At a Glance
Janet had always been a wiz around the kitchen. She had loved making big meals for her family, though these days she was mostly cooking for one. The kitchen floor was a bit slippery, and the pain in her hand had been nagging her for quite some time, but she knew she could manage on her own.
Challenges:
While Janet had not informed her family and caregiver of anything out of the ordinary , Sensi’s system began picking up a few Difficulty Performing Task notifications and instances of Physical Distress. These instances were mostly occurring in the kitchen around times of meal preparation.
System Findings:
Sensi’s system picked up a fall and difficulty performing task, as well as a call for help. A real-time alert was sent, followed by an insight. Janet had fallen while attempting to prepare herself a meal, and had fallen with a knife in her hand.
Data Driven Decisions:
- The agency received the alert and contacted Janet, and ensured that she was safe, as she had been able to get up independently.
- Fall risk assessment was recommended to prevent future falls.
- Meal preparation was added to Janet’s care plan.
- Janet’s family used Sensi’s data to communicate the need for additional care hours. A change that Janet had previously been resistant to.
- Care plan review for increased supervision.
- Review of footwear.
- Equipment review recommended.
- Medical evaluation was performed.
Impact:
Sensi was able to pick up on critical data points that allowed Janet’s care agency to identify and assess when, why and where falls were occurring. Not only were the family and agency informed as to the existence and frequency of Janet’s falls, they were able to pinpoint the exact ADL and location within the home that were prerequisites for a large percentage of the fall occurrences.
While Janet was reluctant to admit needing additional help with a task she felt familiar with and loved, Sensi’s data was able to highlight the dangers of continuous resistance to necessary care.
Through Sensi, the agency and family were able to use data-driven decision making to ensure Janet’s safety and wellbeing, mitigate risk, and extend the ability of her to age at home safely.
