Tahera Yesmin: Using AI to improve emergency room bed cleaning
Tahera Yesmin is a PhD candidate with the University of Toronto’s mechanical and industrial engineering department. Tahera was one of 10 U of T trainees selected to present her research that explores new ways that artificial intelligence can transform healthcare in the future. She will discuss her work in a public T-CAIREM Trainee Rounds presentation on April 13. We caught up with Tahera to learn more about her research.
What inspired you to pursue this field?
Solving real-life problems in healthcare with data-driven mathematical modeling has always been my interest. As with the technological advancement, there are a lot of data available in this field. I was motivated to pursue my research in machine learning and see how it could improve people’s lives by helping them with evidence-based decision making.
You were selected to present your research on the basis of a study you submitted to a panel of leaders in AI in medicine titled “A machine learning approach to predict the number of beds that will require cleaning and staff requirements in the emergency department.” What problem do you hope to solve?
When we’re sick, we go to the emergency department (ED) of a hospital. Depending on the sickness, patients need beds in the ED for their treatment. If there is a delay in getting a bed, there is be a delay in the process of treatment. One way to mitigate the delay in bed turnover processes is through the timely cleaning of dirty beds used by previous patients. My research is to know in advance when there will be a bed available to clean and how many cleaning staff will be required. This will help the hospital prepare enough clean beds for incoming patients and speeding up treatment.
What excites you the most about the possibilities of AI in healthcare?
I am thrilled to see the advancements of AI in the clinical side of healthcare. I am also excited to see how AI is transforming healthcare by addressing operational issues such as wait time and timely delivery of care with the help of the Internet of Things and machine learning-based interventions.
What do you see as the biggest challenge to the field of AI in healthcare right now?
From my perspective, I see three challenges: identifying correct and useful data from the vast collection, having enough experienced and insightful researchers to transform these data into meaningful use-cases, and all the privacy and ethical issues it has created.
What do you like to do when you aren’t working?
Despite the shortage of free time as a graduate student in an emerging field, I like to spend time in nature, read, and explore new cultural aspects such as celebrations, foods and arts. Being in Toronto, I am lucky to be able to have these experiences.
