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Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs)
Clinical Prediction Rules (CPRs) are tools that quantify the contribution of symptoms, clinical signs and available diagnostic tests, and in doing so stratify patients according to the probability of having a target outcome or need for a specified treatment. Most focus on the derivation stage with o...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3216240/ https://www.ncbi.nlm.nih.gov/pubmed/21999201 http://dx.doi.org/10.1186/1472-6947-11-62 |
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author | Wallace, Emma Smith, Susan M Perera-Salazar, Rafael Vaucher, Paul McCowan, Colin Collins, Gary Verbakel, Jan Lakhanpaul, Monica Fahey, Tom |
author_facet | Wallace, Emma Smith, Susan M Perera-Salazar, Rafael Vaucher, Paul McCowan, Colin Collins, Gary Verbakel, Jan Lakhanpaul, Monica Fahey, Tom |
author_sort | Wallace, Emma |
collection | PubMed |
description | Clinical Prediction Rules (CPRs) are tools that quantify the contribution of symptoms, clinical signs and available diagnostic tests, and in doing so stratify patients according to the probability of having a target outcome or need for a specified treatment. Most focus on the derivation stage with only a minority progressing to validation and very few undergoing impact analysis. Impact analysis studies remain the most efficient way of assessing whether incorporating CPRs into a decision making process improves patient care. However there is a lack of clear methodology for the design of high quality impact analysis studies. We have developed a sequential four-phased framework based on the literature and the collective experience of our international working group to help researchers identify and overcome the specific challenges in designing and conducting an impact analysis of a CPR. There is a need to shift emphasis from deriving new CPRs to validating and implementing existing CPRs. The proposed framework provides a structured approach to this topical and complex area of research. |
format | Online Article Text |
id | pubmed-3216240 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-32162402011-11-16 Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs) Wallace, Emma Smith, Susan M Perera-Salazar, Rafael Vaucher, Paul McCowan, Colin Collins, Gary Verbakel, Jan Lakhanpaul, Monica Fahey, Tom BMC Med Inform Decis Mak Correspondence Clinical Prediction Rules (CPRs) are tools that quantify the contribution of symptoms, clinical signs and available diagnostic tests, and in doing so stratify patients according to the probability of having a target outcome or need for a specified treatment. Most focus on the derivation stage with only a minority progressing to validation and very few undergoing impact analysis. Impact analysis studies remain the most efficient way of assessing whether incorporating CPRs into a decision making process improves patient care. However there is a lack of clear methodology for the design of high quality impact analysis studies. We have developed a sequential four-phased framework based on the literature and the collective experience of our international working group to help researchers identify and overcome the specific challenges in designing and conducting an impact analysis of a CPR. There is a need to shift emphasis from deriving new CPRs to validating and implementing existing CPRs. The proposed framework provides a structured approach to this topical and complex area of research. BioMed Central 2011-10-14 /pmc/articles/PMC3216240/ /pubmed/21999201 http://dx.doi.org/10.1186/1472-6947-11-62 Text en Copyright ©2011 Wallace et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Correspondence Wallace, Emma Smith, Susan M Perera-Salazar, Rafael Vaucher, Paul McCowan, Colin Collins, Gary Verbakel, Jan Lakhanpaul, Monica Fahey, Tom Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs) |
title | Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs) |
title_full | Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs) |
title_fullStr | Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs) |
title_full_unstemmed | Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs) |
title_short | Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs) |
title_sort | framework for the impact analysis and implementation of clinical prediction rules (cprs) |
topic | Correspondence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3216240/ https://www.ncbi.nlm.nih.gov/pubmed/21999201 http://dx.doi.org/10.1186/1472-6947-11-62 |
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