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 The method of documentation during a clinical encounter may affect the patient-physician relationship.

To present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machine learning and natural language processing methods to incorporate rich electronic health record data.

 The overall goal of this work is to create a patient-reported outcome (PRO) and decision support system to help postpartum patients determine when to seek care for concerning symptoms. In this case study, we assessed differences in perspectives for application design needs based on race, ethnicity, and preferred language.

 Though public health is an information-intense profession, there is a paucity of workforce with Public Health Informatics and Technology (PHIT) skills, which was evident during the coronavirus disease 2019 (COVID-19) pandemic. This need is addressed through the PHIT workforce program (2021-2025) by the Office of the National Coordinator for training and to increase racial and ethnic diversity in the PHIT workforce. The objective is to share details on the Training [...]

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

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 [...]

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.

Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts.