Search AMIA.org

Acute kidney injury (AKI) is common in intensive care unit (ICU) patients and is associated with high mortality, prolonged ICU stays, and increased costs. Early prediction is crucial for timely intervention and improved outcomes. Various prediction models, including machine learning, deep learning, and dynamic prediction frameworks, have been developed, but their modeling approaches, data utilization, and clinical applicability require further investigation. This review comprehensively assesses the modeling methods, data utilization [...]

Conversational Health Agents (CHAs) are interactive systems providing healthcare services, such as assistance and diagnosis. Current CHAs, especially those utilizing Large Language Models (LLMs), primarily focus on conversation aspects. However, they offer limited agent capabilities, specifically needing more multistep problem-solving, personalized conversations, and multimodal data analysis. We aim to overcome these limitations.

To assess the effect of a digital scribe among pediatric providers on documentation time, cognitive load, burnout, and caregiver satisfaction.

Measles continues to pose a serious threat to global public health, fueled by declining vaccination rates, international travel, and persistent immunization gaps. Early outbreak detection and response remain hampered by fragmented surveillance systems, which often lack interoperability and limit data accessibility.

Falls are a leading cause of morbidity and mortality among older adults. Common methods for identifying fall-related ED visits within both claims and electronic health record datasets rely on diagnosis code-based definitions, which underestimate the true prevalence of falls. This study applies a natural language processing (NLP) algorithm to ED provider notes to identify patients presenting due to falls and compares the characteristics of NLP-identified cases to those identified through [...]

This study compares the performance of machine learning (ML) models and human experts in mapping unstructured nursing notes to the standardized Nursing Interventions Classification (NIC) system. The aim is to advance automated nursing documentation classification, facilitating cross-facility benchmarking of patient care and organizational outcomes.

Data governance, the policies, and procedures for managing data, is a critical factor for secondary use of clinical data for research.

While most health-care providers now use electronic health records (EHRs) to document clinical care, many still treat them as digital versions of paper records. As a result, documentation often remains unstructured, with free-text entries in progress notes. This limits the potential for secondary use and analysis, as machine-learning and data analysis algorithms are more effective with structured data.

The International Classification of Health Interventions (ICHI), currently being developed, seeks to span all sectors of the health system. Our objective was to determine the coverage of the ICHI for hearing interventions commonly delivered to adults with sensorineural hearing loss (SNHL).

Does a Tree-of-Thought prompt and reconsideration of Isabel Pro's differential improve ChatGPT-4's accuracy; does increasing expert panel size improve ChatGPT-4's accuracy; does ChatGPT-4 produce consistent outputs in sequential requests; what is the frequency of fabricated references?