Search AMIA.org

Electronic health records (EHRs) contain valuable patient information, yet certain aspects of care remain infrequently documented and difficult to extract. Identifying these rarely documented elements requires advanced informatics approaches to uncover clinical documentation patterns that would otherwise remain inaccessible for research and quality improvement.This study developed and validated an informatics approach using natural language processing (NLP) to detect and characterize rarely documented elements in EHRs, using spiritual care documentation as [...]

To improve the identification of patients with health-related social needs (HRSNs) in the emergency department (ED), we developed and integrated a risk prediction score into an existing Fast Healthcare Interoperability Resources (FHIR)-based clinical decision support (CDS).

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.

Health consumers can use generative artificial intelligence (GenAI) chatbots to seek health information. As GenAI chatbots continue to improve and be adopted, it is crucial to examine how health information generated by such tools is used and perceived by health consumers.To conduct a scoping review of health consumers' use and perceptions of health information from GenAI chatbots.Arksey and O'Malley's five-step protocol was used to guide the scoping review. Following PRISMA [...]

To evaluate an automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, called RAPID.

Fairness concerns stemming from known and unknown biases in healthcare practices have raised questions about the trustworthiness of Artificial Intelligence (AI)-driven Clinical Decision Support Systems (CDSS). Studies have shown unforeseen performance disparities in subpopulations when applied to clinical settings different from training. Existing unfairness mitigation strategies often struggle with scalability and accessibility, while their pursuit of group-level prediction performance parity does not effectively translate into fairness at the point of [...]

This study aims to tackle the critical challenge of adapting deep learning (DL) models for deployment in real-world healthcare settings, specifically focusing on catastrophic forgetting due to distribution shifts between hospital and non-hospital environments. Metabolic syndrome (MetS) is susceptible to misdiagnosis by DL models due to distribution shifts. This work demonstrates the potential of continual learning (CL) to enhance model performance in MetS identification across diverse settings.

Diagnosing post-traumatic stress disorder (PTSD) remains a challenge due to symptom variability and comorbidities. Linguistic analysis offers an innovative approach to identify PTSD symptoms and severity. This systematic review aimed at identifying linguistic features associated with PTSD, assessing the quality and limitations of existing studies, summarizing the predictive performance of identified models, and describing the clinical utility of these models.

Accurate discharge summaries are essential for effective communication between hospital and outpatient providers but generating them is labor-intensive. Large language models (LLMs), such as GPT-4, have shown promise in automating this process, potentially reducing clinician workload and improving documentation quality. A recent study using GPT-4 to generate discharge summaries via concatenated clinical notes found that while the summaries were concise and coherent, they often lacked comprehensiveness and contained errors. To [...]