5 AI Prompts I Use to Understand Epic Workflows - Not Just Memorize the System

If you work in Epic long enough, you eventually run into a uncomfortable reality:

You can know how to do your job in Epic without fully understanding what is happening across the entire workflow.

You may know the screen.

You may know which queue to check.

You may know which interface failed.

You may even know exactly which team to call.

But then someone in a meeting asks:

“What actually happens after the physician places that order?”

And suddenly, knowing the application isn't enough.

You need to understand the workflow.

That distinction matters more than ever for Epic analysts, Business Analysts, informaticists, interface analysts, project managers, and other Health IT professionals who want to grow beyond the tasks they perform today.

This is one of the ways I use AI in my own work.

Not to replace Epic knowledge.

Not to pretend AI knows my organization's build.

And definitely not to paste confidential patient or organizational information into a public AI tool.

I use AI to question, connect, and deepen what I already know.

Here are five prompts that demonstrate what that looks like.

1. “Walk me through what happens from the moment a lab test is ordered to when the result shows up in the doctor's inbox.”

This is one of my favorite types of prompts because it forces you to stop thinking about Epic as a collection of screens.

Think about our test patient, Maria.

Maria visits her physician because she has been unusually tired. Her physician orders a CBC.

What happens next?

At a high level, you might have:

Physician → Order → Specimen Collection → Laboratory → Analyzer → Result → Verification → EHR → Clinician Inbox

But every arrow represents something worth understanding.

Who creates the order?

What identifiers travel with it?

How does the laboratory know the specimen belongs to Maria?

What happens when the specimen reaches the analyzer?

When does a preliminary result become final?

How does the result get associated with the original order?

What determines whether the physician receives an inbox notification?

What happens if the result is critical?

Now you're no longer learning “where the CBC result appears.”

You're learning how a laboratory workflow operates.

That knowledge travels with you.

If tomorrow you work on an Epic implementation, an integration project, an acquisition, a laboratory replacement, a data migration, or even another EHR platform, you still understand the underlying clinical workflow.

That is career mobility.

2. “What are the most common questions clinicians ask about lab results, and how does the EHR handle each one?”

This prompt changes your perspective.

Instead of asking:

“How does this feature work?” you are asking:

“What problem is the clinician trying to solve?”

For example, a physician may ask:

• Where is Maria's result?

• Is this the final result?

• Why did this result change?

• Has someone reviewed this critical value?

• What was her previous result?

• Why am I receiving this result?

• Who else was notified?

Those questions expose requirements.

“Where is the result?” might lead you into result routing and interface status.

“Why did this result change?” might lead you into corrected results and result versioning.

“What was her previous result?” introduces longitudinal display and trending.

“Who was notified?” introduces routing, acknowledgement, escalation, and auditability.

This is especially powerful for Business Analysts.

A strong BA doesn't simply document what a screen does.

A strong BA understands why the user needs the information, where that information originates, and what happens when the expected workflow breaks.

Use AI to help uncover those questions.

Then validate the answers against your actual environment, SMEs, Epic documentation, workflows, and organizational policies.

3. “Draft a Reporting Workbench extract spec for turnaround time by test type.”

Now we're moving from understanding into producing something useful.

Suppose leadership wants to know:

“How long are our laboratory tests taking?”

That sounds simple.

It isn't.

What exactly is “turnaround time”?

For Maria's CBC, are we measuring:

Order time → Result time? Or: Collection time → Result time? Or: Lab receipt time → Verification time?

And what does “result time” mean?

Preliminary? Final? Corrected?

Before anyone starts building a report, those definitions need to be clear.

AI can help you create the first structure of an extract specification:

Business Question:
What is the laboratory turnaround time by test type?

Population:
Which laboratory tests and encounters should be included?

Time Period:
What reporting period applies?

Required Data:
Test name, order timestamp, collection timestamp, specimen receipt timestamp, result timestamp, final status, ordering department and other relevant dimensions.

Calculation:
Define precisely which timestamps constitute turnaround time.

Exceptions:
Cancelled orders, recollections, corrected results, send-out testing, missing timestamps and other scenarios.

That doesn't mean you blindly hand the AI-generated specification to a developer.

It means you now have something intelligent to interrogate.

Ask: What did we miss?

That is a much better starting point than a blank document.

4. “Review this Beaker interface requirements document for gaps a vendor would flag.”

This is one of the ways AI can help you start thinking beyond your immediate role.

Imagine you're reviewing requirements for a laboratory interface.

Your document covers:

• patient demographics

• orders

• results

• test codes

• timestamps

It looks complete.

So instead of asking AI to summarize it, ask AI to challenge it.

Review these requirements from the perspective of the receiving vendor. What is ambiguous, missing, contradictory, or likely to generate a clarification question?

Suddenly you may start seeing questions such as:

What happens with corrected results?

How are cancelled orders represented?

What happens if the patient cannot be matched?

Which identifier is authoritative?

Are local test codes mapped to a standard terminology?

How are abnormal flags represented?

What happens with preliminary results?

Are units standardized?

How are interface errors reconciled?

What happens during downtime?

Which system owns each data element?

Those are the questions that often separate someone who can read a requirements document from someone who can lead the requirements conversation.

AI becomes valuable here because you're using it as another perspective.

You still need humans with real implementation knowledge to validate the requirements.

But you're entering that conversation better prepared.

5. “Explain the difference between Beaker Anatomic Pathology and Clinical Pathology workflows to a new BA.”

This may be my favorite prompt because it addresses one of the biggest problems I see in Health IT:

We sometimes confuse familiarity with understanding.

A new analyst hears:

“Beaker CP.” “Beaker AP.” “Specimen.” “Accession.” “Result.” “Pathology.”

Everyone around them seems to understand the terminology, so they hesitate to ask the basic question.

AI gives them somewhere to start.

Ask it to explain the difference as if you were a new BA.

Then go deeper.

Ask:

“Show me Maria's journey through both workflows.”

Maria has routine bloodwork performed.

That can help you understand a Clinical Pathology workflow involving structured laboratory testing and results.

Later, Maria undergoes a biopsy.

Now follow that specimen through an Anatomic Pathology workflow involving tissue, gross examination, microscopic examination, pathologist interpretation, and the resulting pathology report.

Then ask:

“What requirements questions would a BA ask in each workflow?”

Now you're learning much more than terminology.

You're learning how to investigate an unfamiliar clinical domain.

And that is a skill you can reuse.

The Real Value of AI Isn't the Answer

This is where I think many conversations about AI and Health IT miss the point.

The goal shouldn't be:

“How can AI do my Epic work for me?”

A better question is:

“How can AI help me understand the work at a deeper level?”

There is a major difference.

If AI simply gives you an answer, your capability hasn't necessarily changed.

But if you use AI to explore:

What happens before this?
What happens after this?
Which system owns this data?
What could go wrong?
What would the clinician see?
What would a vendor ask?
How would I test this?
How would this change during a migration?

—you begin building something much more valuable:

a mental model of the healthcare ecosystem.

That mental model is what allows you to walk into unfamiliar projects and start asking intelligent questions.

That's Where Career Mobility Comes From

Your career becomes vulnerable when your value is tied entirely to knowing one organization's configuration or one set of screens.

Your career becomes more portable when you understand:

Clinical workflow → Application workflow → Data → Integration → Downstream use

Because applications change.

Organizations change.

Implementations change.

Vendors change.

Even standards evolve.

But Maria still enters the healthcare system.

Someone still needs to identify her correctly.

Someone still places an order.

A specimen still needs to be associated with the correct patient.

A result still needs to return.

A clinician still needs to understand it.

And data still needs to move safely between systems.

That's why I don't see AI primarily as a shortcut for Health IT professionals.

I see it as a learning accelerator.

Use it to ask the question you were afraid was too basic.

Use it to challenge the requirement you thought was complete.

Use it to understand the workflow outside your module.

Use it to prepare before the meeting.

Use it to translate technical language into something you can actually visualize.

And then take that understanding back into the real environment and validate it.

Because the Health IT professionals who become increasingly valuable won't simply be the people who know where to click.

They'll be the people who understand why the workflow exists, how the data moves, what happens when it breaks, and how the pieces fit together.

Don't use AI just to get your work done faster.

Use it to understand enough that, eventually, you're ready for bigger work.

Curious about Maria? Find her HERE