How to close the access gap in Asia-Pacific laboratories

Are we missing the opportunity to help Asia-Pacific laboratories to digitise — and improve access and outcomes for the patients who need them most?

Labs tend to lag behind other healthcare institutions in adopting new technologies. Many are still in the early stages of transition; they’re focusing only on specific elements of routine workflows or subspecialties¹.

It’s a pattern reflected across much of the world. A survey of 127 anatomical pathology labs across Asia and Europe found laboratory digitisation can be broken into three main stages (with no significant difference between the two continents)²:

1. Stage 1: Digital case management, such as barcoding slides rather than labelling by hand and writing reports digitally rather than on paper.
2. Stage 2: Digital imaging, using scanners and software rather than analogue microscopes. Most labs are at this stage; 76% have at least one digital slide scanner.
3. Stage 3: Digital diagnosis, using advanced AI tools and algorithms to read slides for quantification, detect hotspots/regions of interest and classify tissue. Just 23% of labs use digital tools for diagnostic purposes, primarily university hospitals and cancer centres³.

Digital diagnostic tools can significantly reduce the reading time per case, reduce the need for second opinions, and support clinicians in their decision-making by comparing large datasets, often even across geographies and different cohorts³. They can also prioritise and assign cases more effectively, sending those which are likely to be complex directly to a specialist⁴.

This translates directly into improved patient outcomes. A study examining AI-assisted scoring of HER2, a protein which helps control how breast cells grow and divide, used slides from 120 breast cancer patients across four pathology labs.

The algorithm “first detects the tissue fragments, then identifies invasive tumour regions. Within those regions, another AI model is used to detect and classify the individual tumour cells. Finally, it calculates the slide-level HER2 score”⁵.

The results speak for themselves: the AI-facilitated diagnosis improved accuracy from 69.7% to 77.2⁵.

We’re making progress — but there’s still an unmet need for deeper integration of digital tools.

The hub-and-spoke model: where emerging markets are pulling ahead

In regions where access to specialists presents a challenge, healthcare leaders are combining digital diagnostic tools with a ‘hub and spoke model’ for testing. A central ‘hub’ laboratory provides mostly non-urgent and specialist testing for smaller regional labs (the ‘spokes’), which focus on turning around urgent tests on-site as quickly as possible.

This lays the foundation for full digitisation: an Open Environment for digital pathology, integrating multiple innovations in a cutting-edge digital health ecosystem.

One such example from Singapore allows AI pathology innovators spanning multiple specialisms and locations worldwide to collaborate as one. By analysing whole-slide images to identify and grade tumours, the technology reduces misdiagnosis risk and improves reporting time by up to 10×6. Cloud-ready and enabled by remote access, the integrated innovations include algorithms for prostate cancer detection, grading and tumour quantification, breast cancer biomarker quantification, Tumour proportion score (TPS) analysis for non-small cell lung cancer, among many other integrations⁶.

Hub-and-spoke models like this are a natural fit for service delivery in resource-limited clinical settings⁷. Emerging markets — where demand for pathology specialists outstrips supply — are now using hub-and-spoke models to leapfrog more developed markets in adoption of digital imaging, allowing greater access to specialist expertise and large clinical datasets⁸.

The benefits for patients are equally compelling. AI-enabled diagnostic ecosystems in a hub-and-spoke delivery model mean less time waiting in distressing uncertainty to find out whether they’re seriously ill: faster turnarounds, with high diagnostic accuracy⁹. It’s a more effective, patient-centric approach.

A holistic approach is how we improve access and outcomes in diagnostic care

Combining resources from across the healthcare ecosystem and using emerging technologies like digital diagnostics, we can unify different types of data from labs, clinics and medical tests (like test results and administrative data) through lab or hospital information systems (LIS/HIS).

Paired with a hub-and-spoke pathology model, that means we can improve access to diagnostics, clinicians can make more informed assessments, and patients can begin treatment sooner — all in the markets with the starkest public health challenges.

But this isn’t a one-off solution. It isn’t like buying software.

We call on policymakers and healthcare leaders to work together to:

• Establish a clear, harmonised, and secure legal framework for health data use, distinguishing it from other consumer data and promoting investment in robust security infrastructure.
• Create a seamless, interconnected ecosystem through interoperable EHRs and common standards, ensuring patient privacy and empowering broader healthcare reform initiatives.
• Implement strong international governance for cross-border health data exchanges, advocating for responsible data flow over restrictive localisation and harmonising privacy and security standards.

Only then can we create a truly integrated digital health system in which pathology teams can make a bigger impact. It moves us from an illness paradigm to a wellness paradigm, prioritising prevention over cure, and helping people better manage diseases they’re already living with. There’s no time to spare.

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