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In response to the coronavirus disease 2019 (COVID-19) pandemic, the Veterans Health Administration (VA) rapidly expanded virtual care (defined as care delivered by video and phone), raising concerns about technology access disparities (ie, the digital divide). Virtual care was somewhat established in primary care and mental health care prepandemic, but video telehealth implementation was new for most subspecialties, including cardiology. We sought to identify patient characteristics of virtual and video-based [...]

[This corrects the article DOI: 10.1093/jamiaopen/ooac036.].

Mapping internal, locally used lab test codes to standardized logical observation identifiers names and codes (LOINC) terminology has become an essential step in harmonizing electronic health record (EHR) data across different institutions. However, most existing LOINC code mappers are based on text-mining technology and do not provide robust multi-language support.

Hypertension has long been recognized as one of the most important predisposing factors for cardiovascular diseases and mortality. In recent years, machine learning methods have shown potential in diagnostic and predictive approaches in chronic diseases. Electronic health records (EHRs) have emerged as a reliable source of longitudinal data. The aim of this study is to predict the onset of hypertension using modern deep learning (DL) architectures, specifically long short-term memory [...]

Health systems in several countries have integrated information and communication technologies into their operations. Electronic medical records (EMRs) are at the core of patient care. The working of these EMRs requires their acceptance and use by medical and paramedical personnel. The objective of this study was to empirically evaluate the intention of health professionals to use these EMRs.

We introduce and review the concept of a study-a-thon as a catalyst for open science in medicine, utilizing harmonized real world, observation health data, tools, skills, and methods to conduct network studies, generating insights for those wishing to use study-a-thons for future research.

The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected racial/ethnic minorities in the United States, who are underrepresented in clinical trials. We assessed the feasibility of using the University of Pennsylvania Health System electronic health record patient portal to diversify the pool of participants in COVID-19 vaccine clinical trials. The patient portal was used to send invitations to eligible individuals living in zip codes with high rates of racial/ethnic minorities [...]

Despite smartphone ownership becoming ubiquitous, it is unclear whether and where disparities persist in experience using health apps. In 2 diverse samples of adults with type 2 diabetes collected 2017-2018 and 2020-2021, we examined adjusted disparities in smartphone ownership and health app use by age, gender, race, education, annual household income, health insurance status, health literacy, and hemoglobin A1c. In the earlier sample (N = 422), 87% owned a smartphone and 49% [...]

The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions.

Healthcare data such as clinical notes are primarily recorded in an unstructured manner. If adequately translated into structured data, they can be utilized for health economics and set the groundwork for better individualized patient care. To structure clinical notes, deep-learning methods, particularly transformer-based models like Bidirectional Encoder Representations from Transformers (BERT), have recently received much attention. Currently, biomedical applications are primarily focused on the English language. While general-purpose German-language models [...]