Specimen Mix-ups, Delays, and Denials: Billing Risks Behind the Scenes

Imagine getting a test result that changes your life, only to find out it wasn’t even yours.
Specimen mix-ups, lost samples, and repeat tests happen more often than you’d expect. In fact, nearly half to two-thirds of lab errors occur before testing even begins. These mistakes can delay care, cause misdiagnosis, and hurt a lab’s bottom line through claim denials and lost revenue.
In this blog, we’ll look at how specimen errors affect both patient care and billing, and how better processes and more intelligent systems can prevent them.
What are Laboratory Errors?
Laboratory testing plays a critical role in diagnosis and treatment decisions, yet it’s more vulnerable to error than most realize. These include mislabeling, contamination, delayed transport, and improper handling.
According to the Agency for Healthcare Research and Quality (AHRQ), around 3–5% of lab specimens encounter pre-analytical errors, including mislabeling, contamination, or improper handling.
Here’s what that looks like in real life:
- A woman underwent a double mastectomy after being told she had breast cancer, only to find out later her biopsy had been mixed up with another patient’s.
- A kidney biopsy sample was lost in transit, forcing the patient to endure the invasive procedure a second time.
- Duplicate tests are sometimes performed unnecessarily due to unclear documentation or lost samples, delaying care and creating billing confusion.
And these errors do not stop at patient care, for specimen mix-ups can lead to billing denials, revenue loss, and legal liability, making it both a clinical and financial risk. As such, healthcare providers must implement strict protocols and use a reliable billing system to catch issues before they get out of hand.
How to Reduce Lab Billing Errors and Protect Your Revenue
When specimen errors occur, laboratory billing often suffers the ripple effects. A national survey by Premier found that nearly 15% of all claims were denied across commercial and managed care payers, and while 45%–60% of these denials were later overturned, the appeals process was often lengthy, expensive, and required multiple submissions, causing challenges to finances.
In this section, we’ll list down common errors in claim denials, what they lead to, and what you can do to fix them.
Tightening up your data, documentation, and coding processes doesn’t just improve accuracy, but it also protects revenue and saves time.
Smarter Lab Billing Starts with the Right Lab Integration
Are you still depending on manual data entries? That’s a key ingredient for denial. Modern lab billing systems integrate directly with your Laboratory Information System (LIS), ensuring accurate data flow from collection to payment. These platforms catch red flags before they become financial roadblocks.
The benefits of integrated, lab-specific billing systems go beyond automation, for they help catch the kinds of errors that can quietly cost labs thousands, including specimen labeling mistakes.
Case Study: How Seamless Lab Integration Transformed Billing Performance
Effective lab billing isn’t just about claims, it’s about how well your systems talk to each other from the start. Seamless lab integration is the foundation for capturing clean data, avoiding denials, and securing full reimbursement.
Take the case of a sleep lab practice with two providers. Before partnering with Synapse Lab Billing, they struggled with frequent billing delays and revenue losses, caused mainly by specimen labeling issues and inconsistent documentation, classic signs of a disconnected workflow.
After switching to Synapse Lab Billing, the improvements were dramatic:
- $52,357.31 average monthly increase in collections
- $628,287.72 additional revenue in just one year
- 37.27% improvement compared to previous billing performance
- The lab had never collected more than $200,000 in a single month—but under Synapse, they did it four times, with a peak of $223,511.56
By streamlining the entire billing process, including front-end specimen data validation, accurate coding, and automated claim submission, Synapse helped eliminate costly errors and maximize reimbursement. What once caused revenue leakage, such as mislabeled specimens or missing information, is now proactively caught and corrected before claims are ever sent out.
Stay Ahead with Synapse Lab Billing
Specimen mix-ups, missing codes, and billing errors don’t just hurt patient care; they can also significantly drain revenue. To protect both outcomes and your bottom line, your lab needs to:
- Ensure precise specimen labeling and tracking
- Maintain coding accuracy and compliance
- Catch issues early with pre-bill checks and automation
- Use a billing solution tailored for the way your lab works
That’s where Synapse Lab Billing makes the difference. Our end-to-end platform helps you stay accurate from the very first label, eliminating costly errors before they happen, speeding up collections, and giving you complete visibility into your billing performance.
- Accuracy-first workflows
- Automated validation and compliance
- Real-time data and revenue insights
Stay accurate. Stay efficient. Stay in control with Synapse.
Sources:
Pre-analytical assessment you best be aware of; Lifted from
https://ascls.org/pre-analytical-assessment-you-best-be-aware-of-its-importance/
Medical coding mistakes could cost you; Lifted from
https://www.ama-assn.org/practice-management/cpt/medical-coding-mistakes-could-cost-you
Trend alert: private payers retain profits by refusing or delaying legitimate medical claims; Lifted from
https://premierinc.com/newsroom/blog/trend-alert-private-payers-retain-profits-by-refusing-or-delaying-legitimate-medical-claims
Pre-analytical pitfalls: Missing and mislabeled specimens; Lifted from
https://psnet.ahrq.gov/web-mm/pre-analytical-pitfalls-missing-and-mislabeled-specimens#:~:text=These%20include%20errors%20such%20as,error%20rates%20are%20far%20lower
Specimen almost lost; Lifted from
https://psnet.ahrq.gov/web-mm/specimen-almost-lost
Analysis of pathology closed claims; Lifted from
https://www.svmic.com/articles/59/an-analysis-of-pathology-closed-claims