Resource allocation for healthcare organizations

Executives of hospitals, specifically, privately managed hospitals in developing countries are facing tremendous pressure from the stake-holders to adopt state-of-the art revenue management strategies, in order to justify return on their investments (RoI). On the other hand, the hospital management face the challenge of providing quality and safe health-care services to all of their patients from both within and outside the country (demand) with limited resource capacity (supply). Revenue from surgeries account for about 40% of the total revenue in most of the hospitals. In this research, we consider surgical demand from elective surgeries and propose a two-phase method for the allocation of resources (supply) to surgeries (demand). The first phase, allocates resources to all the surgical requests. Second phase re-allocates surgeries based on competitive bidding of the surgical requests. Resource allocation is the distribution of limited resources (capacity) among competing consumers (or a firm0s customers). One way of fair allocation of resources to the consumers is through auctions. For the second phase, we develop a model for optimal allocation of multiple resources like operating rooms (ORs), nurses, equipments, and so on; to the surgical demand of hospitals through auctions. The various resources of hospitals like ORs and nurses, are represented as factors or characteristics of a generalized resource. A resource (generalized) is a realization of factors like ORs, nurses, and equipments. That is a resource (generalized) is a specific set of nursing staff and equipments being assigned to a specific OR in a specific time-slot. Optimally allocating resources to various bids (surgical requests) and scheduling the surgical requests to various ORs without any conflict on any given time-slot of a day is an NP-hard problem. The proposed model optimally allocates client (practitioners or agents) bids to the available capacity of resources (generalized). We analyse the application of the model for a business scenario. © 2011 IEEE.