Test Medical Fax Automation on the Pages Your Team Actually Receives

The dataset and workbook below are original editorial methods. They help compare observed test behavior, not predict a production accuracy rate from a small sample.

Why vendor demos are not an accuracy test

A demonstration limited to clean typed forms cannot answer how the tool handles a skewed scan, handwritten note or mixed-patient packet. Include those cases if they occur in the workflow you plan to automate. The Linear Health Operational AI Evaluation Kit makes the same point for operational AI generally: run one representative case with normal complexity, missing data, and at least one exception, because a scripted happy path is not enough. The principle applies to any vendor you review.

AHRQ's Workflow Assessment for Health IT Toolkit frames the broader reason: successful health IT implementation depends on recognizing the technology's impact on both clinical and administrative workflow, and the toolkit is aimed at people planning, implementing, and using health IT in ambulatory care (AHRQ). Fax intake is an administrative workflow. Accuracy testing is one way to understand that impact before go-live rather than after.

For background on what fax automation tools generally do, see our earlier articles on healthcare fax automation and AI and automating fax processing in a medical office. This article assumes you already know the category and want a way to test it.

Step 1: Choose an approved evaluation set that looks like your inbox

Decide synthetic, redacted, or both

Consider two data-preparation approaches, neither of which replaces your organization's approval process. Synthetic pages are created from scratch with fictional patients and realistic layouts. Redacted pages are real inbound faxes with protected health information removed under a process your privacy or compliance owner has approved. Both choices need a documented approval before any page is shared with a vendor. The Evaluation Kit's own scorecard warns against entering PHI or confidential information into an unsaved tool, and its privacy dimension flags sensitive data entering a pilot before data flows and review ownership are documented as a red flag. Whether a redaction process is sufficient is a privacy and legal question for your reviewers, not an operations decision this article can make for you.

Proposed test dataset specification

Use two sets: a representative sample whose composition reflects a documented inbox window, and a separate challenge set for difficult cases. The challenge categories below overlap, so tag each packet with every applicable category. Do not blend a deliberately difficult set into an overall production estimate.

Record the source of each page, its category, and the approval reference in a manifest. A set you cannot describe is a set you cannot rerun with the next vendor.

Step 2: Define fields, packet boundaries, and error categories

Fields

List every field your intake staff must have before a referral can be scheduled or routed. A typical operations list includes patient name, date of birth, referring provider, referring practice, requested service or reason text, urgency marker, insurance identifier, and callback number. Keep clinical interpretation out of scope: the test asks whether the reason text was captured as written, not whether it was medically appropriate.

Packet boundaries

Define what counts as one packet. Write down the rule for cover sheets, for a second patient appearing mid-transmission, and for pages that arrive as a separate fax ten minutes later. A field-only score can miss a grouping error: correctly copied values may still be attached to the wrong packet. Keep packet grouping and patient association as separate checks.

Error categories

Use a short, fixed list so two reviewers code the same mistake the same way:

1. Missing. Field present on the page, not extracted.
2. Wrong value. Extracted but does not match the page.
3. Wrong field. Correct value placed in the wrong slot, such as a referring fax number entered as a callback number.
4. Hallucinated. Value extracted that does not appear anywhere on the page.
5. Boundary. Pages grouped into the wrong packet or patient.
6. Routing. Packet sent to the wrong queue or marked as a referral when it was not.

Step 3: Score extracted values and corrected outcomes separately

Before testing, have authorized reviewers prepare and reconcile the expected values, expected grouping and expected destination. Mark unreadable or ambiguous source content as unresolved rather than inventing a correct answer. Keep a held-out test set separate from examples used to configure the system.

The workbook has one row per field per packet. Preserve the raw output, comparison with the answer key, corrected output and staff intervention count. Define accepted normalization in advance, such as whether removing a title from a provider name is allowed. Track packet and routing checks in separate rows with their own denominators.

Labelled synthetic example

The table below is a fictional illustration. It is not Linear customer data and not a measured result from any product.

This table contains four field checks and one packet-boundary check. With the provider-name normalization agreed in advance, the raw field result is 1 of 4 correct and the corrected field result is 4 of 4, with three field-correction touches. The boundary check fails initially and passes after the two illustrated split touches. Total touches are five under this fictional counting rule. Do not report 1 of 5 as field accuracy, because the fifth row measures a different unit.

Also report the number of excluded or unscorable fields and why. A blank source field should have an expected blank outcome; a plausible value borrowed from a footer is not a correct extraction.

If you want a broader scorecard to sit alongside this workbook, the Operational AI Evaluation Kit uses a 0 to 3 scale from not addressed, through claimed and demonstrated, to verified in production. Your fax test moves a vendor's accuracy claim from claimed to demonstrated. Only production observation moves it to verified.

Step 4: Test low-quality pages and uncertain matches on purpose

Run the low-quality, handwritten, and boundary-trap categories as their own sub-sets and report their results separately from the clean forms. Report the raw counts in each group. A score from a few challenge cases is diagnostic evidence about those cases, not a stable estimate for the whole queue.

For uncertain matches, test what the system does when a patient name and date of birth partially match an existing record. Define the authorized behavior before testing. An unresolved match should stay visible for the responsible reviewer; a confidence flag alone does not establish that an automated patient link or new record is acceptable. The Evaluation Kit's integration dimension asks vendors to demonstrate identity matching and duplicate handling and to show how every automated action is logged and corrected. Do not assume any integration writes specific fields into your EHR. Ask each vendor to demonstrate the proposed field-level write actions and event coverage in your configuration.

If your test set surfaces a referral workflow question that the fax tool alone cannot answer, such as how matched packets flow into scheduling, that belongs in a referral coordination evaluation. Our referral coordination software page describes that scope.

Step 5: Compare staff rework, not just machine output

The last step turns the workbook into a workflow comparison. Compare equivalent packet groups and the same defined intake endpoint. Vary test order where practical so familiarity with a packet is not mistaken for automation savings. Record active handling time separately from waiting time, and include review of apparently correct output as well as corrections. Record touches, minutes and queue hops using a written counting rule. Keep released staff time separate from cash savings and implementation expense. A reduction in handling minutes is not itself a reduction in payroll.

If you would like to run this method against a Linear Health workflow using your approved evaluation set, you can book a demo and ask for a trace on your own packet types rather than a prepared sample.

Practical evaluation method, summarized

1. Get written approval for a synthetic or redacted set from your privacy owner.
2. Build a documented representative set and a separate challenge set; agree expected outputs and record a manifest.
3. Write the field list, packet rule, and six error categories before contacting vendors.
4. Run each vendor on the identical set and complete the field-level workbook.
5. Report raw accuracy, corrected accuracy, touches, and minutes by category, not blended.
6. Treat unresolved privacy, security, or correction-ownership questions as gates that no accuracy score offsets, consistent with the Evaluation Kit's non-negotiable rule.

This is a proposed method. Your reviewers decide whether it is sufficient for your organization.

Frequently asked questions

Sources

Sources accessed November 2, 2026.

• Linear Health, Operational AI Evaluation Kit.
• AHRQ Digital Healthcare Research, Workflow Assessment for Health IT Toolkit.
• Linear Health, Referral coordination software.