Prediction of Hospital Readmissions

Rates of readmission at hospitals are a critical measure of their service levels and have a substantial impact on their costs. Yet, there haven’t been any easy-to-implement, accurate solutions for doctors and managers.

ROSE by Amalfi Analytics predicts, for each patient, their readmission risk using structured data that the hospital already has, enabling saving of resources and allowing for an improvement on quality of treatment.


  • Efficient use of resources to allocate additional resources to patients with higher risk of readmission.
  • Improvement of quality indicators, such as patient safety and efficient use of resources.
  • Immediate cost reduction due to the improved resource allocation and reduction of readmission levels.
  • Learning based on your organization’s data: our Machine Learning model provides realistic and accurate results for your organization.
  • Easy implementation, as it uses existing data in your data warehouse.


  • Single-center system.
  • Patient by patient prediction of readmission risk.
  • Permanently updated thanks to our Machine Learning algorithm, which adjusts its predictions continuously based on actual data from each center.
  • System alerts for patients with high risk of readmission.
  • Record of corrective actions applied, which are also used for future predictions.
  • Exportable reports and API for HIS integration.
  • Data privacy guaranteed, including EU’s GDPR compliance.

Product datasheet

Prediction of Hospital Readmissions – ROSE