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Adaptation of clinical practice guidelines.

Perry Groot¹, Arjen Hommersom, Peter Lucas

¹Radboud University Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, the Netherlands. perry@cs.ru.nl

Studies in Health Technology and Informatics

|September 23, 2008

Summary

Developing clinical practice guidelines is resource-intensive. Local adaptation of existing international guidelines offers a cost-effective and efficient alternative, minimizing duplicated efforts in evidence appraisal.

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Area of Science:

Health Services Research
Medical Informatics

Background:

Clinical practice guideline development is a complex, time-consuming, and expensive process.
Systematic evidence appraisal is crucial but resource-intensive, leading to potential duplication of efforts.
Existing guidelines often require local adaptation to suit specific healthcare contexts.

Purpose of the Study:

To survey and synthesize existing work on the adaptation of clinical practice guidelines.
To explore methodologies, case studies, and effectiveness assessments of guideline adaptation.
To review related advancements in guideline adaptation within the Artificial Intelligence community.

Main Methods:

Systematic review and synthesis of literature on guideline adaptation.
Analysis of case studies demonstrating guideline adaptation in practice.
Examination of research on the effectiveness of adapted guidelines.
Exploration of Artificial Intelligence applications in guideline adaptation.

Main Results:

Guideline adaptation is a viable strategy to reduce costs and time in guideline development.
Various methodologies exist for adapting guidelines, with varying degrees of success.
Case studies illustrate successful and challenging implementations of guideline adaptation.
The effectiveness of adapted guidelines is influenced by the adaptation process and context.

Conclusions:

Local adaptation of international guidelines is a pragmatic approach to efficient healthcare guideline dissemination.
Further research is needed to optimize adaptation methodologies and assess long-term effectiveness.
Artificial Intelligence holds potential for enhancing the efficiency and accuracy of guideline adaptation processes.