As in many other high-volume industries, pathology instruments capable of consistently performing analyses at high speeds have been developed over time. Consolidating these instruments into complex automation systems has resulted in a step-change in productivity in chemistry and bacteriology laboratories. More recently, the innovation of digital vision systems that avoid the need for the physical transfer of microscope slides have shown potential to shorten workflows in cellular pathology laboratories significantly, reducing the lead-time to diagnosis of suspected cancers.

Nevertheless, the science behind pathology testing has remained broadly the same for decades. Testing for infections and viruses still predominately involves incubation of samples, and initial diagnosis of cancer often includes review of stained slides through a microscope. Further, long-established systems of training, validation and accreditation ensure processes and procedures are maintained. However, could all this be about to change?

Molecular techniques have the potential to revolutionise the practice of all the main branches of pathology. Genetic screening for familial inherited disease is but a small part. Rapid identification of the presence (or absence) of disease, and the understanding of how an individual’s genes can inform decisions about appropriate therapy are likely to have major positive effects on patient outcomes. Technological advances in, for example, whole genome sequencing, enable the rapid identification and analysis of bacteria and viruses.

But perhaps the most significant innovation visible on the near-term horizon is the application of machine learning to pathology. Using computers to decipher complex morphological features of tumours has the potential to materially affect cancer treatment and outcomes, and to reduce costs and patient harm by avoiding unnecessary additional testing and chemotherapy. The adoption of machine learning will also allow pathologists to redirect their time to more complex cases and result in improved prioritisation of clinically urgent samples.

The application of artificial intelligence techniques is not limited simply to cancer diagnosis and subsequent decisions about treatment. Possibilities for quality, speed and cost improvements exist in all pathology specialities. For example, introducing clinical decision support systems in chemistry departments will ensure clinical practice harmonisation and consistent management of complex disorders is achieved, resulting in improved patient outcomes and fewer complications associated with chronic diseases.

As the perfect storm fast approaches NHS pathology: with demand increasing, inflation rising rapidly, and shortages in the professional workforce; could the rapid adoption and diffusion of innovation provide part of the solution? We think it might.

We’ve spent the last fifteen years or so understanding the issues associated with providing high-quality sustainable pathology services. Please do get in touch for an initial discussion if you think we might be able to help with yours.