# Using systems science to optimize the impact of point-of-care viral load testing for pediatric HIV management

> **NIH NIH R21** · UNIVERSITY OF WASHINGTON · 2020 · $229,941

## Abstract

ABSTRACT
HIV-infected children have poorer viral load (VL) suppression than adults and experience high morbidity and
mortality. Regular pediatric VL monitoring is critical to direct pediatric treatment and clinical care. VL testing is
generally recommended every 6 months for the first year after treatment initiation and annually thereafter, but
turnaround time of standard of care (SOC) VL systems is slow (median 21 days). Point of care (POC) technology
has revolutionized HIV diagnosis and treatment initiation, and POC VL testing is the next frontier for HIV
treatment monitoring. Clinical effectiveness of POC VL testing for children is currently being assessed in an
ongoing randomized trial in Kenya (R34 PIs: Patel, Abuogi); however, large gaps exist between clinical
effectiveness and real-world use for POC technology. Optimizing POC VL testing using a systems engineering
approach will help realize the full impact of investments in POC VL monitoring. In the current proposal, we take
a systems engineering approach and borrow methods from diverse fields, such as manufacturing, psychology,
and software development to achieve the following aims: Aim 1: To determine the optimal placement of limited
POC VL machines within a hub-and-spoke vs. platform sharing models, to balance budget impact and minimize
turnaround time. We will create a queuing model – used in industrial engineering to model waiting times – to
identify optimal placement of POC machines in Kenya. We will model the reduction in turnaround time and
waiting time associated with placement of POC machines in select “hub” facilities (sites with a POC machine)
and “spoke” (sites that send samples to a hub) facilities vs. platform sharing amongst sets of facilities. At different
budget levels, we will identify the optimal number and placement of POC machines. Aim 2: To determine
policymakers’ opinions about usability of the model tool from Aim 1. We will convert the model from Aim 1 into a
user-friendly, Excel-based model for policymakers to use for decision-making. We will conduct usability
interviews (covering learnability, efficiency, memorability, error recovery, and satisfaction) with approximately 20
health administrators and policymakers about the tool. This novel application of diverse methods borrowed from
industrial engineering, software engineering, psychology, and quality improvement presents an innovative
approach to increase scalability of POC VL testing for children.

## Key facts

- **NIH application ID:** 10009022
- **Project number:** 1R21MH122361-01A1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Rena Chiman Patel
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $229,941
- **Award type:** 1
- **Project period:** 2020-09-15 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10009022

## Citation

> US National Institutes of Health, RePORTER application 10009022, Using systems science to optimize the impact of point-of-care viral load testing for pediatric HIV management (1R21MH122361-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10009022. Licensed CC0.

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