Part 1: What Series A and PE Investment in Healthcare Innovation Programmes Reveal About Real-World Implementation

At Thomas Richardson Partners, we look at what happens when an organisation invests in healthcare innovation through the lens of adoption and scale. The starting point is usually reassuring: a strong strategy, world-class innovation, a great team, numbers that stack up, known competition, and compelling trial or pilot evidence. Funding pathways are established, target customers identified, and the benefits to patients are clear. From here, it can look like a simple matter of getting the innovation to as many patients as possible, as quickly as possible.

The common assumption is that approval, support, funding or procurement is the hardest part of implementation. Our experience across a broad range of healthcare innovation programmes tells a different story: the most significant challenges frequently emerge after approval, as organisations attempt to integrate innovation into the reality of operational delivery.

This is the first instalment in a four-part series drawing on insights from multiple implementation programmes spanning patient safety, digital health, diagnostics, medicines optimisation and service redesign. It combines a UK-focused review with our own real-world operational experience in the UK and the US, looking not at what each innovation did, but at where friction slowed adoption and scale — and where it accelerated.

This first part looks at the friction points for adoption, highlighting counterintuitive observations that show how experience-based review complements rich data analysis. These observations offer a practical checkpoint for any planned or existing investment — useful both pre-letter of intent and around the 100-day post-signature mark, as an experience-led check rather than a data-led one. 

Part 1: Operational Friction During Adoption in Healthcare

Adoption Friction Point 1: Approval Created Activity, Not Adoption

Several programmes secured support, funding or formal regulatory endorsement, yet still struggled to achieve routine operational use. Activity often increased immediately after approval — meetings scheduled, plans produced, implementation work under way — but operational adoption proceeded far more slowly than anticipated.

The challenge emerged when organisations moved from intention to execution. Delivery teams faced competing priorities, constrained resources, established working practices and local operational realities that had been largely absent from the approval process. Agreement on the innovation’s value rarely translated smoothly into embedding it in routine care.

Counterintuitive observation: regulatory and health system approval did not mean clinical use.

Adoption Friction Point 2: Existing Operating Models Absorbed the Innovation

Many innovations entered environments with established workflows, governance arrangements and behavioural norms built up over years. They were designed to improve services, yet the existing operating environment often shaped outcomes more than expected.

In practice, innovations adapted to existing systems more often than they changed them. Teams worked around existing pathways, governance requirements and operational pressures to implement new capabilities — so the resulting service ended up closer to the original operating model than anyone had anticipated.

Counterintuitive observation: new solutions were more likely to conform to existing systems than to change them.

Adoption Friction Point 3: Behavioural Inertia Outpaced Technical Deployment

Across multiple programmes, technical deployment moved quickly: systems were installed, access granted, milestones met. But the shift from availability to consistent operational use was much slower, and in a number of clinical settings it stalled altogether.

Too often, adoption was treated as a programme of activity — a sales campaign, a marketing launch, the sharing of clinical evidence — when it is really a shift in mindset, behaviour and power. Board-level sales and utilisation figures did little to surface the conversion or switching indicators that adoption actually depends on.

The limiting factor was rarely technical capability; it was behavioural change. Clinicians, managers and operational teams had to alter established routines, decision-making processes and working practices, and this took far longer than deploying the technology itself. Uncoupling the “old” products, pathways and workforce can be a bigger undertaking for an organisation than adopting the new innovation.

Counterintuitive observation: deployment was often completed long before adoption began.

Adoption Friction Point 4: Scaling Increased the Burden of Proof

Many programmes demonstrated clear value early on, only to face increasing scrutiny as adoption expanded. Initial evidence built credibility, but wider deployment demanded ongoing proof that benefits held up across different settings and operating conditions.

Evidence generation rarely stopped after a successful pilot. Expectations grew alongside the programme, with decision-makers seeking further operational evidence, utilisation data and outcome measures before committing to wider rollout. This is especially tricky when the case for change rests solely on financial savings — in our operational commissioning experience, we have seen suppliers claim significant cost reductions, only for the claim to fall apart once it meant cutting doctor and nurse numbers.

Counterintuitive observation: the more successful the pilot, the harder the evidence requirement became.

Adoption Friction Point 5: Organisational Complexity Destroyed Decision Velocity

Healthcare delivery relies on numerous stakeholders operating across organisational, professional and managerial boundaries, so implementation often hinged on alignment across groups with different priorities, accountabilities and operational pressures.

Interestingly, delays rarely stemmed from stakeholders disagreeing about the innovation itself. More often, they arose because accountability, coordination and decision-making were spread across too many groups — broad support did not translate into momentum.

Counterintuitive observation: broad agreement frequently produced slower decisions.

Part 2 looks at the factors that accelerated adoption. If you would like to discuss anything in this article, please get in touch.