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We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown.

Automated electronic result notifications can alert health care providers of important clinical results. In contrast to historical notification systems, which were predominantly focused on critical laboratory abnormalities and often not very customizable, modern electronic health records provide capabilities for subscription-based electronic notification. This capability has not been well studied.

After 25 years of service to the American Medical Informatics Association (AMIA), Ms Karen Greenwood, the Executive Vice President and Chief Operating Officer, is leaving the organization. In this perspective, we reflect on her accomplishments and her effect on the organization and the field of informatics nationally and globally. We also express our appreciation and gratitude for Ms Greenwood's role at AMIA.

Population health management (PHM) is an important approach to promote wellness and deliver health care to targeted individuals who meet criteria for preventive measures or treatment. A critical component for any PHM program is a data analytics platform that can target those eligible individuals.

Electronic health records have incomplete capture of patient outcomes. We consider the case when observability is differential across a predictor. Including such a predictor (sensitive variable) can lead to algorithmic bias, potentially exacerbating health inequities.

To identify the risk factors home healthcare (HHC) clinicians associate with patient deterioration and understand how clinicians respond to and document these risk factors.

In response to the coronavirus disease-19 (COVID-19) pandemic, numerous institutions published COVID-19 dashboards for reporting epidemiological statistics at the county, state, or national level. However, statistics for smaller cities were often not reported, requiring these areas to develop their own data processing pipelines. For under-resourced departments of health, the development of these pipelines was challenging, leading them to rely on nonspecific and often delayed infection statistics during the pandemic. To [...]