Working on fall prevention thanks to fall analysis and our fall register
Quality of care and better ageing are at the heart of Mintts values and the fall detection and prevention system we developed. Discover how fall analysis and our fall register can make it easier to work on fall prevention through data analysis.
Analysing
Registering
Preventing
Fall analysis - A better understanding of the circumstances around the fall
Our system can be used in two temporal modalities, allowing for optimal fall management. Firstly, the alert is sent immediately to the nursing staff at the time of the incident, where the priority is a rapid intervention to help the patient. Secondly, after the fall, our system allows an in-depth sequence analysis to understand the circumstances and prevent a recurrence.
Typically, 2/3 of falls are not reported or are not fully documented. Mintt offers analysis tools to fill this gap while ensuring the data remains complete. With our analysis system, we can provide detailed data on each fall, including information on causes, risk factors, and circumstances. This allows healthcare professionals to get a complete picture of the situation, making it easier to plan treatment and prevent future falls.
Once the patient has been managed after a fall, the important thing is to prevent a relapse. Healthcare professionals with granted access can analyse the footage generated by our artificial intelligence after each fall. They can organise multidisciplinary team meetings and collaborate to better understand the fall's circumstances. A clear link between the incident and the patient's health status can be established to personalise treatment plans and limit the risk of further falls.
This personalised approach ensures a better quality of life for patients and greater independence.
Fall register - An effective analysis tool
Our fall detection solution is designed to help care teams improve their practice by accurately identifying frequent causes of falls through a comprehensive and illustrated log. The artificial intelligence in our sensors produces a detailed report including all detected falls, with video sequences, so the quality manager, nurses, doctors, and occupational therapist can personalise patient care.
In addition, by automatically updating the fall register, our solution provides a precise statistical analysis grid: recording the number of falls, their frequency, intervention times, and consequences. This allows us to study the effectiveness of measures taken and better improve patients' quality of life as actions can be optimised proactively.