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Practice parameters: strategies for survival into the nineties.

J Rosenberg, M K Greenberg

    Neurology
    |May 1, 1992
    PubMed
    Summary

    Clinical practice guidelines aim to control rising medical costs by integrating scientific evidence and patient preferences. This review examines historical and organizational approaches to developing scientifically sound practice parameters.

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    Area of Science:

    • Health Policy
    • Medical Informatics
    • Evidence-Based Medicine

    Background:

    • Escalating healthcare costs necessitate cost-containment strategies.
    • Clinical practice guidelines have emerged as a potential solution for cost containment.
    • Historical methods for developing practice standards have been diverse.

    Purpose of the Study:

    • To review historical, governmental, and health organization approaches to developing practice parameters.
    • To examine the mechanism and mission of the American Academy of Neurology Quality Standards Subcommittee.
    • To clarify the terminology and types of recommendations within practice parameters.

    Main Methods:

    • Literature review of historical, governmental, and health organization approaches.
    • Analysis of the American Academy of Neurology Quality Standards Subcommittee's framework.
    • Examination of the classification of practice parameter recommendations (standards, guidelines, options).

    Main Results:

    • Various approaches exist for developing scientifically sound and clinically relevant practice parameters.
    • The American Academy of Neurology uses the term "practice parameter" for consistency with AMA terminology.
    • Practice parameters encompass standards, guidelines, and options, reflecting varying levels of clinical certainty based on evidence.

    Conclusions:

    • Practice parameters are crucial for achieving cost containment in healthcare.
    • Developing robust practice parameters requires integrating scientific knowledge and patient preferences.
    • Clear definitions and evidence-based classifications enhance the utility of practice parameters.

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