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Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing

Background. In response to demand for fast and efficient clinical testing, the use of point-of-care testing (POCT) has become increasingly common in the United States. However, studies of POCT implementation have found that adopting POCT may not always be advantageous relative to centralized laborat...

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Autores principales: Harder, Reed, Wei, Keji, Vaze, Vikrant, Stahl, James E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6589980/
https://www.ncbi.nlm.nih.gov/pubmed/31259251
http://dx.doi.org/10.1177/2381468319856306
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author Harder, Reed
Wei, Keji
Vaze, Vikrant
Stahl, James E.
author_facet Harder, Reed
Wei, Keji
Vaze, Vikrant
Stahl, James E.
author_sort Harder, Reed
collection PubMed
description Background. In response to demand for fast and efficient clinical testing, the use of point-of-care testing (POCT) has become increasingly common in the United States. However, studies of POCT implementation have found that adopting POCT may not always be advantageous relative to centralized laboratory testing. Methods. We construct a simulation model of patient flow in an outpatient care setting to evaluate tradeoffs involved in POCT implementation across multiple dimensions, comparing measures of patient outcomes in varying clinical scenarios, testing regimes, and patient conditions. Results. We find that POCT can significantly reduce clinical time for patients, as compared to traditional testing regimes, in settings where clinic and central testing areas are far apart. However, as distance from clinic to central testing area decreased, POCT advantage over central laboratory testing also decreased, in terms of time in the clinical system and estimated subsequent productivity loss. For example, testing for pneumonia resulted in an estimated average of 27.80 (central lab) versus 15.50 (POCT) total lost productive hours in a rural scenario, and an average of 14.92 (central lab) versus 15.50 (POCT) hours in a hospital-based scenario. Conclusions. Our results show that POCT can effectively reduce the average time a patient spends in the system for varying condition profiles and clinical scenarios. However, the number of total lost productive hours, a more holistic measure, is greatly affected by testing quality, where POCT often is at a disadvantage. Thus, it is important to consider factors such as clinical setting, target condition, testing costs, and test quality when selecting appropriate testing regime.
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spelling pubmed-65899802019-06-28 Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing Harder, Reed Wei, Keji Vaze, Vikrant Stahl, James E. MDM Policy Pract Article Background. In response to demand for fast and efficient clinical testing, the use of point-of-care testing (POCT) has become increasingly common in the United States. However, studies of POCT implementation have found that adopting POCT may not always be advantageous relative to centralized laboratory testing. Methods. We construct a simulation model of patient flow in an outpatient care setting to evaluate tradeoffs involved in POCT implementation across multiple dimensions, comparing measures of patient outcomes in varying clinical scenarios, testing regimes, and patient conditions. Results. We find that POCT can significantly reduce clinical time for patients, as compared to traditional testing regimes, in settings where clinic and central testing areas are far apart. However, as distance from clinic to central testing area decreased, POCT advantage over central laboratory testing also decreased, in terms of time in the clinical system and estimated subsequent productivity loss. For example, testing for pneumonia resulted in an estimated average of 27.80 (central lab) versus 15.50 (POCT) total lost productive hours in a rural scenario, and an average of 14.92 (central lab) versus 15.50 (POCT) hours in a hospital-based scenario. Conclusions. Our results show that POCT can effectively reduce the average time a patient spends in the system for varying condition profiles and clinical scenarios. However, the number of total lost productive hours, a more holistic measure, is greatly affected by testing quality, where POCT often is at a disadvantage. Thus, it is important to consider factors such as clinical setting, target condition, testing costs, and test quality when selecting appropriate testing regime. SAGE Publications 2019-06-21 /pmc/articles/PMC6589980/ /pubmed/31259251 http://dx.doi.org/10.1177/2381468319856306 Text en © The Author(s) 2019 http://www.creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Article
Harder, Reed
Wei, Keji
Vaze, Vikrant
Stahl, James E.
Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing
title Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing
title_full Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing
title_fullStr Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing
title_full_unstemmed Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing
title_short Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing
title_sort simulation analysis and comparison of point of care testing and central laboratory testing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6589980/
https://www.ncbi.nlm.nih.gov/pubmed/31259251
http://dx.doi.org/10.1177/2381468319856306
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