in the biomedical entities they describe, the extent to which [...]

We sought to characterize indicators of digital literacy among persons testing for COVID-19 and community health workers (CHWs) providing testing via a digital platform in low-income, majority-Latino communities in California.

We propose and validate a domain knowledge-driven classification model for diagnosing post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHRs) data.

Over the last decade, treatment of patients with advanced non-small cell lung cancer (NSCLC) has become dependent on tissue and/or blood biomarkers to guide treatment decisions. Timely access to comprehensive biomarker and tumor signature information is crucial for diagnostic testing. With the rapid development and implementation of complex biomarker testing, comprehensive molecular profiling with in-depth analysis of DNA, RNA, and proteins can be easily performed. Initial data from the MedStar [...]

To assess the current state of sex, sexual orientation, and gender identity (SSOGI) data collection options in US electronic health record (EHR) platforms.

Digital interventions are increasingly in demand to address mental health concerns, with significant potential to reach populations that disproportionately face barriers to accessing mental health care. Challenges with user engagement, however, persist. The goal of this study was to develop user personas to inform the development of a digital mental health intervention (DMHI) for a perinatal population.

Health systems are increasingly accountable for patients and require accurate electronic health record (EHR) vital status. We recently demonstrated that 19% of seriously ill primary care patients in one system were not marked dead in the EHR and 80% of these decedents had an encounter or appointment outstanding after death. Herein we describe the mechanism of identifying decedents whose death is not captured at the level of the EHR, characterize [...]

To improve the performance of a social risk score (a predictive risk model) using electronic health record (EHR) structured and unstructured data.

To determine whether the addition of a primary aldosteronism (PA) predictive model to a secondary hypertension decision support tool increases screening for PA in a primary care setting.

Extracting the sample size from randomized controlled trials (RCTs) remains a challenge to developing better search functionalities or automating systematic reviews. Most current approaches rely on the sample size being explicitly mentioned in the abstract. The objective of this study was, therefore, to develop and validate additional approaches.