Efficient Abstraction of Healthcare Data for Real-Time Decision Making Using Logistic Regression and Linear Regression

Data analytics can help drive decision-making and improve patient outcomes in healthcare. But of course, healthcare data is complex, and the process to access & analyze it can be slow. Here, researchers suggest a way to generalize healthcare data at scale and do so quickly enough for real-time decision support. Logistic regression is used to categorize patient data into various groupings using established standards, which could create a targeted patient population that is at high risk. We then perform linear regression on patients' demographics and medical history to predict their outcomes. This information can help with real-time decisions for patient care. Our method will decrease the time and cost of collecting/analyzing healthcare data for faster decision-making. This is particularly crucial in a rapidly moving healthcare environment where every second counts. Also, logistic and linear regression techniques have been used together to perform a complete data analysis so that the predictions are as accurate as possible. The methodology allows us to effectively abstract healthcare data for healthcare providers' (HCP) real-time decision-making. This process can deliver advances in patient care and efficiencies in effectuating decisions-and it could provide immense benefits across healthcare.