Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Introduction to Language of Pathophysiology ll

Introduction to Language of Pathophysiology ll

This lesson explores key terms that describe how diseases progress, their outcomes, and their distribution in populations.Diagnostic tests identify diseases and monitor treatment. These include blood and urine tests, biopsies, imaging (X-ray, MRI), and detection of infectious agents.Remission is a reduction or disappearance of symptoms.Exacerbation refers to the worsening of symptoms, such as increased wheezing during an asthma attack.A precipitating factor triggers an acute episode, while a...

Nursing Clinical Information System

Nursing Clinical Information System

Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:

Introduction to Language of Pathophysiology l

Introduction to Language of Pathophysiology l

Pathophysiology investigates how biological mechanisms—typically starting at the cellular level—disrupt normal bodily functions. It bridges anatomy and physiology to explain the progression of disease. With this foundation, it is important to understand the following key terms used to describe disease processes: Diagnosis:The process of identifying a disease using clinical evaluation, including signs (objective evidence like rashes), symptoms (subjective experiences like pain), laboratory test...

Quality Assurance

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...

Clinical Trials

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...

Quality Control

Quality Control

Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

DICODerma: A Practical Approach for Metadata Management of Images in Dermatology.

Journal of digital imaging·2022

Same author

Mobile Access to ClinicalConnect: A User Feedback Survey on Usability, Productivity, and Quality.

JMIR mHealth and uHealth·2015

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 16, 2026

Enhancing Efficiency and Radiolabeling Yields of Carbon-11 Radioligands for Clinical Research Using the Loop Method

Enhancing Efficiency and Radiolabeling Yields of Carbon-11 Radioligands for Clinical Research Using the Loop Method

Published on: December 20, 2024

LLM-in-the-Loop execution of clinical quality language.

Bell Raj Eapen¹, Oladimeji M Adaramewa¹, Xiaoqing Li¹

¹Management Information Systems, University of Illinois, Springfield, IL, United States.

|June 15, 2026

Summary

This study introduces an LLM-in-the-Loop (LitL) method to enhance Clinical Quality Language (CQL) for querying unstructured text. The open-source prototype improves data analysis by integrating LLMs into the CQL execution loop.

Keywords:

Clinical quality language FHIR clinical decision support large language models unstructured text

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Jun 16, 2026

Enhancing Efficiency and Radiolabeling Yields of Carbon-11 Radioligands for Clinical Research Using the Loop Method

Enhancing Efficiency and Radiolabeling Yields of Carbon-11 Radioligands for Clinical Research Using the Loop Method

Published on: December 20, 2024

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Health Informatics
Natural Language Processing
Clinical Decision Support

Background:

Clinical Quality Language (CQL) is crucial for standardized clinical decision support.
Querying unstructured clinical text remains a significant challenge in healthcare data analysis.
Existing CQL engines primarily handle structured data, limiting their application to narrative clinical notes.

Purpose of the Study:

To develop a method and open-source prototype for augmenting CQL with Large Language Models (LLMs).
To enable CQL to query unstructured clinical text by integrating LLMs into the execution loop.
To preserve compatibility with existing CQL infrastructure while expanding its capabilities.

Main Methods:

Modified a popular CQL engine to incorporate an LLM-in-the-Loop (LitL) pipeline.
The LitL pipeline is triggered when CQL references unstructured FHIR resources.
LLMs generate binary responses to natural language queries derived from CQL.

Main Results:

An open-source prototype implementing the LitL pattern and a modified CQL engine was developed.
Feasibility testing with two local LLMs yielded accuracies of 72% and 93%.
The prototype supports configurable models, prompts, and hyperparameters for flexible implementation.

Conclusions:

The LitL pattern successfully extends CQL capabilities to unstructured data.
This approach maintains compatibility with existing CQL standards and reduces LLM hallucination.
The free and open-source nature of the prototype facilitates wider adoption and further development.