Kihleo is a medtech startup developing an early warning system for urinary tract infections in older people with incontinence receiving care at home or in nursing homes. UTIs are one of the most common reasons for acute deterioration, confusion, and hospitalisation in this group. They are challenging to detect early, because symptoms are often vague, many older people cannot clearly communicate discomfort, and home care staff and nurses have minimal time. Today, detection is primarily reactive. Staff notice that something is wrong, then order urine tests and start treatment when the infection is already well underway. This leads to unnecessary suffering, avoidable admissions, and a high use of antibiotics.
Kihleo’s solution is a small sensor placed in the diaper that measures biomarkers in the urine every time the person voids. When the diaper is changed, the sensor is read, and data is sent to a secure backend. Algorithms and predictive models analyse patterns over time and provide early, trustworthy indications that an infection may be developing, days before clinical symptoms are usually recognised. The system is not a diagnostic tool. It is a decision-support and early-warning system that integrates into existing workflows and helps staff decide when to look more closely, consult a nurse or doctor, and perform standard diagnostic tests.
The value is threefold. For older people, it means faster and more targeted treatment, fewer severe infections, less confusion, and a better day-to-day quality of life. For relatives and care staff, it provides reassurance, objective data, and more time for actual care rather than crisis management. For the public sector, it helps prevent avoidable hospitalisations and complications that are both expensive and resource-intensive.
Kihleo is working with a hardware partner on sensor development and is already in dialogue with Danish municipalities about pilot projects. The long-term vision is to create a scalable, data-driven platform for early detection of infections and other complications in elderly care, using everyday materials like diapers as non-invasive data sources.