The changing diagnostic testing paradigm

Tim Simpson explores the changing diagnostic testing paradigm

Molecular diagnostics is not an emerging technology, however, the Covid-19 pandemic has seen an accelerated rate of adoption, With this increased profile, it is now time to capitalise on the investments made in the past few years and review traditional testing methods. 

As the pandemic recedes, molecular testing is at a crossroads. Laboratories have invested in high throughput technology that can manage large testing volumes and they are now considering ways that the excess capacity can be utilised, which prompts a reassessment of traditional testing methods.

While we cannot ignore the possibility that wide-scale population based infectious disease detection will be required in the future, molecular testing is also crucial to diagnosis, prognosis and monitoring treatment progression across many different fields.

Given the increased sensitivity of molecular testing versus viral cultures and its quicker turnaround time, it has huge potential to be applied to other fields where speed is crucial.[1] Here are some clinical testing areas where we are starting to see molecular testing change the paradigm. Bacterial vaginosis (BV): where more traditional methods are manual, time consuming and subjective.

Historically, the gold standard conventional method of testing BV, one of the most common vaginal conditions, caused by a dysbiosis in the natural flora[2] , is the Amsel criteria or the Nugent scoring system for Gram-stained vaginal smears. However, this has limitations as it relies on the technician’s skill and experience in being able to accurately assess the diagnostic criteria[2] – making the process time consuming and subjective.

In recent years, there have been molecular tests on the market which have the advantage over microscopy-based tests as they are objective, able to detect fastidious bacteria and enable semi-quantitation.[3]

Sepsis: where speed of diagnosis and treatment is critical to patient outcomes

In the UK, there is at least 245,000 cases of sepsis each year, resulting up to 48,000 deaths – more than breast, bowel and prostate cancer combined.[4] Therefore, sepsis is one such field where blood culture analysis can be frustratingly slow, when rapid action is crucial to patient outcomes. A review of emerging molecular technologies for diagnosing sepsis stated that “the swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 hours. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilise small sample volumes and detect polymicrobial infections and contaminants.”[5]  Clearly, molecular diagnostics offers a pathway to improved triage and management of patients in this instance.

Personalised medicine to inform clinical decision making

Following the NHS Long Term Plan, a one-size-fits-all healthcare system cannot meet the increasing complexity of people and hospital demands and expectations. With increasing calls for disease to be treated on an individual basis, personalised medicine is a huge area of focus in healthcare, and expected to benefit up to 2.5 million people in the UK by next year.[6]

We believe molecular diagnostics is a vital tool in realising the vision of personalised care, given the importance of having validated biomarkers to inform clinical decision making. While molecular diagnostics is not a new technology, its application to personalised treatment is considered immature. The future will see molecular diagnostics informing R&D for cancer drug development and predicting disease progression from the point of diagnosis.

Using AI and machine learning in molecular diagnostics

As we look to synthesize huge amounts of data to inform clinical interventions and also predict disease outcomes, the use of artificial intelligence and machine learning in healthcare settings is becoming routine. In 2020, Accelerated Access Collaborative (AAC) announced a £140 million AI Health & Care Award to support the NHS Longer Term Plan, which when then encouraged the Department of Health and Social Care, in 2021, to add £36 million to boost pioneering AI technologies that revolutionise and accelerate diagnosis – including molecular diagnosis.

The role of molecular diagnosis is essential, as it will provide much of the quality data input needed to fuel AI-based algorithms. As molecular technology develops, it is likely that even more insight and clinically relevant information will be generated, so it is vital that human experts can be supported to interpret complex diagnostic data sets that would be unachievable within the acute timeframes required. 

Tim Simpson is general manager of Hologic UK and Ireland 


[1] Green D. Improving health care and laboratory medicine: the past, present, and future of molecular diagnostics. Proc (Bayl Univ Med Cent). 2005; 18(2): 125–129
[2] Redelinghuys MJ, Geldenhuys J, Jung H, et al. Bacterial Vaginosis: Current Diagnostic Avenues and Future Opportunities. Front. Cell. Infect. Microbiol. 2020; 10:354 
[3]Coleman JS, Gaydos CA. Molecular Diagnosis of Bacterial Vaginosis: an Update. Journal of Clinical Microbiology. 2018; 56 (9): e00342-18
[4] The UK Sepsis Trust. Sepsis Statistics (2023) Available at:,diagnostic%20codes%20A41.0%2C%20A41.5%2C%20A41.9%2C%20R65.2%2C%20P36.9%2C%20R65.2 [Accessed: May 5, 2023].
[5] Sinha M, Jupe J, Mack H, et al. Emerging Technologies for Molecular Diagnosis of Sepsis. Clin Microbiol Rev. 2018 Apr; 31(2): e00089-17
[6]  NHS Personalised Care. NHS. Available at: (Accessed: May 5, 2023).

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