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The project has aimed to achieve an intelligence and prediction system that allows for decision-making tools in possible critical situations or simply to optimize and enhance the utilization of available resources.

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The CT Solutions team has participated in the project “Hospitals 4.0: Machine Learning for more effective management,” which has been awarded the Best Intercluster Collaborative Project and Best Disseminated Project along with the Clúster Salut Mental Catalunya at the X Congress of Clusters and Innovative Business Associations organized by Mincotur.

This project has aimed to achieve an intelligence and prediction system that provides decision-making tools for possible critical situations, or simply to optimize and improve the use of the available and limited management resources to address the current situation in the Hospital sector and meet its requirements.

Additionally, the “Hospitals 4.0” project included the implementation of a pilot in a real facility, the Pulmonary Space at the Hospital de Terrassa, where the compatibility and functionality of the developed system were validated.

CT Solutions has created a “digital twin” to accurately represent its performance in a simulated scenario, in order to prepare for possible real situations and anticipate future actions, while collecting data on system usage and wear as if it were reality. This digital twin is connected to artificial intelligence algorithms, which use the data obtained through simulation for predictive maintenance through Machine Learning.

At the same time as pursuing the primary objective of the project, the following additional objectives have been achieved:

• Cost reduction: by improving the efficiency of hospital equipment usage.
• Operational improvement: by minimizing delays in periodic operations caused by incidents requiring urgent resolution, evaluating various scenarios to be considered, etc.
• Risk mitigation: by optimizing facility operation and usage, as well as determining if preventive maintenance is correct and optimal for the equipment.
• Understanding system interactions: through digital twins.
• Fine-tuning prediction: Machine Learning allows for failure prevention and improvement of system operation.
• Improving patient care capacity: through a failure prediction mechanism to avoid equipment unavailability.
• Predicting the need for additional personnel and resources (medical equipment, medications).

This project has been led by Smartech Cluster and involves the participation of Clúster Salut Mental Catalunya, Universitat Politècnica de Catalunya, CT Solutions, BIM6D Consulting & Performance, and Consorci Sanitari de Terrassa It has been financed through the support grants for Innovative Business Associations(AEI) from the Ministry of Industry, Commerce, and Tourism.

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Communications Department