Skip to content

Health Informaticist

Train and support clinical end users

Enhances✓ Available Now

What You Do Today

You train clinicians and staff on EHR usage, provide ongoing support for workflow questions, and serve as the bridge between clinical users and the IT team.

AI That Applies

AI provides in-context help within the EHR, generates personalized training based on each user's proficiency gaps, and answers routine how-to questions automatically.

Technologies

How It Works

The system ingests each user's proficiency gaps 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 — in-context help within the EHR — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Routine EHR support questions are handled by AI assistants, freeing you for complex workflow optimization and training.

What Stays

Understanding why a clinician is struggling, designing training that fits their learning style, and the trust relationship that makes them come to you with problems.

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 train and support clinical end users, understand your current state.

Map your current process: Document how train and support clinical end users works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Understanding why a clinician is struggling, designing training that fits their learning style, and the trust relationship that makes them come to you with problems. 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 In-App AI Assistants 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 train and support clinical end users 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

Which training programs have the highest completion rates, and which have the lowest — what's different?

They set clinical practice guidelines that AI tools must align with

your health informatics lead

How do we currently assess whether training actually changed behavior on the job?

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.