Title |
Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs)
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Published in |
BMC Medical Informatics and Decision Making, October 2011
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DOI | 10.1186/1472-6947-11-62 |
Pubmed ID | |
Authors |
Emma Wallace, Susan M Smith, Rafael Perera-Salazar, Paul Vaucher, Colin McCowan, Gary Collins, Jan Verbakel, Monica Lakhanpaul, Tom Fahey, (Members of the International Diagnostic and Prognosis Prediction (IDAPP) group) |
Abstract |
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. |
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Unknown | 1 | 100% |
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
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United Kingdom | 2 | 2% |
Canada | 2 | 2% |
Australia | 1 | <1% |
Unknown | 126 | 96% |
Demographic breakdown
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Student > Ph. D. Student | 30 | 23% |
Researcher | 18 | 14% |
Student > Master | 14 | 11% |
Professor | 10 | 8% |
Student > Doctoral Student | 9 | 7% |
Other | 27 | 21% |
Unknown | 23 | 18% |
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Computer Science | 14 | 11% |
Nursing and Health Professions | 8 | 6% |
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Biochemistry, Genetics and Molecular Biology | 3 | 2% |
Other | 14 | 11% |
Unknown | 28 | 21% |