Healthcare industry is an ever-emerging field in the 21st century. The statistics from Centers for Medicare services (CMS) [15] website shows that in 2017, in USA, the healthcare industry has invested USD 7.4 billion for research collaborations with physicians. These research collaborations in CMS is spread across multiple facets ranging from contributing towards research, developing new products, running clinical trials, royalty, licences, patents, providing innovative ideas etc. In this thesis, we make an assumption that, a relationship between investment made by the healthcare company and the research profile of a physician exists. We aim to answer, what could possibly be the relation between payments made by the healthcare company; on the physicians and the research profile of the physicians. The research profile of a physician includes factors like h-index, years of research experience, citation count, physician citation network, etc. To validate this relationship we use the data corresponding to returns of the healthcare company. Some of the measures of returns, from a research collaboration between physicians, include, innovation, good will, fame, market share, Return on Investment (ROI). We choose ROI, as a measure of return, due to the availability of data and to determine the relationships mentioned above. To understand the above mentioned relationship, we explore two types of relationships, i.e., direct and indirect relationship. In the direct relationship, we use multiple regression model to understand the direct relationship between the research payments and the research profile of the physicians, by making an assumption that the research profile of the physician describes the research quality of the physician. In the indirect relationship, we make use of a weighted physician co-author citation network, to investigate the relationship between his/her co-author interactions and the research payments he/she received from the healthcare company. To accomplish this, we developed a spreading process that models influence diffusion in a physician citation network. The diffusion of influence is dependent on the topological property of the node in the network. Our models are an exemplification of the direct and indirect relationships, which exists in the real world. To evaluate our models, we use metrics such as coefficient of determination, Pearson correlation coefficient and Spearman's rank correlation. Once the models were evaluated, we inferred that the model for indirect relationship, explains the relationship between research profile, investments, and return 96.3% more than the model for direct relationship. We also perform a deep analysis, by investigating the nature of the distributions of the variables and scatter plots to understand the relationship between the variables used in understanding the direct and indirect relationship. Lastly, we propose two different redistribution methods, where the original payments made to physicians are redistributed to a potential group of physicians in the physician citation network. These potential physicians are identified based on their topological property. In consequence, our redistribution methods may inspire the healthcare companies, to design their future investments made to physicians.