Key Healthcare Stats
DataRobot can help you with:
When patients are readmitted into hospitals after having just completed a treatment stay, the costs incurred by both the hospital and the patient are significant. Using DataRobot’s automated machine learning platform to predict and prevent hospital readmissions leads to the more efficient use of scarce hospital resources while improving the overall quality of care that patients receive.
ICU Utilization Prediction
High costs and periodic scarcity of critical care resources are two key reasons why ICU utilization must be improved. Using automated machine learning to accurately predict which patients truly need intensive care -- and which ones for whom ICU admission might not be necessary -- represents enormous cost savings for hospitals.
During hospital stays, patients are more susceptible to bloodstream infections, which is very costly because this often leads to hospital readmissions. Using DataRobot to predict which patients are more likely to contract sepsis or CLABSI triggers doctors to intervene by running additional diagnostics and testing, reducing the likelihood of patients being readmitted.
Patients with chronic diseases who don’t consistently take their medications lead to more than $100 billion in preventable costs annually. Using DataRobot to create models that identify the patients who are less likely to adhere to prescribed drug regimens, and predict the behavioral drivers, helps create the right intervention plan to decrease medication non-adherence.
Healthcare Hot Spotting
Five percent of the United States population accounts for nearly 50% of total healthcare costs in the country. Healthcare hotspotting -- segmenting big data sets to strategically target different pockets of need -- reveals extreme patterns in defined regions of the healthcare system. With DataRobot, healthcare hotspotting can be both more efficient and more accurate.
Fraudulent claims are costly, but it is too expensive and inefficient to investigate every claim. Using DataRobot, organizations automatically build accurate predictive models to identify and prioritize likely fraudulent activity, allowing for more effective deployment of resources and optimizing customer satisfaction.