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

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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.