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

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:

Methods of Documentation VI: Case Management Model

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic illness...

Data Validation

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...

Statistical Software for Data Analysis and Clinical Trials

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

Clinical Trials: Overview

Clinical Trials: Overview

Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...

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...

You might also read

Related Articles

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

Sort by

Same author

Food insecurity in the household: Interventions tackling children's food insecurity, a commentary on a systematic review.

Journal of family & child health·2026

Same author

Environmental sustainability of clinical laboratories: A scoping review.

Annals of clinical biochemistry·2025

Same author

The influence of ethnicity on frailty in a United Kingdom (UK) population.

The Journal of frailty & aging·2025

Same author

Subtyping of triple-negative breast cancers: its prognostication and implications in diagnosis of breast origin.

ESMO open·2024

Same author

Medical Termination of Pregnancy - An Emerging Risk for Maternal Mortality.

Irish medical journal·2024

Same author

Preparation and evaluation of cytotoxic potential of paclitaxel containing poly-3-hydroxybutyrate-co-3-hydroxyvalarate (PTX/PHBV) nanoparticles.

Brazilian journal of biology = Revista brasleira de biologia·2023

Search research articles

Related Experiment Video

Updated: Jun 9, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

An automation framework for clinical codelist development validated with UK data from patients with multiple

A Aslam^1,2, L Walker³, M Abaho³

¹Information School, University of Sheffield, Sheffield, UK. a.aslam@sheffield.ac.uk.

BMC Medical Research Methodology

|May 24, 2025

Summary

Automating codelist generation significantly reduces time and effort for clinical experts. This framework streamlines the creation of complex healthcare codelists, improving efficiency and accuracy.

Keywords:

Automation Codelist DynAIRx Multiple long term conditions (MLTC)SNOMEDs

More Related Videos

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Related Experiment Videos

Last Updated: Jun 9, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Area of Science:

Health Informatics
Artificial Intelligence in Healthcare
Clinical Data Management

Background:

Codelists are essential for standardized healthcare communication but are time-consuming to create.
Existing literature emphasizes codelist transparency, often overlooking automation's potential.
Developing high-quality codelists requires significant clinical expert input and time.

Purpose of the Study:

To present an automated framework for generating clinical codelists with minimal expert input.
To demonstrate the framework's utility through the DynAIRx project case study.
To make the developed framework and codelists publicly accessible for future use.

Main Methods:

Developed a Codelist Generation Framework to automate codelist creation.
Applied the framework to the DynAIRx project, which aims to optimize medication prescribing for patients with multiple long-term conditions using AI.
Generated and validated approximately 214 codelists for DynAIRx with clinical experts.

Main Results:

The framework automated the shrinking of codelists using trusted sources and added new codes for review.
The DynAIRx case study generated a codelist of ~14,000 codes requiring only 7-9 hours of clinician time, a reduction of over 80% compared to traditional methods.
Validation by experts confirmed the appropriateness of the generated codelists, significantly reducing preparation time.

Conclusions:

The automated framework substantially lowers workload, minimizes human error, and saves considerable time, especially for clinical experts.
Emphasis on automation and trusted sources enhances transparency and reproducibility in codelist development.
This approach offers a significant improvement over traditional methods for creating complex healthcare codelists.