Nitika Pant Pai, Tiago Chiavegatti, Rohit Vijh, Nicolaos Karatzas, Jana Daher, Megan Smallwood, Tom Wong, Nora Engel
{"title":"人类免疫缺陷病毒快速护理点技术概念验证研究可行性的措施和指标:证据和框架。","authors":"Nitika Pant Pai, Tiago Chiavegatti, Rohit Vijh, Nicolaos Karatzas, Jana Daher, Megan Smallwood, Tom Wong, Nora Engel","doi":"10.1097/POC.0000000000000147","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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 (<i>a</i>) catalog feasibility measures/metrics and (<i>b</i>) propose a framework.</p><p><strong>Methods: </strong>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.</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":44085,"journal":{"name":"Point of Care","volume":"16 4","pages":"141-150"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1097/POC.0000000000000147","citationCount":"2","resultStr":"{\"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.\",\"authors\":\"Nitika Pant Pai, Tiago Chiavegatti, Rohit Vijh, Nicolaos Karatzas, Jana Daher, Megan Smallwood, Tom Wong, Nora Engel\",\"doi\":\"10.1097/POC.0000000000000147\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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 (<i>a</i>) catalog feasibility measures/metrics and (<i>b</i>) propose a framework.</p><p><strong>Methods: </strong>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.</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":44085,\"journal\":{\"name\":\"Point of Care\",\"volume\":\"16 4\",\"pages\":\"141-150\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1097/POC.0000000000000147\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Point of Care\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1097/POC.0000000000000147\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2017/11/14 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"Nursing\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Point of Care","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1097/POC.0000000000000147","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/11/14 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"Nursing","Score":null,"Total":0}
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 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.
期刊介绍:
Point of Care: The Journal of Near-Patient Testing & Technology is a vital resource for directors and managers of large and small hospital pathology labs, blood centers, home health-care agencies, doctors" offices, and other healthcare facilities. Each issue brings you peer-reviewed original research articles, along with concepts, technologies and trends, covering topics that include: Test accuracy Turnaround time Data management Quality control Regulatory compliance Cost-effectiveness of testing