Templating strategies specify policies on capacity allocation and appointment booking, which are central to patient access management in outpatient specialty practices (SPs). In the widely implemented partitioned templating strategy, appointment capacity is allocated exclusively to each patient group defined by a combination of patient attributes and medical conditions, which often results in utilization challenges for SPs. Motivated by problems faced by an SP within a large academic medical center, we propose partially partitioned templating strategies that cluster patient groups into access classes and allocate appointment slots to these classes. We formulate a twostage stochastic optimization model to simultaneously optimize decisions on patient group clustering and capacity allocation in the templating strategy design. We develop an efficient solution algorithm for the problem, which otherwise poses a combinatorial challenge, based on Benders' cuts and an anticipatory approximation. Numerical experiments using real-world data show that the partially partitioned strategies outperform benchmark strategies used by SPs through maintaining a balance of high capacity utilization and providing timely access for priority patients. In addition, we study a wide range of SP setups for transferable insights on templating strategies based on practice specifics.

In this paper, we present a parameter-free variation of the Sampled Fictitious Play algorithm that facilitates fast solution of deterministic dynamic programming problems. Its random tie-breaking procedure imparts a natural randomness to the algorithm which prevents it from "getting stuck" at a local optimal solution and allows the discovery of an optimal path in a finite number of iterations. Furthermore, we illustrate through an application to maritime navigation that, in practice, a parameterfree Sampled Fictitious Play algorithm finds a high-quality solution after only a few iterations, in contrast with traditional methods.

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