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Problem-solving in statics is a crucial aspect of engineering and physics that involves resolving issues associated with bodies in a state of equilibrium. In most cases, problem-solving requires several steps to achieve an accurate result. These steps are crucial to ensuring that the solution is accurate and practical.
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The standard problem.

Enrico Coiera1

  • 1Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia.

Journal of the American Medical Informatics Association : JAMIA
|September 1, 2023
PubMed
Summary

This research introduces a framework for measuring and designing scientific standards, addressing challenges in creating stable, long-term standards for open systems. It proposes conformance strategies to bridge the gap between standards and real-world applications.

Keywords:
information standardinteroperabilitymachine learningtechnology evaluation

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Area of Science:

  • Computer Science
  • Information Science
  • Systems Engineering

Background:

  • The inherent challenge of creating singular, persistent scientific standards in open, evolving systems.
  • Uncertainty in standards arises from context variations, quality issues, implementation differences, and temporal drift.
  • Existing standards often require repair to align with real-world usage.

Purpose of the Study:

  • To propose a framework for the scientific research, measurement, evaluation, and design of standards.
  • To address the 'standard problem' of creating adaptable and persistent standards.
  • To guide the development and repair of standards through a performance measurement framework.

Main Methods:

  • Utilizing signal detection theory and technomarkers for performance measurement in context.
  • Identifying three conformance strategies: Universal, Mediated, and Localized conformance.
  • Proposing conformance methods including incremental design, modular design, adaptors, and adaptive agents.

Main Results:

  • A framework is presented for measuring standard performance in context.
  • Three distinct conformance strategies are defined based on common model types.
  • Machine learning is identified as crucial for adaptive fitting and optimizing conformance services.

Conclusions:

  • Interoperability and standardization are conceptually distinct, with interoperability achievable even without formal standardization.
  • The proposed framework supports the adaptive design and evaluation of scientific standards.
  • Future research should focus on task stability and model sharing strategies for conformance services.