PRE-ANALYTICS
Baseline Estimation of GP U&E sample ages
0-2 2-4 4-6 6-8 8+
0 100
Median TAT venepuncture to collect (hours)
2.92 200 300 3.58
Optimised vs Baseline media sample ages at laboratory delivery for GP practices
7 6 5 4 3 2
Baseline Optimised
The data shown in Figure 2 highlight the impact of the optimisation process detailed in Figure 1. Further intelligence on the impact of targeted recommendations for improvements is demonstrated. Using the age of specimens at reporting (time from collection to result) we demonstrate evidence of the quality improvements that optimised transport can have on the analysis and resulting of these primary care specimens. Downstream benefits include improved analytical processes and reduced risk of unnecessary
400
Median TAT collect to deliver (hours)
500 600 5.30
5% 20%
0-2 39% 2-4
8% 29%
Median TAT venepuncture to deliver (hours)
4-6 20% 0 1.47 200
Median TAT venepuncture to collect (hours)
400 600
Median TAT collect to deliver (hours)
1.55 800 1,000
Median TAT venepuncture to deliver (hours)
3.27 Fig 2. Median turnaround time dropped from 2.92 hrs to 1.47 hrs following optimisation.
clinical escalation, such as emergency department admissions triggered by pseudo-hyperkalaemia.
Driving ‘right first time’ diagnostics Pre-analytical optimisation is also about ensuring the right tests are done, the first time. We use benchmarking and demographic data to identify variation in test ordering. For example, why are some practices ordering vitamin D tests at ten times the network average? Why are ESR and CRP often co-requested, despite guidance suggesting this is inappropriate?
By visualising this variation through
dashboards, we empower laboratory managers and clinical leads to challenge inappropriate demand and promote evidence-based practice. This DataViz shown in Figure 3 demonstrates how benchmarking Vitamin D activity over time can help identify potential over- or under-requesting between Trusts. It allows local teams to investigate whether high demand reflects
Count of H: ESR against count of B: CRP per GP 2K Trust
genuine need, or if adjustments in clinical practice are warranted. Figure 4 shows data which we
provided a partner pathology network showing evidence of differing ESR and CRP requesting patterns. This highlights potential inequalities in outcomes and access and provides evidence to inform change that enhances productivity and value for money for the NHS and its patients. (Note: Fig 4 DataViz excludes outliers.)
Aligning testing with population needs
Population health equity is also at the heart of our approach. We overlay test ordering data with demographic indicators, including deprivation, ethnicity, and age, to identify where care may be falling short. In one dashboard, we revealed gaps in PSA testing for men over 50 in practices serving high-risk populations. Elsewhere, we identified over-testing that was straining diagnostic pathways and delaying access for patients at higher risk.
70%
Optimised Estimation of GP U&E sample ages
11%
1.5K 1K 0.5K 0 0 0.5K 1K 1.5K 2K Test count (B: CRP) Fig 4. Comparing requesting rates provides insights of potential unwarranted variation.
WWW.PATHOLOGYINPRACTICE.COM SEPTEMBER 2025 45 2.5K 3K 3.5K
In one case, optimising collection and delivery schedules reduced the median time from venipuncture to laboratory receipt by over 25%
Test count (H: ESR)
Median sample ages (Hours)
Hours
Hours
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