Expanding a Health Technology Solution to Address Therapist Challenges in Implementing Homework With Adult Clients: Mixed Methods Study.

Background: Homework is implemented with variable effectiveness in real-world therapy settings, indicating a need for innovative solutions to homework challenges. We developed Adhere.ly, a user-friendly, Health Insurance Portability and Accountability Act-compliant web-based platform to help therapists implement homework with youth clients and their caregivers.

Introduction and accuracy assessment of Nicolab's StrokeViewer in a developing stroke thrombectomy UK service. a service development/improvement project.

Aim: The aim of this study was to evaluate the implementation of artificial intelligence (AI) software in a quaternary stroke centre as well as assess the accuracy and efficacy of StrokeViewer software in large vessel occlusion detection and its potential impact on radiological workflow.

Materials And Methods: Data were collected during two separate three-month periods comparing the accuracy rate of StrokeViewer in detection of large vessel occlusion to that of a junior registrar. During the first three months, 37 cases were identified and during the second leg, 47.

Open-label, controlled, phase 2 clinical trial assessing the safety, efficacy, and pharmacokinetics of INM004 in pediatric patients with Shiga toxin-producing Escherichia coli-associated hemolytic uremic syndrome.

Background: Shiga toxin-producing Escherichia coli-associated hemolytic uremic syndrome (STEC-HUS) is a severe condition mainly affecting children. It is one of the leading causes of acute kidney injury in the pediatric population. There is no established therapy for this disease.

Towards Reducing Diagnostic Errors with Interpretable Risk Prediction.

Many diagnostic errors occur because clinicians cannot easily access relevant information in patient Electronic Health Records (EHRs). In this work we propose a method to use LLMs to identify pieces of evidence in patient EHR data that indicate increased or decreased risk of specific diagnoses; our ultimate aim is to increase access to evidence and reduce diagnostic errors. In particular, we propose a Neural Additive Model to make predictions backed by evidence with individualized risk estimates at time-points where clinicians are still uncertain, aiming to specifically mitigate delays in diagnosis and errors stemming from an incomplete differential.