Risk aversion is natural when planning for healthcare services. Surgery planning and assigning example will be used to illustrate co-advancement in chance-constrained optimization research to allow consideration of risk-aversion in operational planning problems. We will show the implications of robustness towards out-of-sample risk aversion, and problem solvability when a limited number of data samples are available. We will also see how the classical cut-generation theory is extensible to the more general settings of robust chance constraints.