(501) 830-1478 Request a Demo →
AccuCode performs medical coding and clinical quality abstraction for US health systems. We process the entire patient record, including the unstructured prose where most of the clinical signal actually lives, and return every output cited to the source documentation. Two products. One underlying technology. Built entirely in the United States.
AccuCode runs on a single engineering stack designed to do what generic healthcare AI cannot: read the entire patient record and parse the unstructured clinical prose with accuracy that matters. The two products below configure that stack for two different deliverables.
Autonomous assignment of ICD-10, CPT, and HCPCS codes across every specialty, with a source-cited justification for every code. Cardiovascular-optimized through our MedAxiom partnership.
Fully automated abstraction, calculation, and submission of quality measures to CMS, The Joint Commission, and specialty registries. Gold-standard abstraction methodology, not pattern matching.
Most healthcare AI competes on the prompt or the workflow. AccuCode competes on what the system sees, and on how accurately it reads what surfaces from below. Two proprietary capabilities make the difference between processing a summary and processing the entire iceberg.
A typical inpatient chart runs hundreds of pages: admission notes, progress notes, consults, imaging reads, lab trends, discharge summaries. Most healthcare AI compresses that into a summary before the model ever sees it, because the full record exceeds standard context windows. AccuCode's architecture processes the whole chart, including unstructured data and even handwriting. The signal that disappears in summarization is the signal we use to provide the most accurate output.
Roughly eighty percent of clinical information lives in unstructured documentation. The discrete fields in the EHR are the cover sheet. The chart itself, the clinical reasoning, the qualifying detail that determines whether a code or an abstraction is correct, lives in prose. AccuCode's parsing of that prose performs at accuracy levels that significantly exceed published industry standards. The methodology behind that claim is detailed in our accuracy audit and verifiable on your own data in a pilot.
A claim about accuracy is only as credible as the discipline behind the measurement. The discipline matters more than the headline number, and we apply four standards to every claim we make.
Every record in our validation set has been reviewed against the source documentation by a human reviewer. Not sampled. Not synthesized. Manually confirmed, at a scale most vendors will not commit to.
We measure accuracy against the consensus answer reached after disputed cases are investigated to source documentation. A system grading itself only ever improves on its own definition of improvement.
Independent customer audits, not internal reports, anchor our public performance claims. The Baptist Health case below is one example. Others are available under NDA in evaluation conversations.
Medical coding pilots run on your real charts within four weeks. You see the output, the citations, and the disagreements your current coders will register. The discussion stops being about what we claim and becomes about what we produce.
Four institutional commitments. Each is a question the buyer should carry into every other vendor conversation.
An automated system can be designed to be fast, throughput-maximizing, and confident at all times. We have made different design decisions, four of which are listed below.
Because the structured field is sometimes wrong, and the prose tells you when. A system that trusts the field by default propagates whatever was entered there.
When the documentation is ambiguous, our output reflects that ambiguity. Quiet defaults are how systems hide their errors from the people responsible for catching them.
Self-comparison produces smooth quarterly improvement charts. Comparison against the source documentation is the only measure that survives audit scrutiny.
Every audit cycle surfaces things the system missed or got wrong. Those go in the customer report. They do not get rounded out, smoothed over, or held back for the next release.
Two paths, depending on which product you are evaluating. Both begin with a signed BAA.
Send us one hundred of your own charts. We process them through coding and return every code cited to the source documentation. Real charts, real output, real disagreements with your current coders surfaced for review. Four weeks, end to end.
Request a coding pilot →Quality abstraction requires integration before it can run on your data, so the first conversation is about the registries you submit to and the scope of measures you maintain. We walk through the abstraction interface on synthetic data and lay out what a real deployment looks like for your system.
Request a quality consultation →