Is missing clinical records delaying your reimbursement?
Every missing chart note is time your team doesn’t have and revenue you can’t afford to lose. Chasing physician offices for records that should already be on file eats into staff hours and stalls reimbursement week after week.
You can now automate your medical record retrieval end to end. We match, structure, and link every relevant chart note to its source, then package it into Decision-Ready Checklists your team can act on immediately.
Fewer manual chases, cleaner submissions, and claims that move through payer review faster.
Trusted across healthcare
82%
Of healthcare claim denials are avoidable, primarily driven by documentation gaps
30–60 Min
Staff time saved per complex encounter by eliminating manual chart chasing
99.7%
Extraction accuracy achieved in published peer-reviewed validation testing
The Reimbursement Bottleneck
The real cost of missing clinical notes at test accessioning
Around 82% of claim denials are avoidable.
Most occur because test requisitions arrive without the clinical history required to establish medical necessity.
Incomplete Test Requisitions
Referring clinics routinely omit staging, prior therapy records, and specific ICD-10 diagnosis details, stalling accessioning before testing starts.
Costly Chart Chasing
Reimbursement analysts spend hours calling provider offices and sorting through hundreds of faxed pages to locate a single clinical justification.
Margin-Eradicating Rework
Reworking denied claims costs between $25 and $118 per encounter in staff labor alone, stretching days in accounts receivable and depressing lab margins.
Operational Delivery
How decision-ready documentation closes the loop
xCures connects nationwide health networks directly to your billing workflow, converting unstructured records into verifiable claim evidence.
Rapid Record Retrieval
Assembles complete patient clinical history across nationwide networks, direct integrations, and incoming faxes without requiring physician outreach.
Validated Extraction
Validated clinical extractors isolate the exact data elements needed to prove medical necessity for your diagnostic panel.
Source-Linked Documentation
Delivers concise, decision-ready documentation with clickable citations directly back to the original clinical note or pathology report page.
Rigorous Validation
Evidence-backed accuracy built for complex oncology records
Our platform was developed and tested against millions of cancer-related medical records where clinical nuance and document complexity are highest.
Published Accuracy Benchmark
In validation testing, our technology demonstrated 99.7% accuracy across 6,399 reviewed clinical 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.
*The above is a customer testimonial reflecting individual experience under specific conditions. They are not guarantees of comparable or future outcomes.
Frequently asked.
What causes most healthcare claim denials at test accessioning?
Around 82% of claim denials are avoidable. Most happen because test requisitions arrive without the clinical history needed to prove medical necessity, like staging, prior therapy, or specific ICD-10 details.
How does xCures reduce time spent chasing missing clinical records?
xCures retrieves complete patient clinical history across nationwide networks and direct integrations, without needing physician office outreach or manual fax sorting.
What does “source-linked documentation” mean?
Every extracted clinical element links back to the original medical record page, so your billing team has traceable evidence for each claim, not just a summary.
How much does claim rework actually cost a lab?
Reworking a denied claim costs between $25 and $108 per encounter in staff labor alone, on top of the delay to accounts receivable.
How is xCures’s extraction accuracy validated?
xCures validates its clinical extractors against peer-reviewed testing on complex oncology records. (Flagging: the page currently cites two different accuracy figures for this, 99.7% across 200+ comorbidity assessments vs. 99.7% across 6,399 elements. I’d hold off finalizing this FAQ’s number until that’s resolved, since I don’t want to bake an unconfirmed stat into schema.)