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Sepsis is a heterogeneous syndrome with high morbidity and mortality. Despite extensive clinical trials, therapeutic progress remains limited, in part due to the absence of actionable sepsis subtypes.This study aimed to evaluate the feasibility of using HL7 Fast Healthcare Interoperability Resources (FHIR) for prerandomization sepsis subtyping to support clinical trial enrichment across multiple health systems.Data from 765 encounters at two academic medical centers were analyzed. FHIR-based resources were extracted from [...]

Digital health tools, such as artificial intelligence scribes, offer significant potential to alleviate physician burnout and reduce administrative burdens associated with electronic health records. Despite their promise, Canadian health care organizations face challenges in establishing cohesive strategies for their effective implementation and evaluation.This paper explores actionable, organizational strategies to enhance physician wellness through digital health tools. It examines systemic barriers, promising practices, and infrastructure needs, culminating in five key recommendations [...]

End-of-life care (EoLC), such as advance care planning, advance directives, hospice, and palliative care consults, can improve patient quality of life and reduce costs, yet such interventions are underused. Machine learning-based prediction models show promise in identifying patients who may be candidates for EoLC based on increased risk of short-term (less than 1 year) mortality. Clinical decision support systems using these models can identify candidate patients at a time during [...]

Falls among adults over 60 are a global health concern, including Australia.This study aimed to investigate temporospatial fall alert patterns-across time and location-detected by ambient fall detection sensors in three Australian aged care settings, to inform fall prevention strategies.A mixed-methods approach was used to analyze fall alert patterns and fall risks. Ambient fall detection sensors collected data from three care settings (residential aged care facilities [RACFs], retirement villages [RVs], and [...]

The effective operation of magnetic resonance imaging (MRI) systems relies on physical interactions with complex imaging environments, equipment, and user interfaces (UIs). However, there is limited empirical data evaluating how physical interactions with MRI equipment and accessories, workspace configuration, MRI UI design, and technical proficiency influence clinical workflow.In this study, a cross-sectional survey was conducted among MRI end-users, across public and private health facilities (n = 13), using a structured questionnaire to [...]

As the use of artificial intelligence (AI) in healthcare is rapidly expanding, there is also growing recognition of the need for ongoing monitoring of AI after implementation, called algorithmovigilance. Yet, there remain few systems that support systematic monitoring and governance of AI used across a health system. In this study, we identify end-user needs for a novel AI monitoring system-the Vanderbilt Algorithmovigilance Monitoring and Operations System (VAMOS)-using human-centered design (HCD).

Phone surveys are crucial for collecting health data but are expensive, time-consuming, and difficult to scale. To overcome these limitations, we propose a survey collection approach powered by conversational Large Language Models (LLMs).

Describing the development of a visual dashboard leveraging available tools for efficient recruitment for patient centered clinical trials in resource constrained settings.

Clinical trial data is still predominantly manually entered by site staff into Electronic Data Capture (EDC) systems. This process of abstracting and manually transcribing patient data is time-consuming, inefficient and error prone. Use of Electronic Health Record to Electronic Data Capture (EHR-To-EDC) technologies that digitize this process would improve these inefficiencies.

To engage end-users to develop and evaluate an algorithm to convert electronic adherence monitoring device (EAMD) output into the adherence data required for analyses.