Evidence constraints
Limit AI outputs to identifiable source material where appropriate.
AI IN CLAIMS & PRIOR AUTHORIZATION · WORKFLOW EXPLORATION
How should AI actually change the way claims and prior authorization decisions get made?
Claims and prior authorization work is often described as a processing problem.
Collect the information. Check the policy. Review the documentation. Determine eligibility. Approve or deny.
AI can make many of those activities faster.
But the harder question is whether faster processing produces better decisions.
The relevant evidence may be distributed across clinical documentation, policy criteria, patient history, previous decisions and exceptions.
Can AI review the case?
How should intelligence change the way evidence, context and judgement come together in the decision?
WORKFLOW
CLAIMS & PRIOR AUTHORIZATION REVIEWTHE USUAL QUESTION
The usual question
“Can we use AI to process claims faster?”
That is a reasonable starting point.
Claims teams already spend significant time gathering information, checking documentation, comparing evidence against policy or benefit criteria, identifying missing information and preparing cases for review.
AI can assist with many of these activities.
But processing more cases is not necessarily the same as making better decisions.
A system may retrieve the relevant documentation quickly and still miss context.
It may identify a policy criterion and still misinterpret an exception.
It may produce a fluent recommendation without making clear which evidence supports it.
The organisation may have made review faster.
It may not have made the decision better.
That is where the question changes.
Two ways to approach the workflow
CHOOSE A PATH TO EXPLORE
Select a path to see how the starting question changes.
Let's look at how the case becomes a decision.
EVIDENCE → CRITERIA → CONTEXT → EXCEPTION → DECISION
The workflow
A claim or authorization request does not become a decision simply because the required information has been collected. The work changes character as the organisation moves from evidence to interpretation.
Where does the difficulty sit in your workflow?
SELECT ONE STAGE
SELECT A STAGE
Evidence → Criteria → Context → Exception → Decision
Choose the point where the work becomes difficult for your organisation.
The workflow appears across healthcare, insurance, managed care and administration. Its evidence and consequences change with the context.
INDUSTRY → CLAIMS & AUTHORIZATION
Where this workflow appears
SELECT YOUR CONTEXT
The underlying movement from evidence to decision is recognisable. The policy, clinical context and authority around it are not identical.
The sequence is simple. The understanding is not.
THE COGNITIVE TURNING POINT
The decision isn't
the review.
A review can be fast and still produce a poor decision.
The interesting question is what happens between the evidence and the decision.
A claim may contain the required documentation. A prior authorization request may satisfy several visible criteria. A policy may appear straightforward.
But the case may still depend on context.
The difficult work begins when evidence has to be interpreted, exceptions have to be recognised and someone has to take responsibility for the resulting decision.
01Evidence is not the decision.
02A policy match is not necessarily a complete understanding of the case.
The work is not complete when a case has been reviewed. It is complete when the resulting decision is supported, owned and understandable.
ONE USEFUL INSIGHT
Where the work changes character
WHERE JUDGEMENT BECOMES VISIBLE
The highest-value part of the workflow may not be the largest transaction.
It may be the point where a routine case becomes ambiguous.
That is where:
- evidence has to be interpreted
- context has to be reconstructed
- an exception has to be recognised
- authority has to be exercised
- the decision has to become defensible
This is often where automation alone stops being sufficient. The opportunity is to understand exactly where AI can reduce cognitive and transactional load without obscuring the judgement that remains human.
ILLUSTRATIVE WORKFLOW EXAMPLE
A matched criterion does not determine the whole case.
EXAMPLE · NOT A DIAGNOSIS
- A claim appears to meet a documented coverage rule.
- The workflow connects the rule to the member, evidence, exception history and potential consequence.
- AI surfaces the relevant criteria and missing evidence without converting uncertainty into denial.
- A qualified reviewer makes and records the consequential judgement.
Criteria → Case → Judgement.
So the examination follows the places where evidence becomes interpretation, exception and accountable decision.
How we would examine it
DIMENSIONS · NOT A CHECKLIST
These aren't a checklist to complete. They are dimensions through which we examine how the workflow actually operates.
These questions explain the direction of the examination without turning the visitor's context into a public diagnosis.
And this is where AI requires more care.
AI PARTICIPATION · CONSEQUENCE-APPROPRIATE CONTROL
AI can help. It can also be confidently wrong.
AI can retrieve information.
It can compare documentation.
It can identify relevant criteria.
It can organise case history.
It can prepare a case for review.
It can also be wrong.
It can omit relevant evidence.
It can misinterpret context.
It can infer something that is not established.
It can present an uncertain conclusion with unwarranted confidence.
In claims and prior authorization, that distinction matters when the output influences:
- claims review
- prior authorization
- benefit decisions
- case escalation
- a consequential outcome
The more consequential the decision, the stronger the control around AI should be.
Validation
Require important outputs to be checked against the underlying evidence.
Decision boundaries
Define what AI may recommend, prepare or flag—and what remains outside its authority.
Exception handling
Route unusual or ambiguous cases appropriately rather than forcing them through the standard path.
Human control
Keep accountable decision ownership with the appropriate person.
Auditability
Make it possible to understand what information informed the outcome and where AI participated.
AI should help the organisation see more. It should not make uncertainty invisible.
So what would actually change if we redesigned the work?
What could change?
THE WORK · NOT ONLY THE TECHNOLOGY
The intervention can change more than case-processing speed.
EVIDENCE · CRITERIA
Reduce case-processing time
Reduce unnecessary information handling and review.
EVIDENCE · CONTEXT
Improve evidence accessibility
Make relevant case information easier to find and understand.
CRITERIA
Improve consistency
Reduce avoidable variation in routine review.
EXCEPTION
Handle exceptions better
Make unusual cases visible rather than forcing them through routine paths.
ALL STAGES
Make AI usable within the workflow
Put AI where it supports actual work rather than adding another tool.
DECISION
Improve decision quality
Give decision-makers better evidence, context and control.
The objective is not simply to process more cases. It is to make the decision process more coherent, more defensible and easier to work with—while keeping judgement visible where it matters.
ONE QUESTION WORTH ASKING
How often does your organisation spend more time reconstructing why a case is different than deciding what to do about it?
You may already know the answer.
You may not.
Either is a useful place to begin.
FIND THIS WORK IN CONTEXT
One workflow, connected to the organisation around it.
Return to the wider context without creating a separate version of this workflow.
YOUR WORKFLOW
How does your Claims & Prior Authorization Review workflow operate today?
The page above examines Claims & Prior Authorization Review as a workflow. Now apply the same lens to your own organisation. Answer eight questions and get a structured read on its operating condition.
Take the workflow assessment →You've looked at the workflow.
Now tell us what matters most.
YOUR WORKFLOW · YOUR PRIORITY
What would you want to change?
You know your claims and prior authorization workflow better than we do. Start by telling us where it hurts.
Tell us what matters most. We'll use it to understand where the workflow may be carrying unnecessary work, uncertainty, exception handling or judgement.
ALREADY KNOW YOU WANT TO TALK?
Bring the workflow as it operates today.
If claims or prior authorization work is becoming more complex faster than the organisation can comfortably absorb, the useful starting point is the workflow itself.
You don't need to redesign the process before we examine it.
We can examine where the work is transactional, where it becomes judgement, where exceptions change the shape of the work, and where AI can genuinely improve it.