Predicting Hospital Readmissions

Preventing Readmissions with Data Analytics and AI

Goal

Strategic Advantage –> Better/Personalized Treatments –> Customer Satisfaction –> Increased Revenue and ROI

Challenges

Patients with serious and chronic diseases are hospitalized for a period and then being discharged. Unfortunately, multiple studies have shown that up to 25% of these patients will be readmitted within 30 days to be treated again, in some cases with less favorable outcomes. Focusing on value-based care, healthcare providers try to prevent unnecessary readmissions and improve patient care outcomes. Readmission can be significantly reduced by taking steps while the patient is still in the hospital, defining different actions during discharge, and taking steps post discharge to ensure compliance with home care regimens.

Opportunities

AI is ideally adapted in tasks where data inputs are rather complicated and may elude clinicians. In projecting Readmissions risk data are required about the specific patient’s recent care, their current condition, treatment, their home life and other risk factors from patients’ electronic medical records. AI models use this information to provide preemptive assessment of their risk and notify clinicians while the patient is still hospitalized. AI provides reasons that might lead to readmission and provide recommendations for the most likely types of successful treatments according to a patient’s history. The reason codes are valuable to clinicians because they can pinpoint precise areas to focus on when developing a care plan for the patient, thus preventing unnecessary and costly tests.
Skip to content