Towards explicit and transparent surgical prioritization

This thesis investigates the challenges faced by healthcare systems, particularly surgical capacity, during the COVID-19 pandemic and the potential of outcome-based prioritization models to address resource scarcity. The pandemic highlighted vulnerabilities, with significant reductions in surgeries leading to backlogs and worsened outcomes. The thesis evaluates a decision model that prioritizes surgeries based on health losses from delays, using a utilitarian approach to maximize societal health benefits.

The study compares the decision model’s outcomes with current clinical practices, finding differences that suggest improved prioritization using more objective criteria. The model is refined by incorporating more surgeries, evaluating alternative treatments, and assessing quality of life (QoL) values from both physicians and citizens. These updates strengthen the model’s robustness and applicability.

The thesis also explores stakeholder perspectives, revealing strong support for transparent, objective prioritization, with patients and the public favoring prioritization based on health gains and societal outcomes. It concludes by recommending clearer political decisions on healthcare spending, formalized capacity management, and further refinement of the prioritization model to address ongoing resource challenges.