Risk detection
Catch the quiet signals before they become incidents
Serious incidents usually announce themselves days in advance, quietly. Caleo reads across notes, meds and vitals and surfaces the trends worth knowing — declining fluids, weight loss, repeated missed doses — before they become incidents.
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Trend detection
Looks across days per resident for changes a busy team would miss.
Health signals
Fluids, weight, eating, sleep, mood — pulled into one picture.
Prioritised alerts
Flags only what truly matters — no alert fatigue.
Tied to evidence
Every flag links back to the underlying notes — fully auditable.
Proactive care, not damage control — families and inspectors see the intent.
The quiet signals a busy team misses
Serious incidents in a care center — falls, infections, dehydration — rarely arrive without warning. They announce themselves quietly, days ahead: eating a little less each meal, getting up more at night, slow weight loss, the same dose missed again and again. Each one looks minor in the moment.
The trouble is these signals are scattered across many shifts, written by many hands, some still on paper or in LINE chats. A carer looking after a dozen residents at once has almost no way to connect the dots in time. By the time the whole picture appears, the incident has often already happened. This is the gap Caleo's risk detection is being built to close. It is a feature we are designing, not yet live — read more about how AI fits into elder care.
AI surfaces it — the nurse decides
When it is ready, Caleo is designed to read across notes, medications and vitals for a single resident, then surface the trends worth knowing — fluids trending down, weight slowly disappearing, a dose repeatedly skipped. The system is not built to diagnose. It simply brings forward what might otherwise slip past, so a person can look.
The design keeps the human in control. Every flag is built to link back to the underlying notes, so a nurse or shift lead can open the real evidence and use their own judgement — adjust the care plan, call the doctor, or keep watching. Prioritisation is being designed to flag only what genuinely matters, not to sound an alarm all day until it is ignored. It follows the same principle as our live AI care notes: AI drafts, the human always reviews and approves.
Auditable, and respectful of personal data
Because this touches both health and risk, the design has to be especially careful. Every flag is meant to leave a trail you can trace back — which data it came from, who saw it, and what the team decided next. That trail helps a center show สบส. inspectors that its care is proactive and genuinely watchful, not just damage control after the fact.
A resident's health data is sensitive data under the law. Caleo is designed to fit PDPA from the start — role-based access, encryption, and complete access logs. Risk detection is a tool to help the team see sooner, not a medical diagnosis, and it is not built to replace the assessment of a nurse or doctor.
Frequently asked questions
When will risk detection be available?
It is still in development and not yet live. It is being designed to work on data the team already records through available features, such as care notes and resident records. If you would like to be notified at launch, or to join as an early pilot center, you can reach out to the Caleo team in advance.
Will the AI replace the nurse's judgement?
No. The system is designed to surface trends worth a look, not to diagnose or instruct. All clinical decisions remain with the nurse and care team. Every flag links back to the source notes, so a person can open the real evidence and decide for themselves what to do next.
How are alerts prioritised so they don't cause fatigue?
It is being designed to flag only trends that genuinely matter for that resident, not every small change. Too many alerts make a team start to ignore them, which is more dangerous than no alert at all. The goal is fewer, meaningful signals — few enough that a shift lead can actually act on each one.
Is resident health data safe and PDPA-compliant?
Yes. Health data is sensitive data under the law. Caleo is designed to fit PDPA from the start, with role-based access, encryption, and a log of every access. Risk analysis runs on the center's own data, used only to help that center's team care for its residents.
Can AI flags be audited at inspection time?
Yes. Every flag is designed to carry a trail — which notes it came from, who saw it, and what the team did next. That trail helps a center show สบส. inspectors that it watches proactively and acts, rather than only recording incidents after they happen.