AIMC Topic: Documentation

Clear Filters Showing 21 to 30 of 181 articles

Clinician Experiences With Ambient Scribe Technology to Assist With Documentation Burden and Efficiency.

JAMA network open
IMPORTANCE: Timely evaluation of ambient scribing technology is warranted to assess whether this technology can lessen the burden of clinical documentation on clinicians.

Accuracy and Safety of AI-Enabled Scribe Technology: Instrument Validation Study.

Journal of medical Internet research
Artificial intelligence-enabled ambient digital scribes may have many potential benefits, yet results from our study indicate that there are errors that must be evaluated to mitigate safety risks.

Assessment of Real-Time Natural Language Processing for Improving Diagnostic Specificity: A Prospective, Crossover Exploratory Study.

Applied clinical informatics
BACKGROUND:  Reliable, precise, timely, and clear documentation of diagnoses is difficult. Poor specificity or the absence of diagnostic documentation can lead to decreased revenue and increased payor denials, audits, and queries to providers. Nuance...

Artificial intelligence for better goals of care documentation.

BMJ supportive & palliative care
OBJECTIVES: Lower rates of goals of care (GOC) conversations have been observed in non-white hospitalised patients, which may contribute to racial disparities in end-of-life care. We aimed to assess how a targeted initiative to increase GOC documenta...

Assessing the Efficacy and Clinical Utility of Artificial Intelligence Scribes in Urology.

Urology
OBJECTIVE: To assess the quality of artificial intelligence (AI) scribes and to evaluate their impact on urologic practice.

Extracting International Classification of Diseases Codes from Clinical Documentation Using Large Language Models.

Applied clinical informatics
BACKGROUND:  Large language models (LLMs) have shown promise in various professional fields, including medicine and law. However, their performance in highly specialized tasks, such as extracting ICD-10-CM codes from patient notes, remains underexplo...

Large Language Models to Identify Advance Care Planning in Patients With Advanced Cancer.

Journal of pain and symptom management
CONTEXT: Efficiently tracking Advance Care Planning (ACP) documentation in electronic heath records (EHRs) is essential for quality improvement and research efforts. The use of large language models (LLMs) offers a novel approach to this task.

Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure.

JAMA network open
IMPORTANCE: Serial functional status assessments are critical to heart failure (HF) management but are often described narratively in documentation, limiting their use in quality improvement or patient selection for clinical trials.

Large language models for structured reporting in radiology: past, present, and future.

European radiology
Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence to guidelines, its widespread adopti...

The Impact of Collaborative Documentation on Person-Centered Care: Textual Analysis of Clinical Notes.

JMIR medical informatics
BACKGROUND: Collaborative documentation (CD) is a behavioral health practice involving shared writing of clinic visit notes by providers and consumers. Despite widespread dissemination of CD, research on its effectiveness or impact on person-centered...