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 During and since the coronavirus disease 2019 (COVID-19) pandemic, communities have needed to cope with several conditions that cause similar upper respiratory symptoms but are managed differently. We describe community reactions to a self-management toolkit for patients with upper respiratory symptoms to inform mobile e-health app development. The toolkit is based on the "4R" (Right Information, Right Care, Right Patient, Right Time) care planning and management model.

 Dental informatics (DI) is an emerging discipline. Although the accreditation agency governing dental education programs asserts the importance of informatics as foundational knowledge, no well-defined DI courses currently exist within the standard predoctoral dental curriculum. There is a nationwide lack of DI academic programs. This training gap is due to a lack of qualified dental informaticians to impart knowledge on DI.

To understand barriers to obtaining and using interoperable information at US hospitals.

 Health Information Technology is increasingly being used to help providers connect patients with community resources to meet health-related social needs (e.g., food, housing, transportation). Research is needed to design efficient, simple, and engaging interfaces during a sensitive process that involves multiple stakeholders. Research is also needed to understand the roles, expectations, barriers, and facilitators these different stakeholders (i.e., patients, providers, and community-based organizations [CBOs]) face during this process.

 Duplicate patient records can increase costs and medical errors. We assessed the association between demographic factors, comorbidities, health care usage, and duplicate electronic health records.

We proposed adopting billing models for secure messaging (SM) telehealth services that move beyond time-based metrics, focusing on the complexity and clinical expertise involved in patient care.

This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) examine the quality and limitations of these models.

SNOMED CT provides a standardized terminology for clinical concepts, allowing cohort queries over heterogeneous clinical data including Electronic Health Records (EHRs). While it is intuitive that missing and inaccurate subtype (or is-a) relations in SNOMED CT reduce the recall and precision of cohort queries, the extent of these impacts has not been formally assessed. This study fills this gap by developing quantitative metrics to measure these impacts and performing statistical [...]

Evaluate popular explanation methods using heatmap visualizations to explain the predictions of deep neural networks for electrocardiogram (ECG) analysis and provide recommendations for selection of explanations methods.