Impact of communication on adoption of prescription drugs developing a decision model for Indian healthcare industries

Abstract

ABSTRACT newlineThe need for quality healthcare outcomes remains robust in emerging economies. Over the years, treatment efficiencies in India have not been impressive, no doubt, as reflected in India s poor healthcare outcomes. Most of the past researches on drug adoption have focused on the perceived environment of the supplier for the development and diffusion of new drugs. The status of late-entrant generic drugs (LEGD) has been largely overlooked, allowing such drugs to find their way into the supply chain on a hit-or-miss basis. Received wisdom suggests that the healthcare supply chain in India has not been responsive either for the providers (drug-marketers and doctors) or for consumers. The question which needs to be answered is whether doctors (i.e., medical practitioners ) understand the communication nuances of drug-marketers to treat their patients with quality drugs efficiently. There is also a dearth of studies on communication challenges faced by Indian medical practitioners working in private hospitals, treating both local and foreign patients. In view of the importance of the subject and paucity of research in the field, this study develops an action-based research model drawing upon pharmaceutical firm managers to produce data and findings relating to communication challenges as these affect how drug adoption in India occurs in comparison with the global drug adoption phenomenon, especially for treating non-communicable diseases like hypertension. This action-based research identified the factors involved in drug adoption and their interrelationships with an newlineemphasis on diverse sources of medical information, as these factors influence prescription decisions that characterize the healthcare performance of the nation. newlineDuring the first phase, a meta-analysis on data from two thousand patients was performed using a classification-based association technique with data mining principles, which reveals a new pattern of prescription decision. Patient characteristics were found to play an insignificant r