title: Understanding the effects of pharmaceutical promotion: a neural network approach guided by genetic algorithm-partial least squares.
abstract: With escalating healthcare costs and increasing concerns about optimizing use of medicine, there is an unresolved debate over years around the potential impact of pharmaceutical promotion on physicians' prescribing behaviors. What should be the appropriate balance of promotion dollars to physicians? We use three major brands in the US antibiotic universe to explore this issue, presenting a theoretical framework for better understanding the cause-and-effect relationship between common promotional spending and prescription responsiveness. Using simulations we demonstrate that neural networks guided by genetic algorithm-partial least squares is able to provide managers with better understanding of physicians' prescribing activities without an appreciably lower predictive accuracy when compared to that obtained by a standalone neural network modeling.
authors.affiliation: International Business Department, Daiichi Sankyo Co., Ltd., 3-14-10, Nihonbashi, Chuo-ku, Tokyo 103-8234, Japan lim.cheewooi.ae@daiichisankyo.co.jp
authors.forename: Chee Wooi
authors.initials: CW
authors.lastname: Lim
authors.forename: Toru
authors.initials: T
authors.lastname: Kirikoshi
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents; D006801:Humans; D040541:Marketing; D016571:Neural Networks, Computer; D010818:Practice Patterns, Physicians'
chemical_list: D000900:Anti-Bacterial Agents