Brussels-based Kaspard is a non-intrusive system that – in case of fall or too long bed exits by an elderly person – provides real-time information to nurses in institutions. In a recent development, the company secured €3.1M in a fresh round of funding.
Investors and use of the capital
The round saw participation from existing investors, including Finance&Invest.brussels, ScaleFund, and Theodorus IV. In addition, two new investors, Marcel Hermann and Emmanuel Caeymaex, also invested in the current round.
Marcel Hermann is the former owner of Medipôle, a group of private clinics in France, and Emmanuel Caeymaex, a private investor, is a member of the UCB Executive Committee in charge of Immunological Solutions in the US.
Emmanuel Caeymaex says, “Falls in the elderly are associated with a considerable human and economic cost. I am joining Kaspard’s Board to help reduce these costs through prediction and prevention.”
With an aim to better support the elderly, the proceeds from this round will enable Kaspard to continue its commercial development, strengthen its team, and develop new features.
The challenge
According to Kaspard, fall is the leading cause of accidental death for the elderly. One in three falls by age 65 and one in two above 75. And this leads to the fear of falling. Even the activity by the elderly becomes limited as after the first fall, the risk of recurrence is multiplied by 20. Decreasing the risk of falling is a necessity. Encouraging travel without the fear of falling is a priority.
Currently, nurses, especially at night, have no information about the risky situations in a room. The only way a disoriented resident who is out of bed or who has fallen will be discovered is during a visit by caregivers. And this is where Kaspard looks to respond. The company allows nursing staff to better accompany elderly people in their bed exits.
What solution does Kaspard offer?
Kaspard’s purpose is to improve the quality of care through objective and continuous information. When a resident has a fall or a resident does not return to bed after set time, the caregiver is immediately alerted so that they can act without delay – each resident has a different risk profile and mobility potential.
The company offers a small non-intrusive device placed discreetly in the ceiling of the resident’s room. Without using neither physical contact nor images, it detects falls and residents getting out of bed. In case of a problem, Kaspard sends an alarm in real-time to the nursing staff, who can intervene immediately in the resident’s room.
With a reliability rate of 99.3 per cent and the ‘no return to bed’ alarm, homes for elderly company such as Korian and Orpea are currently using Kaspard’s solution with positive feedback from users. Philippe Kaplan, Founder & CEO of Kaspard, says, “The satisfaction of our users, the excellent feedback from families and our deployment in large groups have motivated us to raise significant funds to strengthen our commercial development.”
Recent developments
During the difficult year in 2020, Kaspard was able to support healthcare personnel, who are under great pressure to manage the pandemic in their various facilities. Also, a recent version of the device has been released to offer new functionalities to users: increased mobility between rooms, enriched activity reports, integration with pager systems, and a major tool: the possibility to visualise in 3D the course of a fall in order to better understand and prevent them.
Philippe Kaplan adds, “Today, the solution allows us to intervene quickly and reduce falls. Our vision is to improve the quality of care through information. Multidisciplinary teams have objective tools to better individualise care and tomorrow we will be able to anticipate deterioration in the health of the elderly thanks to artificial intelligence.”
As of now, Kaspard system has over 40,000 operational nights to its credit. And according to the company, the number of falls in equipped residences has been cut by two-thirds. The quality of care is improved and the costs and consequences associated with falls are reduced considerably.