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Measures and Metrics for Feasibility of Proof-of-Concept Studies With Human Immunodeficiency Virus Rapid Point-of-Care Technologies: The Evidence and the Framework

OBJECTIVE: Pilot (feasibility) studies form a vast majority of diagnostic studies with point-of-care technologies but often lack use of clear measures/metrics and a consistent framework for reporting and evaluation. To fill this gap, we systematically reviewed data to (a) catalog feasibility measure...

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Autores principales: Pant Pai, Nitika, Chiavegatti, Tiago, Vijh, Rohit, Karatzas, Nicolaos, Daher, Jana, Smallwood, Megan, Wong, Tom, Engel, Nora
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737458/
https://www.ncbi.nlm.nih.gov/pubmed/29333105
http://dx.doi.org/10.1097/POC.0000000000000147
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author Pant Pai, Nitika
Chiavegatti, Tiago
Vijh, Rohit
Karatzas, Nicolaos
Daher, Jana
Smallwood, Megan
Wong, Tom
Engel, Nora
author_facet Pant Pai, Nitika
Chiavegatti, Tiago
Vijh, Rohit
Karatzas, Nicolaos
Daher, Jana
Smallwood, Megan
Wong, Tom
Engel, Nora
author_sort Pant Pai, Nitika
collection PubMed
description OBJECTIVE: Pilot (feasibility) studies form a vast majority of diagnostic studies with point-of-care technologies but often lack use of clear measures/metrics and a consistent framework for reporting and evaluation. To fill this gap, we systematically reviewed data to (a) catalog feasibility measures/metrics and (b) propose a framework. METHODS: For the period January 2000 to March 2014, 2 reviewers searched 4 databases (MEDLINE, EMBASE, CINAHL, Scopus), retrieved 1441 citations, and abstracted data from 81 studies. We observed 2 major categories of measures, that is, implementation centered and patient centered, and 4 subcategories of measures, that is, feasibility, acceptability, preference, and patient experience. We defined and delineated metrics and measures for a feasibility framework. We documented impact measures for a comparison. FINDINGS: We observed heterogeneity in reporting of metrics as well as misclassification and misuse of metrics within measures. Although we observed poorly defined measures and metrics for feasibility, preference, and patient experience, in contrast, acceptability measure was the best defined. For example, within feasibility, metrics such as consent, completion, new infection, linkage rates, and turnaround times were misclassified and reported. Similarly, patient experience was variously reported as test convenience, comfort, pain, and/or satisfaction. In contrast, within impact measures, all the metrics were well documented, thus serving as a good baseline comparator. With our framework, we classified, delineated, and defined quantitative measures and metrics for feasibility. CONCLUSIONS: Our framework, with its defined measures/metrics, could reduce misclassification and improve the overall quality of reporting for monitoring and evaluation of rapid point-of-care technology strategies and their context-driven optimization.
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spelling pubmed-57374582018-01-12 Measures and Metrics for Feasibility of Proof-of-Concept Studies With Human Immunodeficiency Virus Rapid Point-of-Care Technologies: The Evidence and the Framework Pant Pai, Nitika Chiavegatti, Tiago Vijh, Rohit Karatzas, Nicolaos Daher, Jana Smallwood, Megan Wong, Tom Engel, Nora Point Care Review Article OBJECTIVE: Pilot (feasibility) studies form a vast majority of diagnostic studies with point-of-care technologies but often lack use of clear measures/metrics and a consistent framework for reporting and evaluation. To fill this gap, we systematically reviewed data to (a) catalog feasibility measures/metrics and (b) propose a framework. METHODS: For the period January 2000 to March 2014, 2 reviewers searched 4 databases (MEDLINE, EMBASE, CINAHL, Scopus), retrieved 1441 citations, and abstracted data from 81 studies. We observed 2 major categories of measures, that is, implementation centered and patient centered, and 4 subcategories of measures, that is, feasibility, acceptability, preference, and patient experience. We defined and delineated metrics and measures for a feasibility framework. We documented impact measures for a comparison. FINDINGS: We observed heterogeneity in reporting of metrics as well as misclassification and misuse of metrics within measures. Although we observed poorly defined measures and metrics for feasibility, preference, and patient experience, in contrast, acceptability measure was the best defined. For example, within feasibility, metrics such as consent, completion, new infection, linkage rates, and turnaround times were misclassified and reported. Similarly, patient experience was variously reported as test convenience, comfort, pain, and/or satisfaction. In contrast, within impact measures, all the metrics were well documented, thus serving as a good baseline comparator. With our framework, we classified, delineated, and defined quantitative measures and metrics for feasibility. CONCLUSIONS: Our framework, with its defined measures/metrics, could reduce misclassification and improve the overall quality of reporting for monitoring and evaluation of rapid point-of-care technology strategies and their context-driven optimization. Lippincott Williams & Wilkins 2017-12 2017-11-14 /pmc/articles/PMC5737458/ /pubmed/29333105 http://dx.doi.org/10.1097/POC.0000000000000147 Text en Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Pant Pai, Nitika
Chiavegatti, Tiago
Vijh, Rohit
Karatzas, Nicolaos
Daher, Jana
Smallwood, Megan
Wong, Tom
Engel, Nora
Measures and Metrics for Feasibility of Proof-of-Concept Studies With Human Immunodeficiency Virus Rapid Point-of-Care Technologies: The Evidence and the Framework
title Measures and Metrics for Feasibility of Proof-of-Concept Studies With Human Immunodeficiency Virus Rapid Point-of-Care Technologies: The Evidence and the Framework
title_full Measures and Metrics for Feasibility of Proof-of-Concept Studies With Human Immunodeficiency Virus Rapid Point-of-Care Technologies: The Evidence and the Framework
title_fullStr Measures and Metrics for Feasibility of Proof-of-Concept Studies With Human Immunodeficiency Virus Rapid Point-of-Care Technologies: The Evidence and the Framework
title_full_unstemmed Measures and Metrics for Feasibility of Proof-of-Concept Studies With Human Immunodeficiency Virus Rapid Point-of-Care Technologies: The Evidence and the Framework
title_short Measures and Metrics for Feasibility of Proof-of-Concept Studies With Human Immunodeficiency Virus Rapid Point-of-Care Technologies: The Evidence and the Framework
title_sort measures and metrics for feasibility of proof-of-concept studies with human immunodeficiency virus rapid point-of-care technologies: the evidence and the framework
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737458/
https://www.ncbi.nlm.nih.gov/pubmed/29333105
http://dx.doi.org/10.1097/POC.0000000000000147
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