Antibiotics have contributed to the decline in mortality and morbidity caused by infections, but overuse may weaken effectiveness resulting in a worldwide threat. Antibiotic overuse is correlated with adverse events like Clostridium difficile infection, antimicrobial resistance, unnecessary healthcare utilization and poor health outcomes. Long term care facility (LTCF) residents are vulnerable targets for this phenomenon as antibiotics are one of the most commonly prescribed medications in this setting. Consequently, multiple organizations mandate strategies to promote antibiotic stewardship in all healthcare sites particularly LTCFs.

To address this global issue, this doctoral project utilized the Outcomes-Focused Knowledge Translation intervention framework to provide sepsis education, promoted use of an established clinical algorithm, and engaged a communication tool for nurses and the certified nursing assistants (CNAs) thus, improving antibiotic stewardship. The project was conducted in a 5-star Medicare-rated LTCF in Mesa, AZ with a convenience sample of 22 participants. The participants received a knowledge questionnaire and Work Relationship Scale pre- and post- intervention to determine improvement.

The results show that the education provided did not improve their knowledge with a p = 0.317 for nurses while p = 0.863 for CNAs over 8 weeks. Lastly, education provided did not improve the nurses’ Work Relationship p = 0.230 or for the CNAs p = 0.689. Though not statistically significant, the intervention tools are clinically significant. Additional research is needed to identify ways to determine barriers in implementing an antibiotic stewardship program.

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.