Many would contend that the United States healthcare system should be moving towards a state of health equity. Here, every individual is not disadvantaged from achieving their true health potential. However, a variety of barriers currently exist that restrict individuals across the country from attaining equitable health outcomes; one of these is the social determinants of health (SDOH). The SDOH are non-medical factors that influence the health outcomes of an individual such as air pollution, food insecurity, and transportation accessibility. Each of these factors can influence the critical illnesses and health outcomes of individuals and, in turn, diminish the level of health equity in affected areas. Further, the SDOH have a strong correlation with lower levels of health outcomes such as life expectancy, physical health, and mental health. Despite having influenced the United States health care system for decades, the industry has only begun to address its influences within the past few years. Through exploration between the associations of the SDOH and health outcomes, programming and policy-making can begin to address the barrier to health equity that the SDOH create.

The objective of this study is to build a model using R and RStudio that automates ratemaking procedures for Company XYZ’s actuaries in their commercial general liability pricing department. The purpose and importance of this objective is to allow actuaries to work more efficiently and effectively by using this model that outputs the results they otherwise would have had to code and calculate on their own. Instead of spending time working towards these results, the actuaries can analyze the findings, strategize accordingly, and communicate with business partners. The model was built from R code that was later transformed to Shiny, a package within RStudio that allows for the build-up of interactive web applications. The final result is a Shiny app that first takes in multiple datasets from Company XYZ’s data warehouse and displays different views of the data in order for actuaries to make selections on development and trend methods. The app outputs the re-created ratemaking exhibits showing the resulting developed and trended loss and premium as well as the experience-based indicated rate level change based on prior selections. The ratemaking process and Shiny app functionality will be detailed in this report.