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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?

To describe challenges and solutions for calculating longitudinal daily opioid dose in morphine milligram equivalents from electronic health record prescriptions for a clinical trial of voluntary opioid reduction in patients with chronic non-cancer pain.

To develop and apply a reproducible methodology for evaluating generative artificial intelligence (AI) powered systems in health care, addressing the gap between theoretical evaluation frameworks and practical implementation guidance.

This study aimed to systematically evaluate and compare the diagnostic performance of leading large language models (LLMs) in common and complex clinical scenarios, assessing their potential for enhancing clinical reasoning and diagnostic accuracy in authentic clinical decision-making processes.

To develop CigStopper, a real-time, automated medical billing prototype designed to identify eligible tobacco cessation care codes, thereby reducing administrative workload while improving billing accuracy.