Tactical and operational decisions for operating room planning: efficiency and welfare implications

In this paper, we evaluate the impact on welfare implications of a 0-1 linear programming model to solve the Operating Room (OR) planning problem, taking a patient perspective. In particular, given a General Surgery Department made up of different surgical sub-specialties sharing a given number of OR block times, the model determines, during a given planning period, the allocation of those blocks to surgical sub-specialties, i.e. the so called Master Surgical Schedule Problem (MSSP), together with the subsets of elective patients to be operated on in each block time, i.e. the so called Surgical Case Assignment Problem (SCAP). The innovation of the model is two-fold. The first is that OR allocation is "optimal" if the available OR blocks are scheduled simultaneously to the proper subspecialty, at the proper time to the proper patient. The second is defining what "proper" means and include that in the objective function. In our approach what is important is not number of patients who can be treated in a given period but how much welfare loss, due to clinical deterioration or other negative consequences related to excessive waiting, can be prevented. In other words we assume a societal perspective in that we focus on "outcome" (health improving or preventing from worsening) rather than on "output" (delivered procedures). The model can be used both to develop weekly OR planning with given resources (operational decision), and to perform "what if" scenario analysis regarding how to increase the amount of OR time available for the entire department (tactical decision). The model performance is verified by applying it to a real scenario, the elective admissions of the General Surgery Department of the San Martino University Hospital in Genova (Italy). Despite the complexity of this NP-hard combinatorial optimization problem, computational results indicate that the model can solve all test problems within 600 s and an average optimality tolerance of less than 0.01%.