Related Experiment Video
Updated: Feb 7, 2026

A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
Practical guide for identifying unmet clinical needs for biomarkers.
Phillip J Monaghan1, Sarah Robinson2, Daniel Rajdl3
1The Christie Pathology Partnership, The Christie NHS Foundation Trust, Manchester, United Kingdom.
This article introduces a framework and checklist for identifying unmet clinical needs in biomarker development. The framework is designed to guide test evaluation by aligning new diagnostic tests with clinical pathways. The checklist is intended for use by multiple stakeholders, including laboratories, clinicians, and researchers. It supports consistent terminology and facilitates collaboration. The tool is aligned with IOM and FDA/CE guidelines. The authors propose that this approach will improve the evaluation and implementation of new medical tests.
Area of Science:
- Clinical laboratory medicine
- Biomarker development
- Medical diagnostics
Background:
Current clinical pathways often lack clear guidance for evaluating new diagnostic tests. Prior research has shown that biomarker development frequently proceeds without sufficient alignment to clinical needs. While existing frameworks exist, no prior work had resolved how to systematically identify unmet needs in diagnostic testing. This gap motivated the need for a standardized approach to biomarker evaluation. Researchers have shown that misaligned testing strategies can delay clinical adoption. The field requires tools that bridge the gap between research and clinical practice. No prior work had resolved how to consistently define test purposes within clinical pathways. This uncertainty drove the development of a practical checklist to guide biomarker evaluation.
Purpose Of The Study:
The study aimed to create a standardized framework for identifying unmet clinical needs in diagnostic testing. This paper proposes a checklist to align biomarker development with clinical pathways. The goal is to ensure that new tests meet real-world clinical requirements. The authors suggest that this approach will improve test evaluation consistency. The checklist is intended for use by multiple stakeholders in the diagnostic field. The tool supports the IOM and FDA/CE regulatory guidelines. It is designed to facilitate collaboration across disciplines. The authors propose that this framework will enhance biomarker implementation.
Main Methods:
The EFLM TE-WG developed a conceptual framework for test evaluation. They created an interactive checklist based on clinical pathway mapping. The framework emphasizes the clinical purpose of each test. The checklist includes criteria for analytical and clinical performance. It is designed for use by laboratories, clinicians, and industry partners. The tool aligns with existing regulatory recommendations. The authors suggest that the checklist supports iterative evaluation cycles. The framework is intended to guide test development from discovery to implementation.
Main Results:
The framework provides a structured approach to biomarker evaluation. The checklist includes 12 key areas for identifying clinical needs. It emphasizes the importance of defining test roles within clinical pathways. The tool supports consistent terminology across stakeholders. It aligns with IOM and FDA/CE requirements. The authors suggest that the checklist improves collaboration. It is designed for use before new tests reach the market. The framework is intended to guide both research and implementation phases.
Conclusions:
The authors propose that the framework enhances biomarker evaluation consistency. They suggest that the checklist supports stakeholder collaboration. The tool is intended to align test development with clinical needs. The framework is designed for use across disciplines. The authors propose that it improves terminology standardization. It is aligned with regulatory guidelines from IOM and FDA/CE. The tool is intended to guide test evaluation cycles. The authors suggest that it facilitates consultation and collaboration.
Frequently Asked Questions
The framework aims to align biomarker development with clinical pathways by identifying unmet clinical needs.
The checklist includes 12 criteria for evaluating clinical and analytical performance before new tests reach the market.
Defining roles ensures that tests meet specific clinical needs and improves consistency in evaluation.
The checklist is designed for use by laboratories, clinicians, researchers, and industry stakeholders.
The tool is aligned with IOM recommendations and FDA/CE regulatory requirements for biomarker evaluation.
The authors propose that the framework will improve collaboration and standardization in biomarker development.
More Related Videos
08:55A Practical Guide for the Production and PET/CT Imaging of 68Ga-DOTATATE for Neuroendocrine Tumors in Daily Clinical Practice
Published on: April 17, 2019
10:28Interventional Diagnostic Procedure: A Practical Guide for the Assessment of Coronary Vascular Function
Published on: March 15, 2022
Related Concept Videos
Characteristics of Practical Op Amps
The ratio of differential gain to the common-mode gain is defined as the common-mode rejection ratio (CMRR). This ratio quantifies the ability of operational amplifiers (op-amps) to reject common-mode...
Theoretical Foundations of Nursing Practice
Theories provide a perspective to assess patients' conditions and organize data and methods. They also assist in analyzing and interpreting information. They represent a...
Equivalent Circuits for Practical Transformers
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
Nursing Clinical Information System
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:
Clinical Trials
There are four phases in a clinical trial. A phase one...
Identifying Statistically Significant Differences: The F-Test