How to reduce errors in lab reports

A "lab report error" is really several different problems wearing one name. A wrong result can start with a mislabelled tube at collection, a value typed in wrong at the keyboard, or a report signed without anyone noticing a flag. Each of these needs a different check, applied at the point where that kind of error actually happens — not a single rule of "read the report carefully," which mostly catches whatever is left over once everything upstream has already gone wrong.

Where the errors actually start

Reviews of laboratory error, in India and elsewhere, consistently find that more errors originate before a sample reaches the analyser than during the test itself — collection, labelling, handling and transport issues such as haemolysis, inadequate volume, wrong anticoagulant, or a sample held too long before processing. The exact share attributed to this stage varies widely between studies, from under a fifth to well over half of all recorded errors, depending on how a study defines and counts an error. What is consistent is the direction: by the time a sample is on the bench, a meaningful part of the eventual error has often already happened. A 2024 review of preanalytical error sources and the controls used against them is a useful primary read on this; see it on NCBI.

That means a lab that only audits the finished report is looking in the wrong place for a large share of its errors. The checks have to sit earlier.

Before the bench: checks at collection and intake

Three checks catch most of what starts here, and all three are about not letting a problem move past the point it happened:

• Identify the patient and the sample together, at collection. Two identifiers checked against the request form, at the time of the draw, catch a mismatch before it ever becomes a result. Checking after the fact relies on someone noticing a result that looks wrong for that patient, which does not always happen.
• Label at the point of collection, not afterwards. A tube labelled away from the patient, even briefly, reintroduces the exact risk the first check was meant to remove.
• Reject rather than interpret around a bad sample. A visibly haemolysed, clotted, under-filled or wrongly collected sample should be rejected and redrawn against written criteria, not run with a mental note to "keep it in mind" when reading the result.

Track rejections by collection point or by staff member. A rejection rate that keeps coming from the same place is a training gap, not a run of bad luck, and it is far cheaper to fix at the source than to keep catching downstream.

Manual entry: the step that doesn't need to exist

Any value a person types from a screen, a printout or a dictated note into the record is a point where a correct result can still end up wrong in the report. This is not a hypothetical risk: a published study of manually entered point-of-care glucose results found that a meaningful share of hand-entered values did not match the instrument's own reading, including some mismatches large enough to matter clinically — see the study on NCBI. Where a value has to be typed by hand, a second independent entry, or a system check against the expected range for that test, catches some of these mismatches — though both add time, and neither is a substitute for not typing the value at all. Connecting an analyser directly to the LIS removes the step rather than double-checking it; see how analyser–LIS integration actually works for what that connection involves and where a person still has to sign.

Sign-off: the last check, not a formality

A signature on a report should mean someone looked at the result in context — the flags on it, the patient's previous value for the same test, whether the specimen matches what was ordered — not that the report reached the top of a queue. Where volumes make that hard to sustain, the practical answer is to make the context visible at the point of signing rather than leaving the reviewer to remember or look it up separately: flags computed against the test's own reference range, and the previous result sitting next to the new one.

This is also where a lab's records matter for more than catching the error once. ISO 15189, the standard NABL accredits against, expects a documented trail from sample to signed report and a record of who did what when — the same records that let a lab show an assessor how an error was caught, not just that it was. See what NABL accreditation asks of a lab's records for what that trail needs to look like.

Track it as a rate, not an incident

A single wrong report gets fixed and forgotten. A lab that wants the rate to actually fall needs to know its baseline: amended or corrected reports as a share of total reports issued, broken down by where each one started — collection, entry or sign-off — rather than one combined number. A category that is not falling tells you exactly where to put training or a stricter check next, which a total figure cannot.

Where Raydian LIS fits

Results from a connected analyser write themselves into the right field against the right patient, and high, low and critical flags are computed from the range configured for that test rather than decided from memory; a result the system cannot match to an open sample is held for a person to resolve rather than guessed at. See analyser integration for how this runs in an Indian lab. Inside the system, access is role-based rather than open to everyone at the counter, and sensitive actions are kept in an immutable audit trail, so a correction is a recorded change rather than a silent overwrite — see how LIS security works.