We present a pilot study of an annotation schema representing problems and their attributes, along with their relationship to temporal modifiers. We evaluated the ability for humans to annotate clinical reports using the schema and assessed the contribution of semantic annotations in determining the status of a problem mention as active, inactive, proposed, resolved, negated, or other. Our hypothesis is that the schema captures semantic information useful for generating an accurate problem list. Clinical named entities such as reference events, time points, time durations, aspectual phase, ordering words and their relationships including modifications and ordering relations can be annotated by humans with low to moderate recall. Once identified, most attributes can be annotated with low to moderate agreement. Some attributes - Experiencer, Existence, and Certainty - are more informative than other attributes - Intermittency and Generalized/Conditional - for predicting a problem mention's status. Support vector machine outperformed Naïve Bayes and Decision Tree for predicting a problem's status.