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Care Manager

Manage care transitions

Enhances✓ Available Now

What You Do Today

When patients move between settings — hospital to home, SNF to outpatient, ED to follow-up — you ensure continuity, medication reconciliation, and timely follow-up appointments.

AI That Applies

AI tracks discharge events in real time, auto-generates transition checklists, and flags patients at high risk of readmission for immediate outreach.

Technologies

How It Works

The system ingests discharge events in real time as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — transition checklists — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

You know about discharges immediately and have risk-stratified transition plans ready rather than finding out days later.

What Stays

The post-discharge phone call that catches the patient who didn't fill their prescription, can't get to their follow-up appointment, or doesn't understand their discharge instructions.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for manage care transitions, understand your current state.

Map your current process: Document how manage care transitions works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: The post-discharge phone call that catches the patient who didn't fill their prescription, can't get to their follow-up appointment, or doesn't understand their discharge instructions. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Transition Management AI tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long manage care transitions takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your department medical director

What data do we already have that could improve how we handle manage care transitions?

They set clinical practice guidelines that AI tools must align with

your health informatics lead

Who on our team has the deepest experience with manage care transitions, and what tools are they already using?

They manage the EHR integrations and clinical decision support configuration

a nurse informaticist

If we brought in AI tools for manage care transitions, what would we measure before and after to know it actually helped?

They bridge the gap between clinical workflow and technology implementation

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.