Technology and its Non-Adoption

This month I’ve turned my attention to the issue of Technology and its Non-Adoption. We’re often hearing about how great a Tech innovation was in a nearby patch or other but more frequently we’re probably hearing (or not hearing!) about how it’s failed to take off whilst it quietly slides off-stage and we get ready to hear about the ‘Next Big Thing.’

Fortunately for me, a few months ago Prof Trisha Greenhalgh published a systematic review looking at just this subject. In it she describes the paucity of evidence into why an innovation isn’t adopted and gets abandoned. We see this in other walks of academic life too- no one likes a negative trial (least of all ‘Big Pharma’), so they don’t get published and when they do it’s usually relegated to a low-impact journal with little readership. So, what of technology? Well as opposed to negative Pharma trials where you at least know the result is negative during the course of the trial and the difficulties in publication lie after the fact. With technology ‘Non-adoption’ and 'Abandonment', you’re attempting to study a 'non-event' or a 'possible-event' sometime in the future - trial design is much tougher! And you still have to go through the same rigmaroles of publishing said negative results.

Nonetheless the last few years have seen a growing body of work to challenge this and the team were able to develop a framework for studying the following five things:

Non-Adoption of technologies

Abandonment of technologies

Problems with Scale up

Problems with Spread

Problems with Sustainability

It’s been snappily named the NASSS framework.

Greenhalgh et al 2017

It’s a rather complex piece of work and captures a number of domains. Now I looked at this and at first glance thought, I've seen all this before! It was fairly all encompassing (and not in a good way); of course if you call out every single component of a multi-layered system you’re going to capture everything but how does that help? It initially bore a resemblance to other frameworks I’d seen, not necessarily related to technology, but general change and innovation itself within complex systems. An obvious example was the Institute for healthcare improvements (IHI) model for spread.

IHI Model for Spread

As with most attempts to explain complex phenomena in simple terms, the devil is in the detail and it is here that I think this framework proves its worth. For each of the specific domains it divides the challenge into simple, complicated and complex (see below).

Greenhalgh et al 2017

An example, as cited by Prof Greenhalgh:

“Take domain 1 (the illness), for example. A broken ankle is “simple” – but so is a heart attack (in that it is relatively straightforward to diagnose and has a clear treatment pathway). An example of a complicated illness is cancer, because it requires coordination of chemotherapy, surgery and radiotherapy (along with management of multiple and potentially serious side effects) – all dictated by an evidence-based care pathway. Now take a complex case – say an IV drug user who is also an alcoholic with psychosis and hepatitis C. And let’s say the person is also from an immigrant group, has uncertain citizenship status and speaks limited English. How much of this person’s trajectory can you predict with confidence? Is there a ‘pathway’ at all?”

I prefer to use the word Wicked, in place of Complex and now I think we’re speaking the same language. When you apply this to all the domains it soon becomes apparent that any technology that is battling against a Wicked challenge in any of the domains is almost certainly doomed to failure if additional strategies aren’t implemented at the same time to work around these Wicked issues. The technology cannot stand on its own and nor can small scale pilots predict success if the wider conditions for success haven’t been met.

It'd be flippant to suggest a Living Lab would be able to overcome all of these obstacles but I think it gives technologies a good chance to anticipate and prepare as best they can for them. The real power of a Living Lab comes with genuine stakeholder participation, as we see by the NASSS framework; though it should certainly include service users and front-line staff it must also lead right to the very top of the organisation, at the very least indirectly. Only in this way, can the wider system be optimised by those senior decision makers for the successful deployment of technology, its spread and scale-up.