Applying the DMAIC Cycle and Machine Learning to Examine COVID-19's Effects on Emergency Department-LOS

Emergency Department Length of Stay (LOS) over time, a methodology known as Lean Six Sigma (LSS) has garnered popularity in the healthcare industry, originating from industrial practices. Its tool, the DMAIC cycle, comprising five main components, offers methodological rigor by comparing quantitative results to aid in process improvement. This study examined the effect of COVID-19 on patient length of stay (ED-LOS) in the emergency department of Penisola Hospital, utilizing LSS, specifically focusing on the DMAIC cycle. Moreover, Machine learning models including Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbors (KNN) were used to forecast the length of stay (ED_LOS).