The partial adoption trap: Coordination failure, trust, and cultural lock-in in health AI adoption

Health artificial intelligence (AI) adoption presents a paradox: point-solution tools diffuse readily through clinical populations, yet system-change AI, which carries the greatest potential for pathway-level transformation, consistently stalls at partial adoption. An evolutionary game theoretic model is developed to explain this pattern. Doctors choose among three strategies: genuine adoption, partial adoption, and rejection, where genuine adoption is required for systemic benefits to materialise above a population threshold. The system is shown to be generically bistable, with a stable partial adoption equilibrium coexisting alongside full genuine adoption. The basin of attraction of the partial adoption trap is enlarged by three compounding failure modes: a threshold coordination failure arising from the non-appropriable nature of systemic benefits; a trust failure arising from the organisation's inability to credibly commit to sharing productivity gains; and a cultural failure arising from negative coordination norms among doctors. These failure modes are shown to be most severe precisely for the technologies with the greatest systemic value: the Value-Adoption Paradox. A cost ratchet dynamic implies that failed adoption attempts permanently lower barriers even when embedding fails, but this benefit is offset when trust erosion is rapid. Conditions are derived under which sustained but imperfect adoption pressure is welfare-improving, and the policy architecture required to escape the trap (targeting trust, sequencing, and team-level adoption) is characterised. Standard health system digital transformation policy, which typically addresses only the threshold failure through individual incentives, is predicted to systematically produce the partial adoption trap.