Diagnostic problem solving involves a myriad of reasoning tasks associated with the determination of diagnoses, the generation and execution of tests to discriminate diagnoses, and the determination and execution of actions to alleviate symptoms and/or their root causes. Fundamental to diagnostic problem solving is the need to reason about action and change. In this work we explore these myriad of reasoning tasks through the lens of artificial intelligence (AI) automated planning. We characterize a diversity of reasoning tasks associated with diagnostic problem solving, prove properties of these characterizations, and define correspondences with established automated planning tasks and existing state-of-the-art planning systems. In doing so, we characterize a class of planning tasks with epistemic and ontic goals which we show can be compiled into non-epistemic planning, allowing stateof-the-art planners to compute plans for such tasks. Furthermore, we explore the effectiveness of using the conditional planner Contingent-FF with a number of diagnostic planning tasks.