Cherish | Fall Monitoring System
I worked with an early-stage startup to design a fall monitoring platform, using design research to ensure product-market fit in assisted living care facilities.
Context
While most offerings used cameras or wearables, Cherish was building a fall monitor that used AI and radar instead. This new alternative allowed older adults to live safely without feeling like there was something getting in the way.
Nursing homes were key partners, acting as investors, consumers, and a real-world environment to test the emerging technology.
The wall-mounted device feels more like a lamp than a health monitor.
Problem
While the tech could significantly improve patient outcomes, it only mattered if it worked with existing workflows. Delivering value meant helping nurses identify and respond to incidents as quickly as possible.
At the same time, the product was still evolving. The device captured powerful information but offered little feedback, and we had to translate this “black box” into actionable insights, accounting for the device’s limitations and functionalities that could at times be unclear.
The existing dashboard was just a table of vitals. It didn't prioritize the fall alerts—the product's entire value prop.
Research
We visited three different nursing homes to understand their workflows, challenges, and possible barriers to adoption. Numerous interviews, usability tests, and observational sessions helped us understand how nursing homes currently dealt with falling.
01
To build foundational context, we reviewed literature on nursing home workflows and looked at common digital platforms used.
02
We visited 3 different care facilities to conduct interviews and observe workflows.
03
To organize our findings, we ran an affinity mapping session, revealing 4 key themes.
What we learned
Across interviews and site visits, three consistent themes emerged about how nurses think about fall risk, staffing, and accountability.
Simply getting out of bed or into a wheelchair can cause falls. Nurses need to be aware of movement without noise from constant alerts.
In a high-turnover, shift-based environment, up-to-date knowledge of each resident's fall risk can't live in someone's head.
Nurses fear data being used against them in court, but that same data can prove they responded fast and did their job.
Initial Approach
Our goals when redesigning the dashboard was to make sure nurses could easily identify and respond to incidents. At the same time, we had to make sure the data presented was in a format that supported engineering's AI training.
The Design
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The original device only detected whether someone was standing, in bed, or on the ground. But most nursing home residents used mobility aids like walkers.
The original dashboard only alerted nurses about falls that had already happened. We introduced a lower-priority alert type focusing on potential falls.
Nurses can add notes for clear communication on what care a resident needs. Tags give an at-a-glance look at a patient's fall risk.
We introduced a timeline that shows exactly when a nurse entered the room after an incident, helping staff verify response times.
Outcome
Lead with measurable results if you have them.
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