By Dr Kun Tian, Senior Lecturer in Marketing and Analytics, Kent Business School k.tian-424@kent.ac.uk
In the fast-moving world of diagnostics, scientific excellence alone does not guarantee success. For a diagnostic innovation to create real-world impact, it must be effectively adopted by the people and systems it is intended to serve. The pathway from laboratory validation to everyday use in clinical and community settings is complex, and this is where marketing analytics and market adoption modelling play a vital role.
Diagnostics do not exist in a vacuum. They are embedded within health systems, guided by institutional protocols, budgetary constraints, user perceptions, and often, competing priorities. Even a highly accurate and portable diagnostic tool may face resistance if it disrupts clinical workflows, lacks a clear reimbursement pathway, or fails to communicate value to key stakeholders. Early consideration of these issues is essential for achieving successful uptake and long-term integration.
My research focuses on how predictive analytics, behavioural insights, and market modelling can help developers and health system stakeholders better anticipate adoption dynamics. By analysing decision patterns and simulating different adoption scenarios, we can gain a deeper understanding of how pricing, usability, perceived risk, and external incentives influence the behaviours of clinicians, patients, and procurement bodies.
This type of insight is particularly valuable in the early stages of diagnostic development, when there is still flexibility to adapt product features, positioning strategies, and launch plans. For example, market modelling can help identify which types of healthcare providers are most likely to adopt a new diagnostic first, what kinds of support they would need to do so effectively, and how changes in regulation or reimbursement might affect the adoption curve.
For small and medium-sized enterprises working in diagnostics, these insights can inform more targeted and efficient go-to-market strategies. Rather than assuming that clinical efficacy alone will drive uptake, organisations can make data-informed decisions that align innovation with real-world needs and constraints. In a resource-constrained and highly competitive market, this can be a key factor in determining commercial viability and public health impact.
As part of the CADDA Community of Practice, I am currently exploring collaborative opportunities that apply these methods to support the adoption and deployment of diagnostics in the UK health system and beyond. I believe the intersection between data science, behavioural research, and diagnostic innovation holds great promise for improving how we understand and enable the use of new technologies in practice.
CADDA provides a unique platform for interdisciplinary collaboration. It brings together experts from life sciences, clinical practice, analytics, and industry to tackle real-world challenges in diagnostics development and application. I look forward to continuing to engage with colleagues across the community to explore how market adoption modelling can support smarter, more sustainable innovation.
If you are developing a diagnostic or working with a company facing adoption or scale-up challenges, I would be very happy to connect and discuss how marketing analytics might support your goals.