Human-machine teaming is key to AI adoption: clinicians' experiences with a deployed machine learning system.

While a growing number of machine learning (ML) systems have been deployed in clinical settings with the promise of improving patient care, many have struggled to gain adoption and realize this promise. Based on a qualitative analysis of coded interviews with clinicians who use an ML-based system for sepsis, we found that, rather than viewing the system as a surrogate for their clinical judgment, clinicians perceived themselves as partnering with the technology. Our findings suggest that, even without a deep understanding of machine learning, clinicians can build trust with an ML system through experience, expert endorsement and validation, and systems designed to accommodate clinicians' autonomy and support them across their entire workflow.

Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis.

Early recognition and treatment of sepsis are linked to improved patient outcomes. Machine learning-based early warning systems may reduce the time to recognition, but few systems have undergone clinical evaluation. In this prospective, multi-site cohort study, we examined the association between patient outcomes and provider interaction with a deployed sepsis alert system called the Targeted Real-time Early Warning System (TREWS).

Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing.

Machine learning-based clinical decision support tools for sepsis create opportunities to identify at-risk patients and initiate treatments at early time points, which is critical for improving sepsis outcomes. In view of the increasing use of such systems, better understanding of how they are adopted and used by healthcare providers is needed. Here, we analyzed provider interactions with a sepsis early detection tool (Targeted Real-time Early Warning System), which was deployed at five hospitals over a 2-year period.

Impacts of an EMS Hospital Liaison Program on Ambulance Offload Times: A Preliminary Analysis.

Introduction: Ambulance patients who are unable to be quickly transferred to an emergency department (ED) bed represent a key contributing factor to ambulance offload delay (AOD). Emergency department crowding and associated AOD are exacerbated by multiple factors, including infectious disease outbreaks such as the coronavirus disease 2019 (COVID-19) pandemic. Initiatives to address AOD present an opportunity to streamline ambulance offload procedures while improving patient outcomes.

Nasopharyngeal carcinoma among the Bidayuh of Sarawak, Malaysia: History and risk factors.

Nasopharyngeal carcinoma (NPC) is a cancer of the epithelial cells lining the nasopharynx. The incidence of NPC has a distinct geographical distribution, mainly affecting the Chinese population of Southern China. In Malaysia, this cancer is exceptionally prevalent among males.