AIMC Topic: Electronic Health Records

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GPT-4 in Clinical Practice: Assessing Its Capability for Symptom Extraction from Cancer Patient Notes.

Studies in health technology and informatics
Accurate extraction of patient symptoms and signs from clinical notes is essential for effective diagnosis, treatment planning, and research. In this study, we evaluate the capability of GPT-4, specifically GPT-4o, in extracting symptoms and signs fr...

Evaluation of the Performance of a Large Language Model to Extract Signs and Symptoms from Clinical Notes.

Studies in health technology and informatics
Large language models (LLMs) have increasingly been used to extract critical information from unstructured clinical notes, which often include important details not captured in the structured sections of electronic health records (EHRs). This study a...

Data Governance in Healthcare AI: Navigating the EU AI Act's Requirements.

Studies in health technology and informatics
The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize patient care, diagnostics, and treatment planning. However, this integration also introduces significant challenges related to data governance, privacy...

Automated identification of fall-related injuries in unstructured clinical notes.

American journal of epidemiology
Fall-related injuries (FRIs) are a major cause of hospitalizations among older patients, but identifying them in unstructured clinical notes poses challenges for large-scale research. In this study, we developed and evaluated natural language process...

Machine learning to improve HIV screening using routine data in Kenya.

Journal of the International AIDS Society
INTRODUCTION: Optimal use of HIV testing resources accelerates progress towards ending HIV as a global threat. In Kenya, current testing practices yield a 2.8% positivity rate for new diagnoses reported through the national HIV electronic medical rec...

Artificial Intelligence Scribes Shape Health Care Delivery.

American family physician
Although most physicians are interested in the use of augmented or artificial intelligence (AI) in health care, only 38% are using AI in their practices.1 Initial results from AI integrated organizations show that AI scribe programs significantly dec...

Applications, challenges and future directions of artificial intelligence in cardio-oncology.

European journal of clinical investigation
BACKGROUND: The management of cardiotoxicity related to cancer therapies has emerged as a significant clinical challenge, prompting the rapid growth of cardio-oncology. As cancer treatments become more complex, there is an increasing need to enhance ...

Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop models that predict which patients with glaucoma will progress to require surgery, combining structured data from electronic health records (EHRs) and retinal fiber layer optical coherence tomography ...

Exploring the full potential of the electronic health record: the application of natural language processing for clinical practice.

European journal of cardiovascular nursing
The electronic health record (EHR) contains valuable patient data and offers opportunities to administer and analyse patients' individual needs longitudinally. However, most information in the EHR is currently stored in unstructured text notations. N...

Reducing readmissions in the safety net through AI and automation.

The American journal of managed care
OBJECTIVES: To implement a technology-based, systemwide readmission reduction initiative in a safety-net health system and evaluate clinical, care equity, and financial outcomes.