As emids is a US-based Healthcare IT solution provider company, going through the company’s products, it’s competitors would have an added advantage in the interview. Also, please go through, use of Analytics in Healthcare and recent developments after the COVID 19 pandemic and how is AI tackling the problems of payers and providers by helping them in making data-driven decisions.
My interviewer was a Lead Data scientist at emids and the interview lasted for around 30 minutes. Initially, questions were about my profile, my previous background, and past work experience. Then he
gave me a case study about state election result prediction and asked about how I would approach the case study and what should be the sampling technique, how to build a model, and what should be the parameters for the evaluation, etc. Then the interview took a slightly technical turn and questions were asked about the interpretation of the evaluation criteria, Type I error and Type II errors, then he gave me some time to prepare a small story and present the technical results in layman’s terms. A fair amount of technical knowledge about regression, classifications, and the evaluation metrics should be enough but having knowledge about ensemble techniques and Neural networks is an added advantage.
3 months of the continuous learning experience with the COVID-19 pandemic devastating the entire world, emids were helping the U.S. healthcare payers and providers in the digital transformation process. I was lucky enough to intern at emids during such critical times where I worked on two predictive analytics use cases. The first one was about the optimization of hospital infrastructure planning by building a machine learning model that can accurately predict the length of stay (LOS) of patients at the hospital before admission. With the help of accurate LOS prediction, hospitals can better plan their staff, bed allocation doctor visits. Also, this helps insurance companies (payers) to have a better idea about the amount that a patient might claim for the treatment. With people struck at homes due to lockdowns for a prolonged period of time, mental health was a major concern so we tried to address this problem in another use case by predicting the risk of severe mental illness based on multiple factors. Data for both use cases were in raw format and couldn’t be directly used to build the model, so first, we cleaned the data, imputed the data for the missing values. Then we carried out the exploratory data analysis to find out the hidden insights in the data. Feature engineering was done using the principle component analysis. Multiple models were built on the data to check which algorithm would better predict the results. Models were later improved using hyperparameter tuning techniques. In the end, use cases were presented to higher management and they were satisfied with the use cases. Luckily interviewer was my mentor and he helped me throughout my stint at emids from Data collection till my presentation to higher management. Also under his guidance, I could complete multiple
machine learning certifications. In simple it was a great learning experience.