How MatchLogic Cut Patient Record Duplicates by 89% and Recovered $1.2M in Denied Claims

Camber

Industry
Behavioral health payments

Scale
100,000+ patients . 40 states ~75 employees . founded 2021

Enterprise-Grade Matching

Camber is a health tech startup headquartered in New York City that streamlines healthcare payments for behavioral health clinics. Founded in 2021 and backed by Y Combinator and Andreessen Horowitz, Camber employs approximately 75 people and manages claims processing for over 100,000 patients across 40 states. The platform focuses on helping pediatric behavioral health clinics, autism centers, and mental health providers get reimbursed faster and more accurately by reducing administrative overhead in eligibility verification, claims submission, and payment reconciliation.

Camber's platform sits between clinics and payers. When a clinic registers a new patient or submits a claim, Camber's engine must instantly verify whether that patient already exists in the system, whether their insurance eligibility is active, and whether the provider is properly credentialed. With patients frequently visiting multiple clinics in the same network, and with data flowing in from dozens of different EHR systems, patient identity is rarely consistent from one intake form to the next.

The Challenge

Camber's engineering team had built a custom SQL-based matching layer to prevent duplicate patient records across their growing network of clinics. The system relied on exact-match criteria, comparing first name, last name, date of birth, and insurance member ID.

The system was breaking down in three critical ways.

Fragmented medical histories

A child with autism visiting Clinic A in January and Clinic B in March would be registered as two separate patients because the parent entered "Jonathon" instead of "Jonathan" or transposed the month and day in the DOB field. Camber's system treated each as a unique record, which meant insurance eligibility checks failed and prior authorization history was lost.

Claims denied due to identity mismatches

When Camber submitted claims to Medicaid or commercial payers, the patient name or DOB on the claim had to match the payer's master file exactly. Even minor variations caused automatic denials. Camber estimated that 8-12% of first-pass claim denials were directly attributable to patient identity mismatches.

We were watching legitimate patients get denied coverage because a front-desk clerk typed 'Michael' instead of 'Micheal' in the intake form. Our SQL engine treated them as completely different people, and the payer rejected the claim. It was costing us and the clinics real money every week.

Match accuracy stuck at 71%

The SQL engine couldn't handle name variations, cross-column mismatches, or insurance ID formatting differences. The data team knew they were missing true duplicates while creating false positives that merged unrelated patients, particularly in high-volume zip codes where multiple children shared the same first name and birth month.

The stakes were high. Every missed duplicate meant a fragmented medical record, which affected care continuity for children with behavioral health needs. Every false merge meant two different patients could be mixed up, creating HIPAA compliance risks and incorrect billing. And with Camber's platform processing thousands of new patient registrations weekly, the matching had to happen in real time during intake. Batch processing was not an option. Even a 60-second delay during clinic registration caused front-desk staff to abandon the verification step and proceed with manual entry.

The Solution

Camber evaluated several matching vendors but kept running into the same limitations: legacy desktop tools with bolted-on APIs that couldn't integrate with Camber's Node.js microservices, black-box ML models that couldn't explain why two patient records matched, or string-only engines that choked on insurance ID formatting and address variations.