AI for health: The augmented caregiver in the hospital of Chalon-sur-Saône
About this good practice
The lengthening of life expectancy and the chronicization of pathologies are changing the way patients are cared for in urban medicine and in hospitals. The challenge for health organizations is to respond effectively to the health needs of a population, in current and exceptional situations, by preserving the quality of care provided by available caregivers despite the increase in the care workload. Digital technology is a booster for transformation in healthcare institutions, the massive collection of data by connected medical devices, enhanced by artificial intelligence tools, improves the care pathway and hospital performance management. The advantages of this solution are for:
- patients: the evaluation of the patient based on objective criteria, highlighting the evolution of the clinical situation from the first signs of deterioration
- caregivers: a simplification of the constant taking, a reduction in the workload, the mental load and the data input.
The focus is on more fragile patients. The aggregation of certain parameters is at the origin of a predictive score of the patient's degradation transmitted to the caregiver.
AI will be soon deployed but several preliminary actions are necessary before integrating the AI system: renew the digital infrastructure of the hospital, data collecting, access and standardization, determine all the ethical and medical protocols of alert system for caregivers and AI and testing.
Resources needed
Till now the total budget is 4,7 million €. The experimentation is funded by the BFC Region with 2,4 million € from which 1,4 million € is ERDF.
Currently, the project occupies about 5.8 full-time workers, not including the healthcare resource who contributes to build, test and deploy the solution.
Evidence of success
At the moment the augmented caregiver is being tested. The tool is very much demanded by caregivers because it will help them to reduce their mental load and have less stress at work.
The assessment methodology will be based on statistics (length of stay in hospital) and patient/caregiver satisfaction. Today there are about 50 heart attack / year. The aim is to prevent 25-50% of the heart attacks in the hospital.
Potential for learning or transfer
The main potential learning lesson of the good practice of the hospital center of Chalon-sur-Saône is that implementation of a robust AI project cannot be done without working previously on the:
- Reorganisation and securing of the technical infrastructure,
- Anonymisation, secured stockage and collection of structured data,
- Social adaptation and managing long-term change, dissemination of trainings,
- Necessary human and financial resources.
Regarding the transfer of this good practice, using digital tools to monitor patient health, integrating AI for predictive care, and reducing caregiver burden through automation, can be applied to other healthcare settings. These improvements streamline processes, improve care quality, and address the growing demand on healthcare systems, which can benefit hospitals in various regions.
