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Care Manager

Monitor high-risk patients

Enhances✓ Available Now

What You Do Today

You track patients who are at high risk for hospitalization, ED visits, or deterioration — checking in regularly, monitoring adherence, and intervening before crises develop.

AI That Applies

AI risk stratification models identify patients whose risk is increasing based on claims patterns, medication fill data, and social factors, prioritizing your outreach list.

Technologies

How It Works

The system ingests claims patterns as its primary data source. Predictive models weight dozens of input variables against historical outcomes, producing probability scores that rank cases by risk level. The output is a prioritized alert queue, with the highest-confidence findings surfaced first for immediate review.

What Changes

You reach out to the right patients at the right time when AI detects early warning signals rather than waiting for a hospitalization to trigger review.

What Stays

The check-in call, understanding what's really going on at home, and the problem-solving when a patient faces barriers AI can't see.

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 monitor high-risk patients, understand your current state.

Map your current process: Document how monitor high-risk patients 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 check-in call, understanding what's really going on at home, and the problem-solving when a patient faces barriers AI can't see. 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 Risk Stratification 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 monitor high-risk patients 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's our current false positive rate, and how much analyst time does that consume?

They set clinical practice guidelines that AI tools must align with

your health informatics lead

Which risk scenarios do we not monitor today because we don't have the capacity?

They manage the EHR integrations and clinical decision support configuration

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.