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A bayesian approach to laboratory utilization management
BACKGROUND: Laboratory utilization management describes a process designed to increase healthcare value by altering requests for laboratory services. A typical approach to monitor and prioritize interventions involves audits of laboratory orders against specific criteria, defined as rule-based labor...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4355837/ https://www.ncbi.nlm.nih.gov/pubmed/25774321 http://dx.doi.org/10.4103/2153-3539.151921 |
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author | Hauser, Ronald G. Jackson, Brian R. Shirts, Brian H. |
author_facet | Hauser, Ronald G. Jackson, Brian R. Shirts, Brian H. |
author_sort | Hauser, Ronald G. |
collection | PubMed |
description | BACKGROUND: Laboratory utilization management describes a process designed to increase healthcare value by altering requests for laboratory services. A typical approach to monitor and prioritize interventions involves audits of laboratory orders against specific criteria, defined as rule-based laboratory utilization management. This approach has inherent limitations. First, rules are inflexible. They adapt poorly to the ambiguity of medical decision-making. Second, rules judge the context of a decision instead of the patient outcome allowing an order to simultaneously save a life and break a rule. Third, rules can threaten physician autonomy when used in a performance evaluation. METHODS: We developed an alternative to rule-based laboratory utilization. The core idea comes from a formula used in epidemiology to estimate disease prevalence. The equation relates four terms: the prevalence of disease, the proportion of positive tests, test sensitivity and test specificity. When applied to a laboratory utilization audit, the formula estimates the prevalence of disease (pretest probability [PTP]) in the patients tested. The comparison of PTPs among different providers, provider groups, or patient cohorts produces an objective evaluation of laboratory requests. We demonstrate the model in a review of tests for enterovirus (EV) meningitis. RESULTS: The model identified subpopulations within the cohort with a low prevalence of disease. These low prevalence groups shared demographic and seasonal factors known to protect against EV meningitis. This suggests too many orders occurred from patients at low risk for EV. CONCLUSION: We introduce a new method for laboratory utilization management programs to audit laboratory services. |
format | Online Article Text |
id | pubmed-4355837 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Medknow Publications & Media Pvt Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-43558372015-03-13 A bayesian approach to laboratory utilization management Hauser, Ronald G. Jackson, Brian R. Shirts, Brian H. J Pathol Inform Original Article BACKGROUND: Laboratory utilization management describes a process designed to increase healthcare value by altering requests for laboratory services. A typical approach to monitor and prioritize interventions involves audits of laboratory orders against specific criteria, defined as rule-based laboratory utilization management. This approach has inherent limitations. First, rules are inflexible. They adapt poorly to the ambiguity of medical decision-making. Second, rules judge the context of a decision instead of the patient outcome allowing an order to simultaneously save a life and break a rule. Third, rules can threaten physician autonomy when used in a performance evaluation. METHODS: We developed an alternative to rule-based laboratory utilization. The core idea comes from a formula used in epidemiology to estimate disease prevalence. The equation relates four terms: the prevalence of disease, the proportion of positive tests, test sensitivity and test specificity. When applied to a laboratory utilization audit, the formula estimates the prevalence of disease (pretest probability [PTP]) in the patients tested. The comparison of PTPs among different providers, provider groups, or patient cohorts produces an objective evaluation of laboratory requests. We demonstrate the model in a review of tests for enterovirus (EV) meningitis. RESULTS: The model identified subpopulations within the cohort with a low prevalence of disease. These low prevalence groups shared demographic and seasonal factors known to protect against EV meningitis. This suggests too many orders occurred from patients at low risk for EV. CONCLUSION: We introduce a new method for laboratory utilization management programs to audit laboratory services. Medknow Publications & Media Pvt Ltd 2015-02-24 /pmc/articles/PMC4355837/ /pubmed/25774321 http://dx.doi.org/10.4103/2153-3539.151921 Text en Copyright: © 2015 Hauser RG. http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Original Article Hauser, Ronald G. Jackson, Brian R. Shirts, Brian H. A bayesian approach to laboratory utilization management |
title | A bayesian approach to laboratory utilization management |
title_full | A bayesian approach to laboratory utilization management |
title_fullStr | A bayesian approach to laboratory utilization management |
title_full_unstemmed | A bayesian approach to laboratory utilization management |
title_short | A bayesian approach to laboratory utilization management |
title_sort | bayesian approach to laboratory utilization management |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4355837/ https://www.ncbi.nlm.nih.gov/pubmed/25774321 http://dx.doi.org/10.4103/2153-3539.151921 |
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