Every diagnostic lab knows the problem. The records that show what a patient needs, and that let your team order, run, and interpret the right test, already exist. They just can’t be seen, found, or relied on when your team needs them. So testing is delayed. And downstream, claims are denied: you can only bill for a test you can document as medically necessary, and you can only document medical necessity with the records you actually have.
For most labs, that math does not work out. The information exists. It is just not where you need it.
A patient who arrives at your lab for testing has likely seen multiple specialists, visited multiple care sites, and generated records scattered across dozens of providers. According to Pham et al., NEJM 2007, a patient’s history is commonly fragmented across six or more providers per patient.
Your lab needs the patient’s prior clinical history to run and interpret the test correctly, the same records that also support medical necessity along the way: pathology reports, tumor staging, node status, physician notes, prior treatment history. These records exist. They sit in EHRs, health information exchanges, referring physician offices, and imaging centers. But they do not arrive together, in a usable and unified format, when your team needs them.
The result is a match rate problem. Your lab can see the patient, but it cannot connect that patient to the care history required to proceed.
What happens when records are missing
When documentation is incomplete, the downstream consequences compound quickly.
Eligibility stalls. Patients wait longer for testing because your team cannot confirm medical necessity without the supporting records. Manual chart abstraction time scales sharply with case complexity . Multiply that across hundreds or thousands of cases and the operational cost becomes staggering.
Claims get denied. Around 82% of denials are avoidable, often tied to documentation that exists somewhere in the patient’s history but never reaches the right workflow at the right time (Change Healthcare 2022 Revenue Cycle Denials Index The Change Healthcare 2022 Revenue Cycle Denials Index ).
Claim denials cost providers an estimated $25.7 billion in 2023 attempting to overturn denied claims, a 23% increase from the prior year. (Alkire MJ, Saha S, Ingram M. “Claims adjudication costs providers $25.7 billion – $18 billion is potentially unnecessary expense.” Premier, Feb 24, 2025.)
Revenue leaks. Every denied claim, every delayed test, every patient who falls out of the workflow represents revenue your lab earned but cannot collect. The problem is not clinical capability. It is documentation.
Why existing approaches fall short
Most labs address this problem with some combination of manual effort (e.g., calling providers directly, the use of fax, and “chart chasing” out of multiple EMRs) and basic interoperability tools.
Manual chart abstraction works, but it does not scale. A trained abstractor can pull together a patient’s history if given enough time. Manual abstraction can take up to an hour per complex patient, the economics collapse for any lab processing meaningful volume.
Interoperability platforms move data, but they do not make it usable. Connecting to a health information exchange gives you access to records. It does not give you the structured patient history your eligibility and reimbursement workflows require. Nearly 80% of the data in the 1.2 billion clinical care documents the U.S. produces annually is unstructured. Healthcare Dive
Access to more unstructured data does not solve the problem. It makes it bigger.
EHRs store data, but they were designed for billing and documentation at the point of care, not for assembling a cross-system patient history from dozens of external sources.
The fragmentation problem is not a data access problem. It is a data usability problem.
That gap is also where most fixes stop short. Most interoperability tools aren’t up to the task of structuring documents before they move them. Our structuring approach was built and tested at an oncology level of complexity and designed to solve usability, not just access, for all medical records.
What a fix for this actually looks like
This is where the xCures® Clinical Clarity Engine comes in. As part of your treatment relationship with the patient, it retrieves the prior history your team needs to run and interpret the test, then structures what it finds into information your intake and eligibility workflows can use.
The xCures Clinical Clarity Engine assembles and structures a patient’s scattered medical history into decision-ready documentation, built and tested at an oncology level of complexity.
It was built and tested against millions of cancer-related records, and once you can solve fragmentation for oncology, the hardest case in medicine, you can solve it anywhere. In validation testing, 99.7% accuracy across 6,399 reviewed elements. Validated Clinical Checklists for Scalable LLM-Based EHR Data Extraction – Stuhlmiller, Rabe, Lui, Mahoney, Kramer, Newton, Wong.
*Results based on a retrospective analysis of a defined historical dataset and do not guarantee future performance.
See xCures in action.
Book a free 30-minute demo, no prep needed.
Why are diagnostic lab claims denied for missing documentation?
Around 82% of claim denials are avoidable and often trace back to documentation that exists somewhere in a patient’s history but never reaches the right workflow in time.
How long does manual chart abstraction take per patient?
Trained abstractors typically spend 30 to 60 minutes per complex patient pulling together a comprehensive record set, a cost that does not scale across meaningful patient volume.
How many providers typically hold pieces of one patient’s medical history?
A patient’s history is commonly fragmented across six or more providers, spanning EHRs, health information exchanges, referring physician offices, and imaging centers.
Is there a tool built specifically to fix this?
The xCures® Clinical Clarity Engine retrieves a patient’s prior history and structures it into Decision-Ready Checklists and an Automated Patient History your team can act on, turning fragmented records into Evidence-Grade Data. It was built and tested against millions of cancer-related records, reaching 99.7% accuracy across 6,399 reviewed elements in validation testing. *
*Results based on a retrospective analysis of a defined historical dataset and do not guarantee future performance.