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Nursing Clinical Information System (NCIS)
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Related Experiment Video

Updated: Sep 13, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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CLEAR: A vision to support clinical evidence lifecycle with continuous learning.

Yilu Fang1, Gongbo Zhang1, Fangyi Chen1

  • 1Department of Biomedical Informatics, Columbia University, New York 10032, USA.

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|July 31, 2025
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Summary

A new framework uses real-world data (RWD) to speed up clinical evidence generation. This approach enhances the learning health system (LHS) for better patient care and faster medical advancements.

Keywords:
Continuous learningEvidence-based medicineEvidence-based researchReal-world data

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Area of Science:

  • Health Informatics
  • Clinical Epidemiology
  • Data Science

Background:

  • Clinical evidence generation is slow and inefficient, widening the gap between healthcare needs and available data.
  • Current Learning Health Systems (LHS) struggle to integrate rapidly generated Real-World Data (RWD) effectively.
  • The demand for high-quality clinical evidence for decision-making consistently outpaces supply.

Purpose of the Study:

  • To propose a novel framework for leveraging Real-World Data (RWD) across the entire clinical evidence lifecycle.
  • To enhance the efficiency and scalability of evidence generation and implementation within a Learning Health System (LHS).
  • To establish a continuous learning mechanism for improving clinical practice and patient outcomes.

Main Methods:

  • Integration of RWD into the clinical evidence lifecycle through four closed feedback loops.
  • Utilizing modern data science and informatics to power the framework.
  • Focusing on research prioritization, study design, guideline development, evaluation, and shared decision-making.

Main Results:

  • The proposed framework enables rapid responsiveness to emerging health data and evolving healthcare needs.
  • Facilitates timely development and optimization of clinical guidelines.
  • Supports sustained improvements in clinical practice and patient outcomes through continuous learning.

Conclusions:

  • A new informatics-supported framework can significantly enhance the clinical evidence lifecycle.
  • Effective integration of RWD via feedback loops improves scalability and efficiency.
  • This approach is crucial for advancing the Learning Health System vision and optimizing healthcare delivery.