Using participatory system dynamics learning to support Ryan White Planning Council priority setting and resource...

Area of Science:

• Public Health
• Health Services Research
• Systems Science

Background:

• The Ryan White CARE Act funds services for low-income people with HIV/AIDS (PLWH).
• Regional Ryan White Planning Councils (RWPC) allocate these funds based on local data to address the greatest needs.
• RWPC face challenges in interpreting complex epidemiological, service utilization, and community needs data for effective priority setting and resource allocation.

Purpose of the Study:

• To pilot system dynamics (SD) learning and a validated HIV care continuum SD simulation model for RWPC decision-making.
• To assess the feasibility and utility of SD modeling for understanding the HIV care continuum and simulating resource allocation strategies.
• To evaluate member responses and attitudes towards SD modeling for informing RWPC priorities and fund allocations.

Main Methods:

• A validated HIV care continuum SD simulation model was calibrated to a specific Ryan White funding area.
• Three workshops were conducted to facilitate SD learning, simulation of resource allocation scenarios, and comparison of population health outcomes.
• A mixed-methods evaluation documented the SD modeling process, participant experiences, and perceptions of benefits and limitations.

Main Results:

• Despite model complexity and member turnover, RWPC members successfully engaged with the SD simulation model.
• Participants demonstrated understanding of the model and proposed strategies for improving HIV care retention, viral suppression, and reducing infections.
• The pilot indicated that SD modeling can assist RWPC in making data-driven decisions for resource allocation.

Conclusions:

• System dynamics modeling offers a valuable tool to support Regional Ryan White Planning Councils in their complex decision-making processes.
• SD modeling can enhance the ability of RWPC to interpret data and identify effective strategies for improving HIV care outcomes.
• The pilot demonstrates the potential of SD learning and simulation to empower RWPC in optimizing resource allocation for people with HIV/AIDS.