Using a stylised coordination problem drawn from inpatient capacity management, three archetypal forms of AI deployment are described: effort-reducing technologies, observability-oriented systems, and interventions that alter underlying incentive structures. Effort reduction and observability may improve performance within existing patterns of behaviour but do not, in general, change which actions are individually rational. As a result, such interventions are typically absorbed into existing equilibria. By contrast, interventions that modify how local actions map to downstream consequences by redistributing or bounding local risk can change stable system behaviour. These mechanism-level interventions differ not in technical sophistication but in their interaction with institutional incentives. The analysis suggests that expectations of system-level gains from AI should be conditioned on whether a deployment changes incentives rather than optimising tasks or information flows alone. For healthcare organisations and policymakers, this has practical implications for procurement, governance, and evaluation of digital technologies.

翻译：借鉴住院病床容量管理中提炼的典型协调问题，本文描述了人工智能部署的三种原型形式：减少劳动的技术、面向可观测性的系统，以及改变潜在激励结构的干预措施。减少劳动和可观测性可能提升现有行为模式下的绩效，但通常不会改变个体理性行为的选择。因此，此类干预通常被吸收进现有均衡中。相比之下，通过重新分配或限定局部风险来改变局部行动如何映射为下游后果的干预措施，能够改变稳定的系统行为。这些机制层面的干预差异不在于技术复杂性，而在于其与制度激励的交互作用。分析表明，对人工智能带来的系统层面收益的预期，应取决于部署是否改变了激励，而非仅仅优化任务或信息流。对于医疗机构和政策制定者而言，这为数字技术的采购、治理和评估提供了实践启示。