The ability of patient RTLS tags to identify and alert caregivers to unexpected patient movement at the bed level offers an important opportunity to prevent or mitigate patient falls, or at least trigger immediate medical attention for the patient who has already fallen.
When a fall is detected, Trackerwave’s system connects quickly to the nearest caregiver with the help of a BLE Coaster, even if the patient is unconscious or unable to move. The nearest caregiver will know the location of the fall and can immediately act upon. The patient will be provided with a BLE Patient Tag with/without a panic button that can be worn as a wristband or around the neck based on requirement. These tags come with built in accelerometers that help detect sudden movements like a fall.
Sensitive patients, in stroke and orthopaedics ward are provided with movement aware wrist band tag, coupled with bed occupancy monitors. The bed occupancy monitor consists of a bed pressure pad placed underneath the mattress of the user and an alert is raised should the user get out of bed and not return by a certain time. The sensor can also raise an alert if the user doesn’t go to bed by a particular time or doesn’t get up by a certain time in the morning. The Bed Occupancy Sensor detects if the bed is occupied or unoccupied during set times and will detect if your patient has not gone to bed, has gotten up from bed and not returned within a certain time frame, or has not gotten up in the morning, thus alerting caregivers to potentially unusual or dangerous situations. If any of these situations are detected, the sensor will trigger an alarm and alert an onsite caregiver. Bed Occupancy Sensor provides an early warning to caregivers by alerting the caregiver that a patient has left their bed and not returned within a defined period of time, indicating a possible fall.
Patient falls and resulting injuries among the elderly remain one of the most nagging and serious patient safety problems in hospitals. One-third of these occurrences happen when individuals leave their beds—usually while trying to go to the bathroom unaided. The frequency of those falls is greatest at night and during weekend shifts when staffing levels are lowest. After feasibility is determined, the observed time of the fall. It is expected that RTLS would accurately detect falls across these three positions and have high interrater reliability. TW accurately detects falls, which is eventually used to improve caregiver and staff response times to a fall, thereby improving patient-care and reducing associated health care costs.
Based on falls, number of falls being mitigated from time to time, from the point where the falls are mitigated from, reports are generated so that they can predictive analysis can be done and such accidents can be understood why or how its happening to create preventive measures.
