Related Experiment Video
Updated: Jul 11, 2025
23:56
Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model
Published on: October 31, 2010
16.8K
Using queueing models as a decision support tool in allocating point-of-care HIV viral load testing machines
Yinsheng Wang, Anjuli D Wagner1,2, Shan Liu2
1Department of Global Health, University of Washington, Seattle, Washington 98195, USA.
Health Policy and Planning
|November 10, 2023
Summary
Optimizing HIV viral load monitoring with point-of-care technologies significantly reduces turnaround time. Adding new machines and improving sample batching are key to maximizing access and efficiency in resource-limited settings.
Area of Science:
- Health Informatics
- Operations Research
- Public Health
Background:
- Point-of-care (POC) technologies for HIV viral load (VL) monitoring are expanding globally, especially in resource-limited settings.
- Optimizing the deployment and operation of these technologies is crucial for maximizing coverage, access, and minimizing turnaround time (TAT) and costs.
- Stakeholder input is vital for developing relevant and effective resource allocation models.
Purpose of the Study:
- To develop and apply an optimization model for HIV VL monitoring in Kisumu County, Kenya.
- To evaluate the impact of different scenarios of POC machine allocation on TAT, coverage, and access.
- To identify optimal referral networks and resource allocation strategies for HIV VL testing.
Main Methods:
- Developed an optimization model using queueing theory and integer programming, informed by qualitative focus groups with stakeholders.
- Modeled three scenarios: centralized labs only, centralized labs with existing POC hubs, and centralized labs with existing and new POC hubs.
- Calculated TAT for existing and optimal referral networks, incorporating sensitivity analyses for distributional fairness.
Main Results:
- The largest components of TAT were sample batching/transport waiting time and testing site waiting time, not transportation time.
- Implementing POC hubs significantly decreased average TAT from 39.8 hours (scenario 1) to 33.8 hours (scenario 2) and 31.1 hours (scenario 3).
- Increasing the number of POC machines and optimizing sample batching/processing rates had the most significant impact on reducing TAT.
Conclusions:
- A stakeholder-informed resource allocation model effectively identified optimal POC VL hub allocations and referral networks.
- The strategic addition of POC machines and operational improvements (e.g., sample batching, processing rates) can substantially decrease HIV VL monitoring TAT.
- This modeling approach provides a valuable tool for decision-makers to optimize resource allocation for HIV testing in resource-limited settings.
