AI doesn't replace your laboratory. It removes repetitive work from it.
Every claim on this page describes assistance, not autonomy. The automation layer reads documents, extracts data, checks it against rules and routes what it cannot resolve to a person. Nothing clinical is decided by a model.
Document → classification → extraction → matching → validation → human review → LIS
The automation does the mechanical part. The validation and review layers around it are what make the output trustworthy.
Automated path
- DocumentFax · upload · API
- ClassificationWhat is this page?
- OCR & extractionField-level, with confidence
- Patient matchKnown, new, or possible duplicate
- ValidationNPI · dates · required fields
Anything that does not clear validation leaves the automated path here.
Human review
A person sees the extracted values next to the source page, corrects and approves.
- Confidence below the configured threshold
- A validator failed — invalid NPI, implausible date, missing required field
- Close match to an existing patient, flagged as a possible duplicate
- A requested test that is not in the catalog
- Document type that does not belong in this queue
Order created in the LIS
With the original document attached, per-field confidence recorded and every edit attributed.
How we describe what this does — and what it does not
Overclaiming in healthcare automation causes real harm, so these boundaries are stated on the page rather than buried in a contract.
AI-assisted, not AI-decided
Extraction and analysis produce proposals. People confirm them. The platform enforces that sequence rather than trusting it.
Human-in-the-loop by design
Low confidence, failed validation and possible duplicates are held for review. There is no path where uncertain data silently becomes an order.
No clinical or coverage decisions
The automation layer does not decide what a result means, whether a test is medically necessary or whether a claim should be paid. It assembles information for the people who do.
Every step is attributable
Extracted values, edits, approvals and rejections are logged with who or what produced them, so a wrong record can be traced to its source.
Four areas where automation earns its place
Document AI
Turning an inbound document into structured, checkable data.
OCR
Text extracted from faxes, scans and photographs, including poor-quality source material.
Requisition extraction
Order details read off the requisition — tests requested, diagnosis codes, collection details.
Patient demographic extraction
Name, date of birth, address and contact details captured field by field with confidence.
Insurance extraction
Payer, plan, member ID and subscriber read from cards and requisitions.
Test and order extraction
Requested tests mapped against your catalog, with unmapped selections flagged.
Document classification
Order form, insurance document, medical record or result report — identified before processing begins.
Workflow AI
Deciding where a piece of work should go, and noticing when something is wrong with it.
Document routing
Documents routed to the right queue based on what they are and which laboratory they belong to.
Missing information detection
Required fields that are absent or unreadable identified before the record moves downstream.
Duplicate identification
Records that closely match an existing patient flagged as potential duplicates rather than silently merged.
Order routing
Orders directed by test configuration, facility rules and specimen requirements.
Exception identification
Anything that fails a validation rule held with the specific reason attached.
Work queue creation
Exceptions grouped into queues a person can actually work through.
Billing AI
Finding the problems that stop a claim being paid, while there is still time to fix them.
Billing issue identification
Orders missing the information a payable claim requires, surfaced before submission.
Missing information detection
Gaps in demographics, coverage or documentation flagged against the claim.
Coding workflow assistance
Coding suggestions derived from the catalog and the order, presented for staff to confirm.
Work prioritization
Billing queues ordered by what is actually blocking payment and by recoverable value.
Denial pattern identification
Repeating denial causes surfaced by payer, client and reason.
Operations AI
Watching the laboratory's own throughput and telling you when it changes.
Workload monitoring
Volume and pending work by queue, by stage and over time.
Turnaround-time monitoring
TAT measured per stage, so a slowdown is attributable rather than general.
Productivity insights
Throughput by queue and team, based on the work records themselves.
Anomaly detection
Volume, rejection and exception rates that deviate from the laboratory's own pattern.
Operational summaries
Plain summaries of what happened in a shift, a day or a week.
The review queue is the product
What the automation could not resolve, why it could not resolve it, and the source page next to the extracted values.
Documents processed
Passed validation
Held for review
Median to order
Review queue — held for a person
| Document | Type | Held because | Confidence | State |
|---|---|---|---|---|
| FAX-90412.pdf | Requisition | Member ID unreadable | 0.61 | Review |
| FAX-90413.pdf | Requisition | Possible duplicate patient | 0.94 | Confirm |
| UPL-11208.pdf | Results report | Not an order — classified out | 0.98 | Rejected |
| FAX-90416.pdf | Requisition | NPI failed checksum | 0.77 | Review |
| FAX-90418.pdf | Requisition | Test not in catalog | 0.88 | Review |
| FAX-90419.pdf | Insurance card | Routed to coverage queue | 0.96 | Routed |
Field confidence
- Patient name97%
- Date of birth94%
- Ordering NPI89%
- Payer / member ID72%
- Requested tests91%
Nothing below threshold becomes an order without a person approving it.
Illustrative interface. Figures are examples, not customer data.
AI agents
Agent-shaped automation is where this is heading. These are described as potential future capabilities, not shipped features — they are listed so you can see the direction, not so you can plan against them.
eFax Agent
PotentialMonitors the inbound queue, classifies and prepares documents for review.
Data Entry Agent
PotentialCompletes routine records within confidence thresholds and escalates the rest.
Eligibility Agent
PotentialResolves coverage questions and prepares exceptions for staff decision.
Billing Agent
PotentialPrepares claims and prioritizes the work blocking payment.
Results Agent
PotentialPrepares results for verification and manages delivery and reconciliation.
Patient Communication Agent
PotentialHandles routine notification workflows the laboratory defines.
Operations Agent
PotentialWatches throughput and surfaces what changed and why it matters.
PLACEHOLDER — none of the agents above is a shipped feature. They describe direction, not availability.
AI questions
Does AI ever create an order without a person seeing it?
The platform is designed so that records failing validation, matching an existing patient too closely, or falling below a confidence threshold are held for human review. Which thresholds apply, and whether any category of record may pass through without review, is a configuration decision your laboratory makes explicitly.
What happens when the document is unreadable?
It becomes an exception with the reason attached, rather than a partially populated record. Illegible pages, misrouted documents and unrecognized formats are routed to a queue for a person to handle.
Does the AI decide medical necessity or coverage?
No. It reads documents, extracts fields, applies validation rules and surfaces information. Medical necessity, coverage determinations and clinical interpretation remain human decisions.
Can we see why the system did what it did?
Extraction results carry per-field confidence and the validation outcome, and every change is attributed. When a record is held, the specific reason is shown to the reviewer rather than a generic failure.
AI Eligibility Intelligence
The same approach applied to coverage.
eFax Management
The managed service running on this pipeline.
Data Entry Services
Trained staff working the review queue.
Automating requisition processing
What automating that queue actually involves.
LIS
Where a confirmed record becomes an order.
Security
Audit logging, attribution and access control.
Ready to modernize your laboratory?
Whether you need a complete LIS, billing infrastructure, workflow automation, integrations, or managed operational support, let's build the right solution for your laboratory.