Health Policy

Development and accuracy of a novel machine learning model to detect toddlers’ physical activity and sedentary time using accelerometers: Little Movers Activity Analysis

Objective: (1) develop and test a novel, open-source, supervised machine learning model to detect toddlers’ physical activity (PA) and sedentary time (SED); (2) compare this novel machine learning model to existing cut-point methods to analyse toddlers’ PA (independent sample cross-validation of existing methods). Methods: We recruited 111 healthy toddlers to attend two semi-structured lab visits ...

Variation and Standardization in Prior Authorization Requirements

Prior authorization (PA) rules are neither regulated nor standardized. To quantify the variation in PA rules of four US health insurers and examine the potential for standardization. Services and medications were identified using their Healthcare Common Procedure Coding System (HCPCS) codes. We manually examined PA rules to identify a simple set of categories applicable across insurers. We categor...

Reinforcement learning-based control of epidemics on networks of communities and correctional facilities

Correctional facilities can act as amplifiers of infectious disease outbreaks. Small community outbreaks can cause larger prison outbreaks, which can ...

Health economic simulation modeling of an AI-enabled clinical decision support system for coronary revascularization

Coronary revascularization decision-making can be challenging. While artificial intelligence (AI) models have been developed to support this decision-...

Optimizing the Clinical Application of Rheumatology Guidelines Using Large Language Models: A Retrieval-Augmented Generation Framework Integrating EULAR and ACR Recommendations

Timely access to current rheumatology guidelines at the point of care is challenging. We aimed to develop and evaluate the first Retrieval-Augmented G...

Machine learning prediction for early-stage melanoma outcomes: recurrence-free survival, disease-specific survival, and overall survival

This study compared machine-learning models for predicting recurrence-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) ...

Cardiac Magnetic Resonance Imaging in the German National Cohort: Automated Segmentation of Short-Axis Cine Images and Post-Processing Quality Control

To develop a segmentation and quality control pipeline for short-axis cardiac magnetic resonance (CMR) cine images from the prospective, multi-center ...

Kolmogorov-Arnold Network for Atherosclerotic Cardiovascular Disease Risk Prediction

Assessing the risk of future atherosclerotic cardiovascular disease (ASCVD) is crucial in clinical practice, yet it continues to pose significant chal...

Applications of Artificial Intelligence in clinical decision-making and technical support in Oncology: A Scoping Review protocol

The management of cancer care generates vast amounts of data, collected in the clinical registry; however, the interpretation of these unstandardized ...

Leveraging AI and Transfer Learning to Enhance Outcome Prediction for Out-of-Hospital Cardiac Arrest in Diverse Settings: Insights from the Pan-Asian Resuscitation Outcomes Study

Access to trustworthy artificial intelligence (AI) models for clinical applications like emergency care is unevenly distributed globally due to health...

Validation of an instrumented shoe insole framework for analyzing spatiotemporal gait metrics in healthy and neurodegenerative populations

Many neurological conditions negatively affect a person’s walking quality, which is a vital aspect of their quality of life. Gait quality, through the...

Datasheet for the IDHea Primary Eye Care Dataset: A Real-World Ocular Imaging Resource for Research

Real-world ocular imaging datasets are essential for advancing research in artificial intelligence (AI), autonomous disease screening, and clinical de...

Building an Analytical Framework for Tobacco-Related Misinformation on Social Media: An Exploratory Analysis with Generative AI Assistance

The propagation of tobacco-related misinformation significantly impacts public health, particularly affecting people with less access to reliable info...

Large Language Models for Sentiment Analysis in Healthcare: A Systematic Review Protocol

Large language models (LLMs) have emerged as powerful tools for sentiment analysis in healthcare, offering potential advantages in capturing contextua...

Self-Logical Consistency Assessment of Large Language Models for Patient Feedback Classification : Algorithm Development and Validation Study

Patient satisfaction feedback is crucial for hospital service quality, but manual reviews are not possible due to their time-consumption, and traditio...

AI Implementation in U.S. Healthcare and Its Association With Elder Mortality and Quality of Care

Hospitals are increasingly adopting artificial intelligence (AI) tools in clinical care. However, their overall impact on the health of older adults r...

Human-Centered Design of an Artificial Intelligence (AI) Monitoring System: The Vanderbilt Algorithmovigilance Monitoring and Operations System (VAMOS)

As the use of AI in healthcare is rapidly expanding, there is also growing recognition of the need for ongoing monitoring of AI after implementation, ...

Deep Learning-Based Risk Prediction Model for Major Adverse Cardiovascular Events in Long-Term Breast Cancer Survivors

Clinical practice guidelines recommend cardiovascular toxicity risk restratification including evaluation of new cardiovascular risk factors and cardi...

Modeling the Impact of Social Determinants on Breast Cancer Screening: A Data-Driven Approach

This study addresses the critical implementation science challenge of operationalizing social determinants of health (SDoH) in clinical practice. We d...

Completeness and Quality of Neurology Referral Letters Generated by a Large Language Model for Standardized Scenarios

Large Language Models (LLMs) offer promising applications in healthcare, including drafting referral letters. However, access to LLMs specifically des...