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 in | Journal of the American Medical Informatics Association : JAMIA Vol. 30; no. 12; pp. 2086 - 2097 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
17.11.2023
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Subjects | |
Online Access | Get full text |
ISSN | 1067-5027 1527-974X 1527-974X |
DOI | 10.1093/jamia/ocad176 |
Cover
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. |
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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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