Intelligent scheduling for radiology facilities with continuous learning [Dynamic Resources]

With the help of AI and machine learning algorithms, we optimize scheduling and resource utilization in radiology to increase efficiency and precision. Continuous learning is used to predict potential patient delays and appointment adherence for optimal resource utilization. Over time, the models are adapted by continuous learning of new events and thus increasingly accurate predictions are made. The reliability of planning also depends on unpredictable factors such as environmental influences, traffic conditions or employee absences, which can be minimized through real-time monitoring and adaptive adjustment of planning. Real-time monitoring can help to adapt planning adaptively with the help of learned prior knowledge.

Project goals:
✔ Intelligent appointment management: Optimization of appointments and resource deployment by linking deployment and appointment planning with intelligent algorithms, Informatics-SYSTEMS GmbH.
✔ Predicting adherence to schedules and delays: Using data analytics and AI to make scheduling more accurate and flexible, Biosignal Processing Group.
✔ Voicebot and chatbot: Implementation of a natural language interface for simplified appointment booking, rescheduling, cancelation and reminders by phone, messenger or chat on the website, alphaspeech.

Further information on the website of the Biosignal Processing Group.

This project is co-funded by the European Union and co-financed from tax revenues on the basis of the budget adopted by the Saxon State Parliament.