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Updated: Jul 1, 2025

Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model

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Published on: October 31, 2010

A Generalizable Decision-Making Framework for Selecting Onsite versus Send-out Clinical Laboratory Testing.

Lee F Schroeder¹, Paul Rebman², Parastu Kasaie²

¹Department of Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA.

Medical Decision Making : an International Journal of the Society for Medical Decision Making

|March 7, 2024

Summary

This summary is machine-generated.

Choosing between onsite and send-out laboratory testing depends on balancing testing delays and accuracy. Onsite testing is often preferred when delays significantly reduce clinical utility or when onsite accuracy is high.

Keywords:

CP general cost-effectiveness decision analysis diagnostic network global health laboratory network management/administration model specimen transport threshold approach utility

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Last Updated: Jul 1, 2025

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Area of Science:

Medical Decision Making
Health Services Research
Laboratory Science

Background:

Laboratory networks offer onsite testing or transport to higher-tier facilities.
Test placement decisions involve complex interactions between testing, treatment, and epidemiology.

Purpose of the Study:

To develop a generalizable model for optimizing laboratory test placement.
To inform decisions on onsite versus send-out testing using a threshold approach.

Main Methods:

A decision model was created to compare onsite and send-out testing utility.
Monte Carlo simulations were used to determine optimal testing strategies.
Tuberculosis testing was used as a case study.

Main Results:

Lost clinical utility from send-out delays and accuracy decrements in onsite testing are key factors.
Onsite testing is favored when delays reduce utility by over 20% (minimal accuracy loss) or over 50% (significant accuracy loss).
Relative costs influence thresholds, especially when testing expenses exceed 10% of treatment costs.

Conclusions:

A threshold approach enables evidence-based decisions for laboratory test placement.
The model considers clinical utility lost to delays and relative testing accuracy.
This framework is adaptable for various laboratory network scenarios.