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Case Manager

Collaborating with multidisciplinary teams

Enhances◐ 1–3 years

What You Do Today

Coordinate with therapists, doctors, probation officers, school counselors, and housing specialists to wrap services around your client. You're often the hub of the wheel.

AI That Applies

AI tracks which providers are involved with each client, schedules coordination meetings, and shares relevant updates across the team (with consent protocols).

Technologies

How It Works

The system ingests which providers are involved with each client 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 results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Team coordination is tracked and documented. Everyone on the team sees what others are doing without relying on phone tag and faxes.

What Stays

Building effective working relationships across disciplines and advocating for your client's perspective in team discussions.

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 collaborating with multidisciplinary teams, understand your current state.

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Map your current process: Document how collaborating with multidisciplinary teams works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
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Identify the judgment points: Building effective working relationships across disciplines and advocating for your client's perspective in team discussions. These are the boundaries AI won't cross.
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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 shared care platforms 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 collaborating with multidisciplinary teams 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 collaborating with multidisciplinary teams?”

They set clinical practice guidelines that AI tools must align with

your health informatics lead

“Who on our team has the deepest experience with collaborating with multidisciplinary teams, 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 collaborating with multidisciplinary teams, 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.

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