A Markov Sensitivity Model for Examining the Impact of Cost Allocations in Hospitals

The cost allocation process in hospitals typically entails an accounting step-down procedure whereby costs are allocated from non-revenue producing service centres to revenue centres. The resulting revenue centre costs are then compared with the third party (Blue Cross, Medicare, Medicaid) allowable costs. Any costs in excess of the allowable costs are not reimbursable. This procedure has been conceptualized using a Markov chain in a recent journal article. The purpose of this paper is to demonstrate how the Markov model may be used to assess the impact of various changes in the original data without having to recalculate the entire step-down process via a Markov model or any other procedure. The changes include an alternate step-down model, a different cost allocation basis for one or more service centres, and the expansion or contraction of one or more service centres.     

Differential inhibition of postnatal brain,spinal cord and body growth by a growth hormone antagonist

Background  

Growth hormone (GH) plays an incompletely understood role in the development of the central nervous system (CNS). In this study, we use transgenic mice expressing a growth hormone antagonist (GHA) to explore the role of GH in regulating postnatal brain, spinal cord and body growth into adulthood. The GHA transgene encodes a protein that inhibits the binding of GH to its receptor, specifically antagonizing the trophic effects of endogenous GH.     

Linear programming with multiple fuzzy goals

This paper describes the use of fuzzy set theory in goal programming (GP) problems. In particular, it is demonstrated how fuzzy or imprecise aspirations of the decision maker (DM) can be quantified through the use of piecewise linear and continuous functions. Models are then presented for the use of fuzzy goal programming with preemptive priorities, with Archimedean weights, and with the maximization of the membership function corresponding to the minimum goal. Examples are also provided.