Lab Requisition Errors: Costs and Prevention

A missing diagnosis code can turn one lab order into several phone calls, a billing hold, and eventually a denial.

Incorrect patient information, unclear test selections, missing specimen details, and incomplete provider documentation create the same problem. Staff have to stop the normal workflow, find the missing information, contact the referring practice, verify the correction, and restart the order.

At scale, these are not small administrative mistakes. They create measurable rework for accessioning, billing, and client-service teams.

The documentation problem is significant. In Medicare's 2024 data for "other" laboratory tests, insufficient documentation accounted for 87.7% of improper payments in that category.

Requisition quality affects much more than whether a form looks complete for diagnostic labs. It can determine whether a test can be performed correctly, whether an order is sufficiently documented, and whether the resulting claim can be supported.

What Is a Lab Requisition?

A lab requisition is the request used to communicate a laboratory order and the information needed to process it.

A lab requisition form may contain:

• Patient identifiers
• Ordering-provider information
• Tests requested
• Specimen type or source
• Collection date and time when relevant
• Diagnosis or clinical information
• Other information required for a specific test

Under CLIA requirements, laboratories must ensure that test requests contain information needed for accurate testing and reporting.

Depending on the test, that can include patient identification, the authorized ordering person, the requested test, specimen source, collection information, and other relevant clinical information.

The patient's chart or medical record can sometimes serve as the test requisition when the necessary information is available to the laboratory.

This makes requisition handling part of the pre-analytical process. Problems at this stage can affect the rest of the lab workflow, from accessioning through billing.

The Requisition Correction Loop Creates Hidden Work

Incomplete orders often follow the same pattern.

1. The lab receives the specimen and requisition.
2. Accessioning identifies missing or conflicting information.
3. Staff determines whether testing can continue.
4. The referring practice is contacted.
5. Testing, reporting, billing, or a combination of these is placed on hold.
6. The practice sends the missing information.
7. Laboratory staff verify and enter the correction.
8. The order returns to the normal workflow.

The process becomes more expensive when the first response is incomplete, conflicting, or never arrives.

One requisition may require only a few minutes of work. Hundreds of defective requisitions each month create significant manual workload.

The impact can spread across:

• Accessioning productivity
• Test turnaround time
• Billing turnaround
• Claim submission
• Denial management
• Provider relations
• Client satisfaction
• Staff workload

A lab that tracks only final denials will miss much of this cost because most requisition rework occurs before the claim is submitted.

Not All Lab Requisition Errors Create the Same Risk

Grouping every incomplete requisition into one category makes the correction process inefficient.

A missing specimen source is different from a missing diagnosis. An ambiguous test order is different from incomplete insurance information.

Labs can separate defects according to the part of the workflow they affect.

The next step depends on the type of defect.

Some errors prevent safe or accurate testing. Others allow testing to continue but prevent the claim from moving forward. Some can be corrected from existing documentation without contacting the provider again.

Requisition Errors That Can Affect Testing

Patient identification conflicts, unclear test orders, incorrect specimens, and missing specimen information can directly affect the laboratory's ability to perform or interpret a test correctly.

CMS's CLIA guidance requires test requests to include information relevant and necessary for accurate and timely testing and reporting.

Laboratories also need processes that ensure manually entered requisition information is accurately transferred into the LIS.

For accessioning teams, the stop criteria should therefore be defined before a problem occurs.

A written policy can specify:

• Patient-identification discrepancies that require a hold
• Tests that require a specimen source
• Collection information required for time-sensitive tests
• Unacceptable or uncertain test names
• Specimen requirements that cannot be corrected after collection
• Clinical information required for accurate interpretation
• Staff responsible for resolving each type of problem

This reduces inconsistent decisions between shifts, locations, and individual employees.

Requisition Errors That Affect Billing

A different group of defects may not prevent the laboratory from performing the test.

The problem appears later.

A specimen can be valid. The requested test can be clear. The laboratory can complete the analysis successfully.

Billing may still lack the information needed to support the claim.

Common examples include:

• Missing diagnosis information
• Missing or invalid ordering-provider information
• Insufficient documentation of medical necessity
• Coverage criteria that are not supported by the information received
• Frequency-limit conflicts
• Missing authorization where applicable

This creates a particularly expensive type of requisition error because the laboratory may discover the problem only after it has already performed the test.

Medical Necessity Problems Need to Be Detected Earlier

Medicare requires documentation supporting both the order and the medical necessity of diagnostic laboratory services.

Under current lab order requirements, the claim-submitting entity must retain documentation of the ordered service, information identifying the ordering provider, evidence of correct order processing, and diagnostic or other medical information supplied to the laboratory.

The patient's medical record also needs enough information to support why the ordered test was reasonable and necessary.

This creates an important distinction for laboratory workflows.

A requisition can contain enough information to perform a test while still containing too little information to support reimbursement.

For example, a valid specimen may arrive with a clearly ordered test but no usable diagnosis or relevant clinical information.

Nothing about the specimen itself necessarily prevents testing.

The financial risk appears when the claim reaches billing.

This is why medical necessity denials often begin as order-quality problems rather than billing problems.

CMS's MolDX guidance makes the distinction particularly clear for covered molecular services. A requisition containing enough relevant clinical information may support the medical-necessity review, while insufficient information can trigger a need for additional documentation.

Finding that documentation gap before testing or claim submission gives the lab more options than finding it after a denial.

Laboratory Staff Should Correct Data, Not Create Clinical Justification

There is an important difference between correcting documented information and supplying information that the ordering provider never gave the laboratory.

For example, a staff member may be able to correct a transcription error by checking reliable source documentation.

That is different from selecting a diagnosis because it appears likely to satisfy a coverage policy.

CMS's lab documentation requirements state that diagnostic or medical information supporting the service should be documented, including an ICD-10-CM code or narrative information provided to the laboratory.

Lab policies should therefore distinguish between:

• Correcting an obvious data-entry error from documented information
• Transcribing information already supplied by the provider
• Obtaining missing information from the provider
• Changing the ordered test
• Adding diagnosis information
• Adding clinical justification for medical necessity

The last three categories may require ordering-provider involvement rather than an internal laboratory correction.

This protects both claim integrity and the audit trail.

Route Requisition Errors to the Right Team

Sending every requisition problem back to the same inbox creates unnecessary delays.

Ownership can be defined by defect type. 

Accessioning and laboratory operations

These teams commonly handle:

• Patient and specimen matching
• Specimen-source problems
• Collection details
• Test-menu mapping
• Specimen acceptance
• Internal data-entry errors

Ordering provider or referring practice

Provider involvement is generally needed for:

• Unclear test orders
• Missing clinical history
• Diagnosis information
• Confirmation of provider intent
• Changes to the ordered service
• Clinical documentation supporting medical necessity

Billing and revenue cycle

Billing teams typically handle:

• Payer information
• Claim requirements
• Coverage-policy review
• Frequency edits
• Authorization status
• Diagnosis-to-service billing issues

Compliance

Compliance review becomes important when the problem is systemic rather than clerical, including:

• Unsupported diagnoses being added to orders
• Missing evidence of provider intent
• Repeated medical-necessity problems
• Untraceable changes to orders
• Persistent ordering problems from a referring practice

Clear ownership reduces duplicate work and prevents accessioning, billing, and client-service staff from independently chasing the same correction.

Measure the Lab Requisition Defect Rate

Most laboratories already know which clients frequently send incomplete orders.

That knowledge becomes much more useful when it is measured.

A basic requisition defect rate can be calculated as:

Requisitions requiring correction ÷ total requisitions received × 100

A second useful measure is the first-pass clean requisition rate:

Requisitions received without manual correction ÷ total requisitions received × 100

The overall rate should then be broken down further.

Useful dimensions include:

• Referring practice
• Ordering provider
• Collection location
• Test
• Test category
• Error type
• Payer
• Correction owner
• Number of contacts required
• Time to resolution
• Testing delay
• Billing delay
• Denial outcome

Consider a laboratory processing 10,000 orders per month.

If 1,100 require manual correction, the overall defect rate is 11%.

Further analysis might show that:

• 600 defective orders come from five practices
• 420 involve missing diagnosis information
• 180 require multiple follow-ups
• 90 delay claim submission

That immediately changes the improvement strategy.

The laboratory does not have 1,100 completely different problems. It has a few recurring defects concentrated in specific parts of the ordering workflow.

Use Defect Data to Improve Referring Practices

Generic reminders about completing all required fields rarely address the actual cause of recurring requisition problems.

Practice-level defect data is more useful.
 

Each practice needs a different correction.

Practice A may need changes to its ordering workflow.

Practice B may need specimen-specific training.

Practice C may have an EHR configuration or provider-record problem.

A practical client-improvement process includes:

1. Identifying high-defect practices.
2. Separating defects by type.
3. Finding the source of the recurring error.
4. Giving the client targeted guidance.
5. Measuring the defect rate after the intervention.
6. Escalating persistent problems when needed.

This also gives client-service teams objective data.

Instead of repeatedly telling a practice that its requisitions are incomplete, the laboratory can show exactly which defects are recurring and how often they occur.

Standing Orders Need Their Own Controls

Recurring orders create another documentation risk.

Current CLIA guidance states that laboratory policies should define which tests may be covered by standing orders and how often those orders should be reconfirmed.

Labs should be able to identify:

• The provider responsible for the standing order
• The tests covered by it
• Its effective period
• Reconfirmation requirement
• Documentation supporting continued testing
• Any payer-specific frequency requirements

An old order should not remain operational simply because it exists in the system.

Prevent Defects Before They Reach Accessioning

The most effective requisition correction happens before the specimen enters the testing workflow.

Enforce Required Information During Ordering

A required field has limited value if an order can still be submitted without it.

Digital requisitions can prevent incomplete submissions when essential information is missing.

Requirements can also change based on the selected test. A molecular test may need different clinical information from a routine chemistry test. Certain specimens require source details. Some services have specific coverage criteria.

A single static form cannot always handle these differences well.

Validate Test-Specific Requirements

The ordering workflow can identify missing information while the order is still being completed.

Examples include:

• Missing patient identifiers
• Missing ordering-provider details
• Missing diagnosis information
• Test-specific specimen requirements
• Required clinical information
• Coverage-related information
• Frequency concerns

Early detection keeps the correction close to the person who has the information.

Separate Hard Stops From Review Flags

Not every defect should stop an order.

A patient-identification conflict may require immediate resolution.

A possible payer-coverage issue may instead need billing or compliance review.

Treating both situations as the same type of alert can create unnecessary friction.

Labs can classify validation rules by severity:

• Submission cannot continue
• Manual review required
• Provider confirmation required
• Informational warning only

This keeps the ordering process controlled without creating a hard stop for every exception.

Digital Requisitions Should Do More Than Replace Paper

Moving a paper form onto a screen does not solve requisition quality if incomplete orders can still be submitted.

The greater value comes from validating the order while it is being created.

Docus Digital Requisitions use structured ordering fields, guided inputs, and validation to reduce incomplete information before the request enters the laboratory workflow.

Docus can also support compliance checks around missing information, diagnosis-test alignment, medical necessity, and testing frequency before these issues reach later billing stages.

The operational difference is straightforward.

In a reactive workflow, laboratory staff discover the defect after receiving the order. The referring practice is contacted, the correction is returned and verified, and the order is released back into testing or billing.

With earlier validation, the defect can be identified while the order is still being completed and while the ordering side has immediate access to the relevant patient and clinical information.

The objective is not simply to digitize a requisition.

It is to reduce the number of defective orders entering the laboratory in the first place.

Build a Requisition Quality Dashboard

A lab does not need dozens of KPIs to start improving requisition quality.

A useful dashboard can begin with six:

Over time, denied claims can also be connected back to requisition defects.

Incomplete or invalid claim information, for example, can contribute to CO-16 denials. Missing clinical support can contribute to medical-necessity denials.

Connecting denial data to the original order helps the laboratory identify whether the real problem began in billing or much earlier.

The same root-cause approach is useful across denial management, where recurring payer codes may actually trace back to order entry, documentation, or requisition quality.

Start With One Month of Requisition Data

A useful first audit does not need to be complicated.

Take one month of orders and classify every requisition that required manual intervention.

Record:

• Referring practice
• Test
• Defect type
• Team that corrected it
• Number of provider contacts
• Correction time
• Testing delay
• Billing delay
• Final claim outcome when available

Then identify the three defect types creating the most manual work.

Those three problems are the best starting point for workflow changes, referring-practice training, or digital validation.

A recurring defect should not remain a recurring task.

Once the pattern is visible, it can be managed.

Final Thoughts

Lab requisition errors cost more than the time required to correct a form.

They create accessioning work, provider follow-up, testing delays, billing holds, documentation gaps, and avoidable denials.

The strongest labs treat requisition quality as a measurable operational process.

Defects are categorized by risk. Correction ownership is defined. Referring practices are measured by defect type. Standing orders are controlled. Medical-necessity problems are identified before billing whenever possible.

Most importantly, validation moves earlier in the workflow.

By the time a deficient order reaches billing, the laboratory may already have spent money performing the test.

Preventing the defect before the specimen enters the normal workflow is usually far less expensive than correcting it afterward.