Special thanks are due to Dr. Amit Diwakar, Dr. Jeffrey Solomon, and Jennifer Hayes for their contributions in making this project a resounding success and continuously striving for improvement.

I had the pleasure of presenting our project at ShinyConf 2023 sponsored by Appsilon, “A Medical Educato(R)’s Journey to Data Science: Residency Applicants Ranking Dashboard and Algorithm,” which revolutionized our approach to ranking residency applicants. We sought to address the challenge of ranking candidates more fairly by curating and analyzing complex data. By utilizing the highly customizable R Shiny dashboard and implementing multiple-criteria decision analysis (MCDA), we transformed the recruitment process at the Cleveland Clinic Akron General Internal Medicine residency program. The dashboard provided program leadership with accessible and curated candidate assessment data, minimized bias, increased diversity in rankings, and offered a quantitative tool to enhance decision-making. Visualizing noisy data through the dashboard enhanced our ranking experience, enabling us to make informed decisions with ease.

Our project exemplifies innovation and a paradigm shift in recruitment practices. By embracing the power of data science, we aimed to foster diversity, equity, and inclusion in selecting future physicians. Through the R Shiny dashboard, we harmonized program values with individualized weights for variables, while the MCDA framework facilitated fair decision-making. This transformative journey not only empowered program leadership but also inspired others to explore data science’s potential in creating a brighter and more equitable future in medical education and recruitment. Together, we can leverage the transformative power of data to shape a tomorrow where fairness, diversity, and excellence thrive.

Special thanks are due to Dr. Amit Diwakar, Dr. Jeffrey Solomon, and Jennifer Hayes for their contributions in making this project a resounding success and continuously striving for improvement.

Disclaimer: The presented data is fabricated for illustrative purposes only. We showcased the framework for assessing candidates at a granular level.

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