7:30 - 8:00 PM - A Medical Educato(R)’s Journey to Data Science: Residency Applicants Ranking Dashboard and Algorithm – From Open Concept to Open Reality

Ken Koon Wong

How can we rank interview candidates more fairly? What form of data is needed to make that decision? How do we curate that data? How do we compile and summarize noisy data into something interpretable? How can we incorporate an algorithm that minimizes bias in recruitment? These questions are relevant for making an informed decision in recruiting candidates to be trained as future physicians. At the Cleveland Clinic Akron General Internal Medicine residency program, we have used the R Shiny dashboard for over three years to make recruitment more diverse, equitable, and inclusive. It would be very challenging for the human eyes to notice subtle differences in data for 100 to 200, or sometimes an even greater number of interview candidates, given 6 to 10 variables per candidate. We used multiple-criteria decision analysis (MCDA) as a potential solution to our question. The R Shiny dashboard is highly customizable, allows individualized program formula derivation with a chosen weight that matters most to the program’s core value, is easily accessible for program leadership to look at curated candidate assessment data, minimizes bias, and increases diversity in ranking, and provides another quantitative tool to tune PD’s intuition for ranking candidates. Most importantly, the R Shiny dashboard allowed the program leadership to visualize noisy data to enhance the ranking experience.

He is an Associate Program Director of Internal Medicine residency program and Infectious Disease physician at Cleveland Clinic Akron General, Ohio, USA. He is a Data Science hobbyist and has been an R convert since late 2019, all because of a question during a meeting, “How can we make sense of all these numbers?”. He has learned R from online tutorials, uses R daily, and has built several dashboards and automation tasks for better efficiency and learning. He is also passionate about using experiential learning to improve data literacy. For example, he experienced probability theory by dedicating 2022 to randomly buying his wife ~24 bouquets, which is estimated to be a ~6.6% chance per day. To his surprise, there were several occurrences of back-to-back purchases of flowers. He enjoys no-till gardening, practicing Tai chi, and learning.
Sun 1:50 am - 12:00 am
Case Study
Ken Koon Wong