A patient arrives on an acute ward feeling unwell. A finger-prick sample goes onto the ward's point-of-care analyser, and a number comes back that sits just on the wrong side of a decision threshold. The team acts: a treatment is started, an escalation is made, a phone call goes to the on-call registrar. A few hours later a venous sample drawn at the same time reaches the main laboratory, runs on a large automated platform, and the number that comes back is just on the right side of the same threshold. Same patient, same blood, same analyte, two devices, two verdicts. Somebody now has to decide which number the patient is.
Most people who work in point-of-care testing have felt this moment, even if they have never given it a name. A ward result and a lab result that "should" agree, but do not, quite. A value that trends beautifully until the day it was measured on a different machine and then jumps for no clinical reason. A reference range printed on a report that was quietly borrowed from a different method. We tend to file these under "point-of-care is less precise" and move on. That is not wrong, but it is not the real story either.
The real story is this, and it is the thesis I will defend: the same blood tested on two different point-of-care platforms can legitimately give two different numbers, and near a clinical decision threshold that difference can change what happens to the patient. Harmonisation and standardisation across POCT methods is unfinished business. Method-dependent reference ranges and decision limits are a patient-safety issue hiding in plain sight, and until the science is finished, clinical caution near thresholds is the safety net we cannot switch off.
Why two analysers can disagree about the same blood
It is tempting to assume that measuring, say, a glucose or a haemoglobin is a fixed physical fact, like weighing a bag of flour, and that any well-made device should land on the same answer. That intuition is the trap. Two analysers are not two scales reading the same weight. They are frequently two different chemistries, answering a slightly different question, and then expressing the answer on a scale that may or may not be tied back to the same reference.
There are a handful of reasons the numbers drift apart, and they are worth naming plainly because each one is a real, recognised phenomenon in laboratory medicine, not a fault to be blamed on a single machine.
Different measurement methods
The same analyte can be measured by genuinely different reactions. One device may use an enzymatic method, another an electrochemical sensor, another an immunoassay built around a particular antibody. Each method has its own response, its own interferences and its own quirks. An immunoassay depends on which part of the target molecule its antibody recognises, and two antibodies raised against the "same" marker can see different things in the same sample. An enzymatic reaction responds to its substrate and to whatever else in the blood happens to nudge that reaction. These are not defects. They are different instruments playing the same note in different keys.
Calibration and traceability
A raw signal, a colour change or a current, means nothing until it is converted into a concentration. That conversion depends on how the device was calibrated and, crucially, on what its calibration is traceable to. Where a recognised international reference material and reference method exist, and where every manufacturer ties its calibration back to that same anchor, results converge. Where that chain is incomplete, or where different manufacturers anchor to different materials, two correctly working devices can sit on subtly different scales for reasons that have nothing to do with the sample in front of them.
Matrix and sample type
Point-of-care testing often runs on whole blood from a fingertip; the laboratory often runs on plasma or serum from a vein. The matrix matters. Whole blood and plasma are not interchangeable for every analyte, and a device tuned to report a plasma-equivalent value from a capillary drop is doing a conversion that carries its own assumptions. Add the ordinary variability of capillary sampling, a squeezed finger, a first drop, a cold hand, and you have another honest source of difference between the ward number and the lab number.
Two analysers are not two scales reading the same weight. They are two different chemistries answering a slightly different question, then expressing the answer on a scale that may not share the same anchor.
None of this is exotic. Different results from different methods for the same analyte is a long-standing, recognised problem in laboratory medicine, and it is precisely why international bodies run formal harmonisation and standardisation programmes to pull methods back towards a common answer. Those programmes exist because the problem is real and the work is not finished.
Why it matters most exactly at the threshold
Here is the part that turns a measurement-science curiosity into a clinical-safety question. A difference between two devices is usually clinically irrelevant. If one machine reads a value comfortably in the healthy range and another reads it a little differently but still comfortably in the healthy range, nobody is harmed and nothing changes. The difference only bites in one place: at a decision threshold, where a small numerical gap flips the clinical action from one thing to its opposite.
Clinical medicine is full of these lines. Above this value we treat; below it we reassure. Above this value we admit; below it we discharge. Above this value we anticoagulate, transfuse, escalate, repeat urgently; below it we watch and wait. The threshold is a cliff edge drawn across a continuous number, and a patient whose true value sits near that edge is in the danger zone, because now the ordinary, honest disagreement between two devices is enough to put them on different sides of the cliff.
Imagine, purely for the sake of argument, a decision threshold set at a value of 10. A patient's true concentration is genuinely close to 10. One device reports 9.6 and the clinician reassures. Another reports 10.4 and the clinician acts. Neither device is broken. Neither clinician is wrong given the number in front of them. The measurement spread of a single true value has straddled the line, and the patient's care has forked on which machine happened to be in the room. Multiply that by every threshold, every ward, every clinic and every device in a health system, and the scale of the exposure becomes clear.
The quiet unsafety of a borrowed reference range
If method-dependence stopped at the raw number, it would be manageable. It does not. The deeper trap is that reference intervals and clinical decision limits are themselves method-specific. A reference range is not a property of the human body alone; it is derived on a particular method, in a particular population, and it only means what it says when it is read against a result produced by that same method.
This is where services get quietly caught out. A range gets copied from the laboratory's report, or from one device's documentation, and pasted onto another device because the analyte "is the same". A decision limit lifted from a guideline gets applied to a point-of-care result without anyone checking which method that limit was established on. The number looks authoritative. The line looks official. But a range derived on one method and applied to another can be misleading, flagging normal results as abnormal or, worse, reassuring on results that the correct range would have flagged. The interface reads clean. The clinical logic underneath it is subtly wrong.
And it compounds the moment results are trended. Trending assumes the numbers are commensurable, that a 9 last week and a 10 this week describe a real change in the patient. Trend a value across a ward device and then the lab, or across two different point-of-care platforms, as if the numbers were interchangeable, and you can manufacture a "change" that is nothing but a method difference. You can equally hide a real deterioration behind a method offset pointing the other way. The graph looks smooth and convincing. It is comparing apples with slightly different apples.
Whose job is it to fix this
It is fair to ask why the profession has not simply solved this. The honest answer is that it is being solved, slowly, and by more than one party. Harmonisation is a shared responsibility, and no single actor can deliver it alone.
Manufacturers carry the first duty: to tie calibration to recognised reference materials and methods wherever they exist, to be transparent about the method a device uses, and to state the reference intervals and limits their method actually supports rather than borrowing someone else's. External quality assessment schemes, run through bodies such as UK NEQAS and WEQAS, expose method-to-method differences by showing how the same material reads across the field, which is one of the few mechanisms that makes the problem visible at all. Standards bodies and professional colleges drive the reference-method and reference-material work that gives everyone a common anchor to aim at.
But the last mile belongs to the service that runs the devices. Standards are explicit about this. ISO 15189:2022 requires that where more than one instrument, method or location is used to examine the same measurand, results are demonstrably comparable across them. That is not a nice-to-have; it is a stated expectation of an accredited service. Comparability does not happen by assumption. It has to be checked, and the checking is work that only the service can do, because only the service knows which devices its patients are actually being measured on.
A reference range is not a property of the human body alone. It is derived on a particular method, and it only tells the truth when it is read against a result from that same method.
What this means for your service
None of this argues against point-of-care testing. Fast results in the room save lives and spare patients journeys, and a well-run POCT service is a genuine clinical asset. It argues for one specific discipline: treat the number as method-dependent, and be most careful exactly where it matters most, at the threshold. Here is what that looks like in practice.
- Know your method. For every analyte on every device, know the measurement method it uses, what its calibration is traceable to, and which reference range and decision limits are correct for that method. If you cannot answer this for a device, you do not yet know what its numbers mean.
- Use method-appropriate reference ranges, never borrowed ones. Do not copy the lab's range, or another device's range, onto a platform without confirming it applies. A range is part of the method, not a universal fact about the analyte.
- Do not mix devices in a trend without a comparison. Before you plot results from two platforms on one line, know how those platforms compare for that analyte. If you have not established comparability, annotate the change of device on the record and treat the step with suspicion.
- Run split-sample comparisons. Periodically measure the same sample on your point-of-care device and on the laboratory method, and look at the agreement, paying particular attention to values near clinical thresholds. This is the concrete way you meet the comparability expectation rather than assuming it.
- Escalate values near a threshold; do not trust a single device. When a result sits close to a decision limit, that is the moment to confirm rather than commit: repeat, cross-check on the laboratory method, or interpret alongside the clinical picture. A borderline number from one machine is the weakest evidence in the whole system, and it is exactly where a single device should not be the final word.
- Train the people reading the numbers, not just the people running the devices. The clinician acting on a borderline ward result needs to understand that the threshold and the method are linked. This is competency, and it belongs in your training and governance, not in folklore.
If you want help putting this on a formal footing, our consultancy designs method-comparison and split-sample programmes that satisfy the comparability requirement and stand up to accreditation. Our training, including the free POCT Fundamentals course, covers why method and reference range travel together and how to read a result with the threshold in mind. And our analyte guides set out, analyte by analyte, why method matters for interpretation, so the people using the numbers understand what the numbers actually are.
The number is not the patient
Two devices, one patient, two numbers, and a threshold drawn between them: that is the situation, and it is not going away until harmonisation is finished, which it is not. The safest clinicians and the best-run services already behave as if this were true. They know their method, they respect the threshold, and near the line they confirm rather than commit. Until the science closes the gap, that caution is the safety net. The number on the screen is a measurement of the patient. It is not the patient. Keep the two apart, especially at the edge of a decision, and the difference between two good devices stops being a hazard and becomes just what it is: the honest, unfinished state of the art.