The standard problem

Abstract Objective This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed. Methods Beginning with the notion of common models, the framework describes the general standard problem—the seeming impossibility of cre...

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Published inJournal of the American Medical Informatics Association : JAMIA Vol. 30; no. 12; pp. 2086 - 2097
Main Author Coiera, Enrico
Format Journal Article
LanguageEnglish
Published England Oxford University Press 17.11.2023
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Online AccessGet full text
ISSN1067-5027
1527-974X
1527-974X
DOI10.1093/jamia/ocad176

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Abstract Abstract Objective This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed. Methods Beginning with the notion of common models, the framework describes the general standard problem—the seeming impossibility of creating a singular, persistent, and definitive standard which is not subject to change over time in an open system. Results The standard problem arises from uncertainty driven by variations in operating context, standard quality, differences in implementation, and drift over time. As a result, fitting work using conformance services is needed to repair these gaps between a standard and what is required for real-world use. To guide standards design and repair, a framework for measuring performance in context is suggested, based on signal detection theory and technomarkers. Based on the type of common model in operation, different conformance strategies are identified: (1) Universal conformance (all agents access the same standard); (2) Mediated conformance (an interoperability layer supports heterogeneous agents); and (3) Localized conformance (autonomous adaptive agents manage their own needs). Conformance methods include incremental design, modular design, adaptors, and creating interactive and adaptive agents. Discussion Machine learning should have a major role in adaptive fitting. Research to guide the choice and design of conformance services may focus on the stability and homogeneity of shared tasks, and whether common models are shared ahead of time or adjusted at task time. Conclusion This analysis conceptually decouples interoperability and standardization. While standards facilitate interoperability, interoperability is achievable without standardization.
AbstractList This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed.OBJECTIVEThis article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed.Beginning with the notion of common models, the framework describes the general standard problem-the seeming impossibility of creating a singular, persistent, and definitive standard which is not subject to change over time in an open system.METHODSBeginning with the notion of common models, the framework describes the general standard problem-the seeming impossibility of creating a singular, persistent, and definitive standard which is not subject to change over time in an open system.The standard problem arises from uncertainty driven by variations in operating context, standard quality, differences in implementation, and drift over time. As a result, fitting work using conformance services is needed to repair these gaps between a standard and what is required for real-world use. To guide standards design and repair, a framework for measuring performance in context is suggested, based on signal detection theory and technomarkers. Based on the type of common model in operation, different conformance strategies are identified: (1) Universal conformance (all agents access the same standard); (2) Mediated conformance (an interoperability layer supports heterogeneous agents); and (3) Localized conformance (autonomous adaptive agents manage their own needs). Conformance methods include incremental design, modular design, adaptors, and creating interactive and adaptive agents.RESULTSThe standard problem arises from uncertainty driven by variations in operating context, standard quality, differences in implementation, and drift over time. As a result, fitting work using conformance services is needed to repair these gaps between a standard and what is required for real-world use. To guide standards design and repair, a framework for measuring performance in context is suggested, based on signal detection theory and technomarkers. Based on the type of common model in operation, different conformance strategies are identified: (1) Universal conformance (all agents access the same standard); (2) Mediated conformance (an interoperability layer supports heterogeneous agents); and (3) Localized conformance (autonomous adaptive agents manage their own needs). Conformance methods include incremental design, modular design, adaptors, and creating interactive and adaptive agents.Machine learning should have a major role in adaptive fitting. Research to guide the choice and design of conformance services may focus on the stability and homogeneity of shared tasks, and whether common models are shared ahead of time or adjusted at task time.DISCUSSIONMachine learning should have a major role in adaptive fitting. Research to guide the choice and design of conformance services may focus on the stability and homogeneity of shared tasks, and whether common models are shared ahead of time or adjusted at task time.This analysis conceptually decouples interoperability and standardization. While standards facilitate interoperability, interoperability is achievable without standardization.CONCLUSIONThis analysis conceptually decouples interoperability and standardization. While standards facilitate interoperability, interoperability is achievable without standardization.
This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed. Beginning with the notion of common models, the framework describes the general standard problem-the seeming impossibility of creating a singular, persistent, and definitive standard which is not subject to change over time in an open system. The standard problem arises from uncertainty driven by variations in operating context, standard quality, differences in implementation, and drift over time. As a result, fitting work using conformance services is needed to repair these gaps between a standard and what is required for real-world use. To guide standards design and repair, a framework for measuring performance in context is suggested, based on signal detection theory and technomarkers. Based on the type of common model in operation, different conformance strategies are identified: (1) Universal conformance (all agents access the same standard); (2) Mediated conformance (an interoperability layer supports heterogeneous agents); and (3) Localized conformance (autonomous adaptive agents manage their own needs). Conformance methods include incremental design, modular design, adaptors, and creating interactive and adaptive agents. Machine learning should have a major role in adaptive fitting. Research to guide the choice and design of conformance services may focus on the stability and homogeneity of shared tasks, and whether common models are shared ahead of time or adjusted at task time. This analysis conceptually decouples interoperability and standardization. While standards facilitate interoperability, interoperability is achievable without standardization.
Abstract Objective This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed. Methods Beginning with the notion of common models, the framework describes the general standard problem—the seeming impossibility of creating a singular, persistent, and definitive standard which is not subject to change over time in an open system. Results The standard problem arises from uncertainty driven by variations in operating context, standard quality, differences in implementation, and drift over time. As a result, fitting work using conformance services is needed to repair these gaps between a standard and what is required for real-world use. To guide standards design and repair, a framework for measuring performance in context is suggested, based on signal detection theory and technomarkers. Based on the type of common model in operation, different conformance strategies are identified: (1) Universal conformance (all agents access the same standard); (2) Mediated conformance (an interoperability layer supports heterogeneous agents); and (3) Localized conformance (autonomous adaptive agents manage their own needs). Conformance methods include incremental design, modular design, adaptors, and creating interactive and adaptive agents. Discussion Machine learning should have a major role in adaptive fitting. Research to guide the choice and design of conformance services may focus on the stability and homogeneity of shared tasks, and whether common models are shared ahead of time or adjusted at task time. Conclusion This analysis conceptually decouples interoperability and standardization. While standards facilitate interoperability, interoperability is achievable without standardization.
Author Coiera, Enrico
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Issue 12
Keywords technology evaluation
interoperability
machine learning
information standard
Language English
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Snippet Abstract Objective This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and...
This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed. Beginning with...
This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed.OBJECTIVEThis...
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SubjectTerms Reference Standards
Title The standard problem
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