Care coordination with AI/ML and ADT data

Care coordination fails when the next team does not know the patient just moved. ADT (admission, discharge, transfer) data is the signal that a journey changed. Combined with models that already know the patient's risk, it becomes an instruction, not a log.

ADT carries facility, disposition, length of stay, diagnosis, and contact data in near real time. SquareML uses that stream with encounter history so a care team sees the current move and the 360 journey behind it.

A practical example: a patient with heart failure is admitted. The platform can alert cardiology, refresh the care plan, and schedule follow-up before discharge. That is the difference between a dashboard and a workflow.

The same pattern applies to COPD and other chronic conditions where missed follow-up, not the index stay, drives the next admission. Flag the risk, notify the team, close the loop.

For health systems, this is also an operations problem. Fewer surprise readmissions means better use of beds, staff, and post-acute capacity. SquareML's job is to make the ADT feed actionable inside the teams that already own those patients.