Challenges

Patients with serious and chronic illnesses are treated in the hospital and then discharged. Unfortunately, according to multiple studies, up to 25% of these patients will be readmitted within 30 days to be treated again, often with less favorable outcomes. With a focus on value-based care, providers are trying to prevent unnecessary readmissions and improve patient care outcomes. Readmission can be significantly reduced by taking steps while the patient is still hospitalized, defining different actions during discharge, and taking steps post discharge to ensure compliance with home care regimens.

Opportunity

AI is ideally suited to tasks where the data inputs are complex and may elude clinicians. Readmissions risk prediction can require data about the specific patient’s recent care, their current condition, treatment, their home life and other risk factors from electronic medical records. AI models can use this information to provide a proactive assessment of their risk and notify clinicians while the patient is still hospitalized. AI can provide the reasons that will lead to readmission and also provide recommendations for the types of treatments that are most likely to be successful given the patient’s history. The reason codes are valuable to clinicians because they can pinpoint areas to focus on when developing a care plan for the patient and prevent unnecessary and costly tests.