A Sustainable, Paperless Environment Using Machine Learning in Hospitals and Medical Sectors

The work mostly focuses on making a paperless environment in the hospital and medical sector, considering Bangladesh’s situation. In the healthcare industry, the utilization of paper is expensive. Therefore, we endeavored to examine the prevailing dependence on paper and sought ways to minimize its usage. As a result, we identified several scenarios wherein the paper is predominantly employed and devised solutions to transform them into paperless processes through the implementation of web-based applications or other innovative techniques. We have also collected data from Bangladesh’s hospitals about their number of doctors, patients, and the use of papers. On that data, we have applied various machine learning techniques to find out whether the hospital should become paperless immediately or if they can take their time to become. It gives predictions about the cost-effectiveness of the sustainability of going paperless in hospitals and medical health sectors. We have used Random Forest Classifier, Decision Tree Classifier, Logistic naive Bayes, etc. to predict going paperless.