What You Can Build on NHance

NHance does one thing very well: it takes any clinical document and turns it into clean, structured, coded, FHIR-ready data. That can sound narrow. It is not. Once a messy document becomes reliable data, a surprising number of healthcare jobs turn out to be the same job underneath.

That is the useful thing to understand before looking at use cases. NHance is the data layer. You bring the rule for your workflow, and the same engine powers very different products.

One pattern, many workflows

Almost every clinical operations task has the same shape. You start with a document, you apply a rule (a coding guideline, an eligibility checklist, a payer policy), and you produce a decision. The hard, expensive part has always been the first step: getting trustworthy data out of the document. NHance does that step, so the workflow on top becomes straightforward.

Catch the diagnoses that slipped through

The best-known example is risk adjustment. Health plans are paid based on how sick their members are, measured through a CMS model called HCC (Hierarchical Condition Categories). The catch is that a condition only counts if it is documented and coded correctly for that year. Plenty of real, documented diagnoses never get coded because they are buried in a faxed note no one re-read.

Feed those documents through NHance and the documented conditions come out as structured, coded data, each one linked back to the sentence that supports it. Teams use that to find the diagnoses that were genuinely recorded but never captured, and to drop the ones the notes do not actually support.

Turn intake into a decision

The same engine reads intake packets and referrals and checks them against an eligibility checklist. Instead of a person flipping through hundreds of pages to see whether someone qualifies, NHance pulls out the relevant facts and lines them up against the criteria.

Get ahead of prior authorization

Prior authorization is the approval a provider needs from a payer before a treatment is covered. It is slow because someone has to gather the supporting clinical evidence from the chart and match it to the payer's policy. Federal rules (the CMS Interoperability and Prior Authorization rule) are pushing this onto FHIR-based interfaces by 2027. NHance assembles the evidence from the source documents and hands the workflow a structured, traceable package instead of a stack of faxes.

Find the quality measures hiding in the notes

Health plans report quality through HEDIS, a standard set of measures maintained by the NCQA and used by most US health plans. Many of those measures depend on details that live in clinical notes rather than tidy claims data, such as whether a screening was actually done. NHance surfaces those details as structured data, so a quality team can measure from the full record instead of a sample.

It is not only PDFs

Documents are the obvious input, but the same idea works on other unstructured data. Teams have used NHance to turn recorded patient calls into themes and sentiment, and to sort free-text complaints by topic so a quality team can see patterns instead of reading every message. Anything written for humans rather than machines is fair game.

The thread through all of these is simple. None of them are really document problems once the document has become reliable data. NHance does the hard conversion once, and the workflow you build on top, whether it is coding, eligibility, prior auth, or quality, gets to start from clean ground.