Work Order Documentation Quality: Why Every Shop Defines It Differently
"Good to go."
Three words, and the work order is closed. Monty Gonzales named this pattern in AAMI News years ago, and his point was not that technicians are careless. It was that a note like that reduces real work to nothing, and leaves the department holding the liability. Garbage in, garbage out.
Every HTM leader agrees the note is too thin. Almost nobody agrees on what should have been written instead.
That disagreement is the actual problem, and it is why twenty years of fixes have not moved work order documentation quality much.
What does a good work order note look like?
Ask five HTM shops and you get five answers.
Across HTM programs we keep hearing the same structure underneath: the shop has a house format for closing notes, it is taught and repeated and audited against, and it looks nothing like the shop's next door. Some run on an approved vocabulary, a phrase the department wants on every job of a given type. Some run on a required structure, a set of things the note has to cover before it counts as finished. Same requirement underneath. Two incompatible shapes.
Then there is the gap between having that standard and the records reflecting it, and that gap is currently bridged by hand. Reminders in team meetings. Notes stuck near a monitor. A supervisor reading a sample of last week's work orders and sending a few back.
That is why generic advice about "better documentation" bounces off. A shop does not want better writing. It wants writing in its own shape.
Haven't we already tried to fix this?
Yes, and the fixes are sensible. They just all land in the same place.
Ted Cohen and Matt Baretich laid out the standard toolkit in 24x7: written field definitions so every person uses a field the same way, standardized activity coding, real-time error checking that warns or stops on a bad entry, and periodic sampling audits by supervisors to catch whether work orders are "documented in sufficient detail."
Notice where every one of those sits. Field definitions, dropdowns and required minimums shape the container. Audits and sign-offs read the container afterward. Both operate on either side of the moment a technician decides what to say.
And the container has a ceiling. Cohen and Baretich say it plainly about coding: dropdown lists standardize data, but sometimes at the expense of detail. You can force a field to be filled. You cannot force it to be true, complete, or comparable.
Allison Woolford put the practitioner version of this in TechNation. You fix a vital signs monitor. Do you write "passed manufacturer's specifications," or do you write which part you replaced, its part number and price, and the steps you took to verify the device was working? Both notes pass the required-field check. Only one of them is a record.
What does an inconsistent record actually cost?
The cost never shows up as a documentation line item. It shows up four other places.
Comparison stops working. Cohen and Baretich wrote that HTM "has done a poor job of standardizing field definitions and establishing data collection guidelines," and that the resulting inconsistency makes meaningful comparison very difficult. If your five sites write notes five ways, you do not have one fleet's history. You have five archives that happen to sit in one CMMS.
Your AEM position gets softer. An alternative equipment maintenance strategy is an argument built out of your own service history. Records that vary by who happened to write them are a weak foundation for changing an interval.
Repairs are where it hurts most. In conversations across multiple service organizations, the same thing keeps coming up: corrective work carries the most inconsistent resolution notes of any job type. That is the opposite of where documentation tooling usually gets aimed. PM checklists are structured by design. The repair, the job with the most to say, is a free text box.
Every analytics ambition inherits it. More than one organization has told us their own AI and reporting plans are held up by the state of their existing notes, not by the tooling. Failure trends, capital planning and any model you point at your CMMS all read the same field a technician filled in at the end of a long shift.
So what is the actual test?
Not length. Woolford's framing is the useful one: documentation is a window into the life of the equipment, and anyone involved has to be able to open it later and understand what happened. The person reading your work order in eighteen months is a colleague you have not met, an incident reviewer, or a surveyor. They were not there.
That test does something a character count cannot. It scales with the job. A thermometer swap and a multi-stage repair on an angiography system need very different notes, and any fixed floor is wrong for one of them.
What actually improves work order documentation quality?
Move the fix earlier, and let it take the shop's shape.
The reason "Good to go" survives is not attitude. It is timing. The note gets written after the work, from memory, in a text box, at the end of a day that already ran long. Everything the department does to raise quality happens later still.
Capture at the moment of work changes what there is to standardize. When a technician narrates what they are doing while they are doing it, the detail is still in the room. The part number, the reading, the thing they tried first that did not work, the reason they deviated from the procedure. Structure gets applied to what was actually said, not reconstructed from what someone can still remember at 4:30.
That is what Leera AI does. It sits on top of your CMMS, listens while the work happens, and writes the note into your fields in your format. And the second half matters as much as the first: a tool that produces beautifully written notes in the wrong shape is worth nothing to a shop that spent years teaching its own. Configurability is the product, not the prose.
One rule underneath all of it, from a service leader who put it better than we could: if the AI wrote it, I did not mean it. The technician narrates, the technician reviews, the technician owns the record. That is the design, not a setting. The liability sits with the person who signed the work order, and no documentation tool gets to move it.
Your shop already knows what a good note looks like. The problem was never the standard. It was that the standard lived on paper and the record lived somewhere else.
FAQ
What is work order documentation quality? It is whether a closed work order lets someone who was not there reconstruct what happened: what failed, what was checked, what was done, what parts were used, and how the device was verified. It is not the same as compliance. A work order can satisfy every required field and still fail this test.
Why do technicians write short work order notes? Because the note is written after the job, from memory, in a free text box, usually at the end of a shift. The detail decays between the work and the writing. Monty Gonzales made this point in AAMI News: notes like "Good to go" are a symptom of how documentation gets captured, not a measure of how much the technician cares.
Do mandatory fields and character minimums fix it? They raise the floor and they cap nothing else. A required field can be satisfied with padding, and a fixed character count is too long for a thermometer swap and far too short for a multi-stage imaging repair. The right length is a function of repair complexity, which no threshold can express.
Can we benchmark our HTM program without fixing this first? Not reliably. Benchmarking assumes the underlying records mean the same thing across sites and technicians. HTM consultants Ted Cohen and Matt Baretich have been explicit that inconsistent field definitions and data collection practices make meaningful comparison very difficult, and that gap starts at capture, not at the report.
Dima Okhrimchuk is the founder of Leera AI, which builds voice-first documentation for HTM teams. Leera AI is built in the field with HTM teams at major US health systems.