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Prediction of postoperative infections


Description:

Infections hit one in four patients, yearly more than 10 million in Europe, after their surgery. On average only after day 5 the the infection is diagnosed with a maximum accuracy of 70% (through biomarkers, vital signs, medication use, past medical history and pre-op risk score) and treatment started. Infections more than double hospital stay and oftentimes necessitate additional invasive treatment, lowering the patient’s quality of life. The cost of treating a post-operative infection is on average €10,000 per patient. Early or even timely recognition of infections fails in the current system. The project team's vision is to disrupt the current post-operative work processes and protocols in hospitals by providing a unique software tool that uses machine learning (ML) algorithms to enable a shift from a diagnostic and responsive system towards a predictive and proactive system. This tool to predict post-surgery infections is based on electronic health records (EHR) data and will be accurate, transportable, robust, scalable, easy-to-use and integratable into existing hospital workflows and IT infrastructures. This can potentially significantly lower the number of infections, hospital readmissions and length of stay (LoS) of a patient, leading to lower costs, a relieve on the workload of hospital staff and a higher quality of life for patients.


Project leader: Robert Jenssen

Started: 2019

Ends: 2021

Category: Universiteter

Sector: UoH-sektor

Budget: 3070000

Institution: UNIVERSITETET I TROMSØ - NORGES ARKTISKE UNIVERSITET

Address: Tromsø