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Severe infection can lead to organ dysfunction and sepsis. Identifying subphenotypes of infected patients is essential for personalized management. It is unknown how different time series clustering algorithms compare in identifying these subphenotypes.

The 21st Century Cures Act Final Rule's information blocking provisions, which prohibited practices likely to interfere with, prevent, or materially discourage access, exchange, or use of electronic health information (EHI), began to apply to a limited set of data elements in April 2021 and expanded to all EHI in October 2022. We sought to describe hospital leaders' perceptions of the prevalence of practices that may constitute information blocking, by actor [...]

To adapt and validate an algorithm to ascertain transgender and gender diverse (TGD) patients within electronic health record (EHR) data.

The annual American College of Medical Informatics (ACMI) symposium focused discussion on the national public health information systems (PHIS) infrastructure to support public health goals. The objective of this article is to present the strengths, weaknesses, threats, and opportunities (SWOT) identified by public health and informatics leaders in attendance.

This qualitative study aimed to understand patient and researcher perspectives regarding consent and data-sharing preferences for research and a patient-centered system to manage consent and data-sharing preferences.

A prior randomized controlled trial (RCT) showed no significant difference in wrong-patient errors between clinicians assigned to a restricted electronic health record (EHR) configuration (limiting to 1 record open at a time) versus an unrestricted EHR configuration (allowing up to 4 records open concurrently). However, it is unknown whether an unrestricted EHR configuration is more efficient. This substudy of the RCT compared clinician efficiency between EHR configurations using objective measures [...]

Nonexercise algorithms are cost-effective methods to estimate cardiorespiratory fitness (CRF), but the existing models have limitations in generalizability and predictive power. This study aims to improve the nonexercise algorithms using machine learning (ML) methods and data from US national population surveys.

This study aimed to assess Uganda's readiness for implementing a national Point-of-Care (PoC) electronic clinical data capture platform that can function in near real-time.