Diagnostic laboratory demand is often discussed as if every extra request represents avoidable cost. The reality is more complicated. Some tests are repeated unnecessarily, some are ordered before they can inform a decision and some are missing when they could change care. A useful demand-optimisation programme must tackle both inappropriate testing and underuse, with patient need at its centre.
Laboratories are well placed to help because they see patterns across departments, settings and time. Yet the laboratory alone cannot decide what clinical information a requesting clinician needs. The strongest programmes involve clinicians, laboratory specialists, informatics teams and service managers working from a shared understanding of diagnostic value.
At a glance: Diagnostic test demand optimisation aims to make laboratory requests more clinically appropriate, not simply reduce test numbers. It examines repeat intervals, pathways, requesting information and feedback to clinicians. Success is measured through clinical usefulness, patient safety, turnaround and resource use rather than volume reduction alone.
Demand is not the same as workload
A laboratory might process many samples, but each sample can generate several tests and additional manual work. Demand analysis therefore needs more detail than a single monthly request total. Test-level volumes, panels, repeat intervals, sample rejection, add-on requests and high-cost specialist investigations tell different stories.
Two departments may have different requesting rates for legitimate reasons, including patient mix, acuity and the tests available elsewhere. Comparing them without context risks labelling appropriate care as waste. Data should be stratified by clinical setting and interpreted with relevant teams.
A sensible starting point is a small group of tests with meaningful volume, cost, variation or clinical concern. The goal is to understand why they are being requested before proposing restrictions.
Look at repeat intervals and decision points
Some clinical investigations need frequent monitoring. Others are unlikely to change meaningfully within a short period, unless a patient's condition or treatment has altered. Evidence-informed repeat-testing guidance can help clinicians distinguish the two.
A request review might ask: What decision will this result inform? Is there a recent valid result? Has something clinically changed since the previous test? Is the investigation part of a recognised care pathway? Could an alternative test or sequence provide the same information more efficiently?
These questions should shape clinical decision support rather than become rigid barriers. A blanket minimum retesting interval might prevent a clinically necessary repeat in an unstable patient. Where software rules are used, appropriate exceptions, override reasons and escalation pathways are essential.
NHS England published guidance on optimising blood testing in primary and secondary care in September 2021. Its context included supply pressures, but it also identified principles that can inform routine practice. Locally approved guidance and current clinical standards should govern the specific rules adopted.
Improve the request form before changing the laboratory
The request interface can create demand problems. Confusing test names, duplicated panels, poorly described indications or default selections can encourage investigations that the clinician did not specifically intend to order.
A digital review should examine the catalogue as a user experiences it. Are similar tests clearly distinguished? Are clinical indications visible where they help? Can obsolete tests be removed or renamed safely? Are there useful reflex or reflective pathways that reduce unnecessary follow-up requesting while preserving professional oversight?
Any interface change should be evaluated for unintended consequences. A redesigned screen that reduces one test's volume may simply redirect demand to another assay or make the right test harder to find.
Treat education and feedback as part of the intervention
Software alone rarely changes an established requesting culture. Clinicians need to understand the clinical reasoning behind recommendations and have a route to challenge rules that do not fit real cases.
A practical programme might combine targeted educational sessions, concise test-use guidance, peer discussion and periodic feedback showing how requesting varies. A UK study published in Annals of Clinical Biochemistry examined education-based interventions to reduce unnecessary hospital laboratory tests, illustrating the role of clinician engagement alongside operational data.
Feedback should be constructive rather than punitive. The aim is not to rank individual clinicians by the fewest tests ordered. It is to make better use of diagnostic information and identify cases where pathways need improvement.
Measure value, not just volume reduction
Test counts are useful, but insufficient. Track whether the intervention changes repeat patterns, sample quality, turnaround time, downstream investigations and clinician experience. Where feasible, assess safety indicators and whether patients experience missed or delayed investigations.
Financial analysis must also distinguish reagent price from true service cost. A marginally cheaper test strategy may create extra administration, recollection or delay elsewhere. Equally, fewer tests may release analytical capacity without immediately reducing fixed laboratory costs.
Governance should establish an agreed baseline, define objectives and review outcomes after implementation. If a rule creates delays or fails to achieve its intended effect, teams must be willing to revise it.
Demand optimisation is a joint clinical and laboratory responsibility
The most useful outcome is neither maximum testing nor minimum testing. It is a service that provides timely, clinically appropriate results without unnecessary repetition or avoidable harm. Achieving that requires careful analysis of requesting behaviour, transparent clinical rules and a willingness to adapt systems around how decisions are actually made.
For diagnostic laboratories, demand optimisation can be a valuable part of broader service redesign. Done well, it connects scientific expertise with informatics, quality improvement and responsible use of resources.
Where Optymum SS fits: Optymum SS can support evidence reviews, laboratory-demand analysis planning, workflow mapping and implementation documentation. Decisions about clinical requesting criteria remain with the responsible healthcare and laboratory teams.
Related on this site: Smart Lab & Digital Solutions, Research, Development & Scientific Advisory.
Key takeaways
- Optimise clinical value rather than target volume alone.
- Agree rules and exceptions with clinical users.
- Monitor unintended effects and revisit interventions.
Frequently asked questions
What is diagnostic test demand optimisation?
It is a clinically governed approach to improving what tests are requested, when they are repeated and how results support decisions, while avoiding unnecessary or poorly timed requests.
Does demand optimisation mean rationing tests?
No. A sound programme protects appropriate testing and uses evidence, clinical agreement and monitoring to avoid harmful under-testing.
How can demand optimisation be measured?
Track appropriateness measures, repeat-test intervals, exceptions, clinical feedback, patient-safety indicators and resource use rather than counting avoided requests alone.
