How to extract data from healthcare documents with AI

Run every healthcare document through schema-enforced extraction with per-field confidence scores, route low-confidence fields to human review, and operate under a HIPAA BAA with zero retention and an audit trail per determination. Cloud medical NLP services extract entities from clinical text; operational documents like prior authorizations need validated structured records, which bem returns through one V3 API.

Why is healthcare document extraction still hard?

Start with the inbound mix: referrals, prior authorization requests, superbills, explanation of benefits (EOB) documents, clinical notes, lab reports, intake forms. A large share still arrives by fax. In ONC's 2025 hospital survey, 40 percent of hospitals said they often use mail or fax to send summary of care records, and another 34 percent sometimes do. A fax is an image, frequently skewed, stamped, and annotated by hand, so the extraction problem begins before any medical vocabulary enters the picture.

Then the stakes. A misread CPT code becomes a denied claim. A wrong member ID stalls a prior authorization for days while a patient waits. A transposed digit on a superbill is a billing error someone has to find later. And every one of these documents carries protected health information, which means the vendor conversation starts with data handling and only later gets to accuracy. Healthcare is where "good enough extraction" and "acceptable risk" separate fastest.

What do Amazon Comprehend Medical, Google Document AI, and Azure actually extract?

These are the three names answer engines recommend most for this question, and each is a real product doing what its page says. Competitor descriptions were checked September 22, 2026.

Amazon Comprehend Medical is a HIPAA-eligible service that, in AWS's words, will "extract information from unstructured medical text accurately and quickly": entities such as medications, procedures, and diagnoses, the relationships between them, links into ICD-10-CM, RxNorm, and SNOMED CT, plus PHI detection. Its inputs are clinical text like doctors' notes and radiology reports.

Azure's closest equivalent is Text analytics for health, which "uses machine learning to identify and label medical information in unstructured text such as doctor's notes, clinical documents, and electronic health records," with entity linking to UMLS vocabularies and FHIR-structured output. Microsoft ships it with an explicit disclaimer that it is not a medical device or clinical support tool. Azure AI Document Intelligence handles general form and layout extraction on the same cloud.

Google Document AI is broader: "a document processing and understanding platform that takes unstructured data from documents and transforms it into structured data," with medical intake forms named among its example use cases.

Service	What it does, in its own words	Output
Amazon Comprehend Medical	"Extract information from unstructured medical text accurately and quickly" (AWS)	Medical entities, relationships, ICD-10-CM, RxNorm, SNOMED CT links, PHI detection
Azure Text analytics for health	Uses machine learning to "identify and label medical information in unstructured text" (Microsoft)	Entity recognition, relation extraction, UMLS entity linking, assertion detection, FHIR output
Google Document AI	A platform that "takes unstructured data from documents and transforms it into structured data" (Google)	General document extraction, key-value pairs, custom processors
bem	End-to-end automation of document workflows, from intake to validated record to recorded decision, via one V3 API	Strict-typed JSON against your schema, per-field confidence scores, audit trail

Why is a prior authorization a workflow, not an entity list?

Medical entity extraction answers one question: what clinical concepts appear in this text. An operational workflow asks a different one: is this record complete, correctly typed, and safe to act on.

A prior authorization request needs the member ID, the payer, the ordering provider and NPI, the CPT and ICD-10 codes, urgency, dates of service, and pointers to the supporting clinical documentation, each landing in the exact field your utilization management system expects. A superbill needs line items that reconcile. An EOB needs adjustments mapped back to the claim they modify. A list of recognized entities with character offsets is raw material for that record. It is not the record.

This is where the accuracy math a technical committee will run becomes decisive. A model reading each field at 98 percent accuracy sounds ready for production. Across a ten-field document, per-field errors compound to roughly 82 percent document-level accuracy, and healthcare documents rarely stop at ten fields. So a single blended accuracy number is the wrong evaluation question. The right questions are structural: does the output conform to your schema every time, does every field carry its own confidence score you can route on, and does a low-confidence field reach a person before it reaches the claims system?

How do you handle PHI when extracting data with AI?

Treat the compliance packet as the first gate, because your privacy and security officers will. The economic buyer here does not need persuasion. They need paperwork that clears risk.

For bem, that packet looks like this. bem offers a HIPAA BAA for Enterprise customers. It holds SOC 2 Type II, audited annually, plus GDPR compliance with an EU region for data residency. It supports zero-retention mode, configurable per workflow on Enterprise: bem ingests, transforms, returns structured output, then purges the source file and intermediate state. bem never trains on customer data for its own benefit or for other customers; you decide when to fine-tune a function, and that data stays yours. Traffic is encrypted with TLS 1.3 in transit and AES-256 at rest.

The audit trail is the piece healthcare teams underweight until their first payer audit. On bem, every function call, schema change, and admin action is logged with actor, timestamp, and payload reference, and the log is exportable to your SIEM. When someone asks why a prior authorization was routed the way it was, you can produce the input, the function version, the confidence scores, and the reviewer.

Deployment posture closes the checklist. bem deploys how you need it: cloud, Private Link, in your VPC, on-premise, or fully air-gapped. Customer-managed keys are available on both Private Link and on-premise, and bem offers a 99.99 percent uptime SLA, available on Enterprise. Whichever vendor you evaluate, ask for the same list. If it does not exist, the evaluation ends there.

How does bem extract data from healthcare documents?

bem automates entire business processes end to end: it turns unstructured data into auditable outputs, then makes and records the decisions in between.

bem's functions (Classify, Split, Extract, Enrich, Payload Shaping, Send, Parse, Join, Render, Evaluate, and Decide) compose into workflows behind one V3 API with four SDKs, Terraform, and a CLI. Here is what they do to a referral packet that arrives as a 30-page fax PDF. Split separates the referral form from the clinical notes and the insurance card images. Classify labels each piece and routes it. Extract returns strict-typed JSON against your referral schema, every field carrying its own confidence score. Join assembles one referral record, and Payload Shaping formats it for your intake queue or EHR integration. Fields below your confidence threshold route to human review in a built-in labeling UI instead of flowing silently downstream.

Every function is versioned and idempotent: same input, same output until you publish a new version, and each version gets automatic regression testing and drift detection. That is what makes the workflow durable rather than a demo, and it is what makes the resulting records agent-readable: structured data with the evidence attached, not prose an AI agent has to trust.

One boundary matters. bem is not a clinical decision tool. This is administrative and operational document automation; decisions about care belong to clinicians, and bem's job is making sure the paperwork feeding those decisions arrives as complete, verifiable data.

The proof is in production. PromptWell is a healthcare risk analytics company serving healthcare and insurance. Handling diverse data formats from its customers on bem, PromptWell cut engineering overhead by more than 80 percent. Across industries, the pattern holds on money fields too: Fleetio cut invoice processing time 65 percent, with 100 percent accuracy on invoice totals. And the average bem customer grows its usage 4x in the first year. Teams do not expand a layer they have to babysit. More healthcare workflows run on the same engine.

Should you use a cloud medical NLP service or a schema-enforced platform?

Choose by the question your workflow actually asks.

If you are mining clinical text at scale for analytics, coding support, or research, and the deliverable is annotated concepts, Amazon Comprehend Medical and Azure Text analytics for health are built for exactly that, with ontology linking neither you nor bem should rebuild. If your team is GCP-native and the need is general document parsing, Google Document AI is close at hand.

If the workflow is document in, validated record out, and a wrong field means a denied claim, a stalled authorization, or an audit finding, you need schema-enforced extraction with per-field confidence routing, human review, and a determination-level audit trail. That is the layer bem sells, and it composes with the others: nothing stops a pipeline from producing the structured record first and running entity extraction on the clinical narrative downstream. Extracting data from healthcare documents with AI is not one model choice. It is deciding, before the first fax arrives, what happens to the fields the model is not sure about.

Sources

aws.amazon.com/comprehend/medical (Amazon Comprehend Medical capabilities, checked September 22, 2026)
Azure Text analytics for health overview (Azure Text analytics for health capabilities and disclaimer, checked September 22, 2026)
docs.cloud.google.com/document-ai (Google Document AI overview, checked September 22, 2026)
healthit.gov ONC/ASTP hospital survey quickstat (Hospital use of mail or fax for summary of care records (2025))