Building solutions to difficult problems in life science and laboratory medicine.

At Optymum SS, our R&D work is focused on building practical and scalable solutions to some of the most difficult problems in life science and biomedicine.

One of our key areas of development is digital haematology. We are exploring new ways to strengthen blood cell morphology training, improve referral pathways, expand access to specialist expertise and support earlier prioritisation of abnormal blood films.

As part of this work, we are developing a next-generation digital haematology ecosystem designed to modernise and scale blood film diagnostics. The ecosystem brings together AI-powered cloud-based technology, telehaematology, and workflow innovation to address critical challenges faced by haematology laboratories and healthcare providers.

In a global healthcare market under pressure from workforce shortages and rising diagnostic demand, Optymum SS is building toward a more scalable, connected and accessible future for haematology practice.

The Problem

Haematological disorders, ranging from anaemia and leukaemia to countless morphological abnormalities, impact an estimated 2.5 billion people globally. Full Blood Count (FBC) is the most ordered lab test in the world, with 10+ million performed daily. Of these, up to 2 million per day require follow-up blood film analysis; a labour-intensive process heavily dependent on a dwindling pool of expert haematologists and morphologists.

This is not just a problem in low-resource settings. Even advanced healthcare systems face rising demand, outdated workflows, and critical workforce shortages, resulting in delays, diagnostic errors, and costly inefficiencies. Manual, paper-based referrals (often involving handwritten forms, phone calls, and unstructured emails) create significant bottlenecks, poor traceability, and inconsistent turnaround times.

These systemic inefficiencies present a massive, largely untapped opportunity to modernise an essential diagnostic workflow.

Our Solution

Our answer brings together four tightly linked layers, using a roadmap that has been updated to better align with user feedback and current system needs.

.Layer 1: HemoEdge.ai – Strengthening training and competency development

We start with HemoEdge.ai, an AI-enabled, blood cell morphology training and competency development platform designed for biomedical scientists, haematology trainees, universities and healthcare providers. This layer addresses one of the root causes of the global morphology challenge: the limited availability of structured, accessible and high-quality morphology training. HemoEdge.ai will support progressive learning, case-based practice, expert explanations and competency-focused development, helping to build confidence, improve recognition skills and expand the future workforce of skilled morphologists.

Layer 2: HemoRefer – Modernising the referral workflow

Our second strand focuses on modernising the workflow in laboratories of high-income countries by replacing outdated, manual referral processes with HemoRefer, a secure, digital, internal workflow referral and collaboration platform, purpose-built for haematology labs. By integrating with third-party whole-slide scanners, HemoRefer allows blood films to be digitised, annotated, and seamlessly referred, even across institutions. This results in faster decisions, fewer errors, and an auditable, scalable system fit for the demands of modern healthcare. 

.Layer 3: HemoExchange – Building a global telehaematology marketplace

The third layer introduces HemoExchange, a telehaematology-enabled expert access platform designed to connect healthcare providers with external morphology expertise for second opinion support, case review, remote consultation and professional collaboration. HemoExchange creates a pathway for laboratories and healthcare providers to access specialist knowledge beyond their local workforce. This has particular value for underserved settings, complex cases, out-of-hours support and healthcare systems affected by shortages of haematologists and experienced morphologists.

Layer 4: HemoVisionAI – Prioritising cases via artificial intelligence-powered triage

The final layer is HemoVisionAI, an AI-supported triage and prioritisation system designed to support HemoRefer and HemoExchange. Rather than attempting full diagnostic automation at the outset, HemoVisionAI will focus on intelligent case prioritisation. Using computer vision and AI-supported analysis, the system aims to help identify urgent or abnormal blood films, flag potential morphological anomalies and route cases toward the most appropriate expert review pathway while preserving professional oversight.

In the long-term goal, all four layers will be connected in an engine that ensures that every blood film is reviewed, escalated and supported through the right pathway, at the right time, by the right level of expertise.