Look Before You Leap: Planning with Risk and Cost Awareness for a Clinical Diagnosis Agent

Clinical diagnosis unfolds as a sequence of consequential decisions: every test reveals information, consumes resources, and changes what should be done next. Yet diagnostic agents typically decide one step at a time, without explicitly considering how an action may shape the future diagnostic trajectory. We ask whether an agent can make better decisions by looking ahead before acting. To study this question in realistic clinical settings, we construct FISC, a Fast healthcare interoperability resources-based Interactive Sequential Clinical case dataset derived from 156 longitudinal real-world patient records, enabling standardized and interactive diagnostic trajectories. We then introduce SPARC, a Sequential Planning with Anticipated Risk and Cost agent, which formulates diagnosis as resource- and risk-aware planning under partial observability. Before executing a test, SPARC imagines its plausible outcomes and their downstream diagnostic trajectories, and uses stochastic AND–OR search to choose actions by jointly considering information gain, cumulative cost, diagnostic error, and the risk of missing critical disease. Following receding-horizon planning, SPARC executes only the selected next action and replans after observing its real outcome. Across real-world clinical cases, SPARC achieves diagnostic performance comparable to alternative sequential strategies while using fewer tests, and its behavior across planning depths reveals a trade-off between lookahead horizon, resource use, and diagnostic performance. These results suggest that diagnostic actions can be usefully evaluated not only by their immediate utility, but also by their anticipated downstream consequences.