Balancing patient choice and health system capacity: a system dynamics model of dialysis in Thailand

As universal health coverage schemes mature, governments often seek to improve patient choice, whilst ensuring that services are appropriate, high-quality, and financially sustainable, especially for high-cost interventions like dialysis. Policy levers to manage supply and demand for services have shown mixed results across contexts, highlighting the complex interactions and feedback effects that shape health system behaviours. Following a policy change in Thailand aiming to improve patient choice for dialysis, we developed a system dynamics model of dialysis demand and supply, to explore the impact of proposed policies on dialysis services whilst accounting for considerable uncertainty in how these policies may work. Model structure was based on a causal loop diagram developed in consultation with stakeholders and iteratively refined through testing, calibration, and validation. The resulting model projected profile of dialysis patients over a 10-year time horizon (2025–2034) under the current policy alongside policy interventions proposed by a working group under the National Health Security Office. We conducted structural and parameter uncertainty analysis to account for uncertainties in the base model and in the mechanisms of action of proposed policy interventions. The model projected that more than one-third of new dialysis patients would inappropriately initiate dialysis under the current policy. None of the proposed policy interventions, either alone or in combination, achieved the defined policy target of 50% new dialysis patients on peritoneal dialysis within 3 years, with a maximum of 45% achieved from combining policies. Performance of all policies decreased over time unless the policy was able to progressively reduce financial incentives paid by private dialysis centres to physicians. Regulating financial incentives in the Thai health system offered the greatest potential to reduce inappropriate dialysis initiation and increase peritoneal dialysis uptake. The system dynamics model showed that coupling policies with complementary mechanisms could address key uncertainties and amplify their impact. We suggest that policymakers incorporate quality of care and time-dependent performance into policy goals to achieve sustainable improvements. Our findings highlight the value of a systems approach to account for unintended consequences of well-intended policy interventions, resulting from delayed responses across organisational boundaries.