Predict Hospital Readmission
Predict better the likelihood of readmitting a patient by improving the accuracy of the existing prediction models using EvoML's model optimiser.
Healthcare systems around the world are under constant pressure to provide high quality care while keeping costs low. Hospital readmission rate is an indicator of hospital quality negatively impact the care cost. In the UK alone, statistics show that readmission to hospital within 30 days of discharge can cost the NHS around 1.6 billion a year. Furthermore, readmission places patients at higher risk of hospital acquired infections and impacts hospital bed availability. Reducing readmission therefore is one of the top priorities for healthcare providers.
How can TurinTech help?
Using TurinTech, healthcare providers can automatically create highly accurate AI models to determine and understand which patients are at risk of being readmitted, while still in hospital. They can then prioritise these patients and implement the necessary measures to avoid readmission. The models can also recommend the most effective treatment plan according to their risk level, thus minimising cost by avoiding prolonged stays and further testing. Furthermore, for readmission to stay low, after-discharge care is extremely important. TurinTech models go beyond inpatient care to also suggest the most appropriate post-discharge interventions.
By being able to control the patient journey, from initial hospital stay to post-discharge, practitioners are in a better position to offer high quality care and avoid extra costs.
- • Predictive readmission risk assessment: determine which patients are more likely to return, while they are still in hospital
- • Efficient treatment planning: avoid extra costs by knowing on time which treatment works best for a particular patient
- • Improve patient care: offer high quality care by implementing the right measures along the patient’s clinical pathway