Healthcare AI Governance
Clinical NLP for EHR Data: Governing What Models Read, Extract, and Write Back
Clinical NLP reads the part of the record that structured fields never captured. That is also the part where identifiers, errors, and judgment calls hide. Here is how a health system keeps it governed.
Clinical NLP turns free-text notes in an EHR into structured data a model can use. Governing it means deciding which notes a model may read, applying HIPAA de-identification where required, checking output against clinician review, and recording every model version. Kriv AI advises health systems on this work, starting at $200 per hour.
context
Why Free Text Needs Its Own Governance
Most of what a clinician actually thinks is written in prose: the history, the reasoning, the plan, the caveat. Clinical natural language processing exists to read that prose at scale. The same property that makes notes valuable makes them hard to govern, because a sentence can carry a diagnosis, a name, a relative's employer, and an opinion all at once.
Identifiers hide in sentences, not fields
A structured table has a column called name that you can drop. A note does not. The HIPAA Privacy Rule's safe harbor method at 45 CFR 164.514(b)(2) requires that listed identifiers "of the individual or of relatives, employers, or household members of the individual, are removed," and it also requires that the covered entity "does not have actual knowledge that the information could be used alone or in combination with other information to identify an individual who is a subject of the information."
The list reaches well beyond names. It ends with "Any other unique identifying number, characteristic, or code," which is exactly the kind of detail a clinician types into a narrative. A pipeline that strips a name field and ships the note has not de-identified anything. Whether a particular dataset meets the standard is a determination for your privacy officer and counsel.
Two routes, two different burdens of proof
The same section gives a second route. A person with appropriate knowledge of statistical and scientific principles must determine that the risk is very small that the information could be used to identify an individual, and must document "the methods and results of the analysis that justify such determination." For clinical text that analysis is a real piece of work with a named owner, not a configuration flag.
Not every use is de-identified use
A model that summarizes a chart for a clinician inside the health system's own environment is a use of protected health information, not a release of de-identified data. There, the question is different. The Privacy Rule's minimum necessary standard at 45 CFR 164.502(b) says a covered entity "must make reasonable efforts to limit protected health information to the minimum necessary to accomplish the intended purpose of the use, disclosure, or request." That is a design constraint on which note types, which date ranges, and which patient populations a model is allowed to see.
ai gap
Where Clinical NLP Programs Go Wrong
The first failure is scope that nobody wrote down. A team connects a model to the whole notes table because it is easier than choosing, and the question of which note types are in scope is never asked. Some note types carry stricter handling rules than others, and your privacy officer should say which. A register entry that names the note types a model may read, and the ones it may not, prevents most of the later arguments.
The second failure is treating extraction accuracy as one number. A model that finds medication names well may handle negation badly, so "no chest pain" becomes a chest pain finding. It may also confuse a family history with a patient history, or read a template's default text as a clinical statement. Evaluate each extracted field separately, on notes from your own sites, written by your own clinicians, and keep the examples that failed.
The third failure is silent write-back. Output that lands in a structured field can look like a clinician's entry to the next person who reads the chart. Mark model-derived values as such, route them to review before they affect care or billing, and decide in advance who can overrule them and how that is recorded.
The fourth failure is a model that changes without anyone noticing. Vendors update language models, and a prompt that worked in spring can behave differently in autumn. Pin versions where you can, re-run your own test set after any change, and record which version produced which output.
capabilities
What Kriv AI Provides for Clinical NLP
Scope and note-type register
We help define which note types, populations, and date ranges each model may read, who approved that scope, and how a change to it is requested. The register is built so your committee maintains it after we leave.
De-identification decision support
We help structure the question of whether a use needs de-identified data, which method fits, and who documents the result. The determination itself belongs to your privacy officer, your expert, and counsel.
Field-level validation design
We help design a clinician-reviewed test set, field-by-field error categories, and the thresholds that decide whether a model goes live. Clinical acceptance stays with your clinical leaders.
Output provenance and audit trail
We help specify how model-derived values are labeled in the record, how overrides are captured, and which version of which model produced each output, so a reviewer can reconstruct a decision later.
differentiation
How This Differs From Our Related Pages
This page is about the free-text layer: what a model may read in notes, and how to govern what it extracts. Our clinical NLP implementation blueprint covers a self-hosted architecture for the build itself, our page on healthcare AI interoperability governance covers moving data between systems, our HIPAA requirements page covers the rules for AI in clinical settings generally, and our page on auditing an AI agent's decisions covers reconstructing a specific decision after the fact. Use this page when the question is how to govern a model that reads clinical notes.
engagement
How an Engagement Works
We start with a scoping call about which models already read your notes, who owns them, and which decisions are coming in the next two quarters. We then agree a defined scope and deliverables, work alongside your privacy, security, and clinical informatics leaders, and hand over artifacts you own. We do not replace your compliance functions, give legal advice, or make HIPAA determinations for you.
tiers
What You Get at Each Tier
1. Enterprise / regulated organizations
A governance design for clinical NLP: a note-type and scope register, a de-identification decision path, field-level validation criteria, and an audit trail specification your committee maintains.
2. Fractional CTO / AI governance lead
Ongoing ownership of the register, the re-validation calendar after model changes, and leadership reporting while an internal owner is hired or developed.
3. Specialized advisory
A focused session or second opinion on a proposed use, a vendor's de-identification claim, or a validation plan.
rate card
Kriv AI's Rates for This Work
These are Kriv AI's own published rate floors, not an industry average.
| Track | Kriv hourly rate | Typical engagement model | Minimum engagement |
|---|---|---|---|
| Enterprise / regulated (banks, broker-dealers, payment processors) | From $200/hr | Fixed-scope project or retainer | $8,000 |
| Fractional CTO / AI governance lead | $300 to $400/hr | Part-time, ongoing (monthly) | $8,000 |
| Specialized advisory (vendor evaluation, second opinion) | $400 to $700/hr | Hourly, per-session | Varies by engagement |
| Small business | $150/hr | Referred to Kriv AI's partner network | n/a |
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How to Get a Real Quote
The rates above are floors, not a quote. Actual price depends on how many models read clinical text, how many note types and sites are in scope, and how mature your review process is. Book a discovery call and we will scope it honestly.
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Straight answers
Frequently asked questions about Clinical NLP for EHR Data: Governing What Models Read, Extract, and Write Back
What is clinical NLP for EHR data?
It is software that reads free-text clinical notes and extracts structured information such as problems, medications, and findings. Because notes contain identifiers and clinical judgment, it needs governance beyond what structured data needs.
Does removing patient names de-identify a clinical note?
No. The HIPAA safe harbor method at 45 CFR 164.514(b)(2) lists many identifiers, including those of relatives and employers, and requires no actual knowledge that the data could identify someone. Your privacy officer and counsel decide whether a dataset qualifies.
How should a health system validate a clinical NLP model?
Test each extracted field separately on notes from your own sites, have clinicians review the results, keep the failures, and re-run the test after any model change.
Who owns the decision to put an NLP model into production?
Your governance committee and clinical leaders own it. An advisor can structure the evidence and the review path, but acceptance and compliance determinations stay with the health system.
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Bring your requirements to a working session with the person who'll actually deliver.
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