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Clinical pharmacy has a rich history of advancing practice through innovation. These innovations helped to mold clinical pharmacy into a patient-centered discipline recognized for its contributions to improving medication therapy outcomes. However, innovations in clinical pharmacy practice have now waned. In our view, the growth of academic–practice partnerships could reverse this trend and stimulate innovation among the next generation of pioneering clinical pharmacists. Although collaboration facilitates innovation,academic institutions and health care systems/organizations are not taking full advantage of this opportunity. The academic–practice partnership can be optimized by making both partners accountable for the desired outcomes of their collaboration, fostering symbiotic relationships that promote value-added clinical pharmacy services and emphasizing continuous quality improvement in the delivery of these services. Optimizing academic–practice collaboration on a broader scale requires both partners to adopt a culture that provides for dedicated time to pursue innovation, establishes mechanisms to incubate ideas, recognizes where motivation and vision align, and supports the purpose of the partnership. With appropriate leadership and support, a shift in current professional education and training practices, and a commitment to cultivate future innovators, the academic–practice partnership can develop new and innovative practice advancements that will improve patient outcomes.

We present an approach to the automatic construction of decision procedures, via a detailed example in propositional logic. The approach adapts the methods of proofdplanning and the heuristics for induction to a new domain, that of metatheoretic procedures. This approach starts by providing an alternative characterisation of validitys the proofs of the correctness and completeness of this characterisation, and the existence of a decision procedure, are then amenable to automation in the way we describe. In this paper we identify a set of principled extensions to the heuristics for induction needed to tackle the proof obligations arising in the new problem domain and discuss their integration within the clamdOyster system.