OPTIMAL aims to optimise hospital patients’ follow up processes, especially during the first month after discharge, with a clear goal to reduce readmissions by 5% within the first year of its installation and improve the process by means of patient satisfaction. 
It devises an intelligent and structured follow up process driven by a software platform which aids in the adherence and enforcement of post-discharge guidelines. The anonymized data from over 100,000 annual attendees were analysed in order to build a readmission risk prediction model, establish patterns in the admission of frequent attenders and identify the most common factors leading to readmission for similar patient profiles.
OPTIMAL mainly aims at prediction of probability of readmission, automation of task management for the nurses and general process improvement for all healthcare personnel based on patient data analysis. By contacting patients after the hospital discharge, nurses can provide healthcare service, and track each case. The tracked data, after analysis, can help identify critical points in the healthcare process having as a goal to improve services and decrease re-admissions.
Currently OPTIMAL is in its trial phase. The software platform supports NHS staff and patients of Croydon Hospital having already contributed to a 10% reduction in emergency readmission based over a 6-month period on a year over year comparison.


OPTIMAL uses a prediction algorithm to identify the best intervention candidates and keeps track of all actions that follow discharge so that all information is available to all stakeholders. The result is a reduction of avoidable readmissions, better bed utilisation and use of emergency care resources. 


Kingston University, Croydon NHS (United Kingdom)

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