Start narrow
Begin with a measurable workflow and a clear economic or clinical outcome.
About reCare.ai
Healthcare data is abundant. Individual patient context is not.
reCare.ai exists to help healthcare organizations connect fragmented signals into an evolving understanding of each patient — whether the care plan is on track, whether the patient is meaningfully changing, and what follow-through is appropriate.
Our focus
Make fragmented patient signals individually meaningful.
Healthcare systems generate enormous amounts of data, but much of it remains episodic, siloed, or interpreted primarily through population-level views.
We are building an intelligence layer that connects longitudinal patient context to real workflows — care-plan reconciliation, rising-risk and change detection, readmission-risk visibility, engagement, coordination, and escalation — without replacing human clinical judgment.
Why we’re building reCare.ai
Bryan Saba
FounderBryan founded reCare.ai in 2023 after seeing family members repeatedly end up in the emergency department and then struggle to follow the care plan after discharge.
That experience exposed a recurring problem: the healthcare system often knows what should happen next, but the information needed to understand whether it actually happened, or is already planned, lives across disconnected systems and teams.
reCare.ai is being built around that broader gap: connect the patient journey, understand whether expected care is actually on track, recognize when the patient’s own pattern is changing, and give healthcare teams the context and workflow support needed to act before either becomes an avoidable outcome.
How we build
Grounded in real workflows.
Learn longitudinally
Use each new signal and workflow result to enrich the individual patient context over time.
Expand responsibly
Automate where the workflow is understood; involve people where judgment should remain in control.
Move from signals to action
See whether care is on track, and when the patient is changing.
Bring us a high-value workflow, a care-plan problem, or a population where earlier visibility into patient change could prevent an avoidable outcome. We’ll show you how the intelligence layer fits.