Over the last couple of days I have been speaking to clinicians who have ideas for tech. They want to see clinicians lead tech innovation and they want to see more of it. Sound exciting right? However, there seems to be a naive way of looking at tech. There are clinicians out there who want to lead tech but don’t want to learn how to code. To me I am mildly shocked at this. I think it’s either because they don’t understand the complexities that are involved in code, software and hardware or that they want to justify to themselves that they don’t have to learn these things in order to get a slice of the tech pie. Because of this I feel the need to write this in a medical context in order to show them how this goes against everything they practice and teach in medicine, and how it’s clearly a really inefficient way of going about clinical tech innovation.

Let’s just set the scene. A patient comes into A and E with crushing chest pain. ECG shows ST-elevation and the POC trop is through the roof. The emergency medicine doctor checks the referral pathway and concludes that the patient needs PCI. He refers to the cardiologists who accept and perform a PCI on the patient. Now to the emergency medicine doctor the role and desire of PCI is fairly straightforward, it’s to relieve the acute myocardial ischemia. The cardiologists perform this function in order to achieve this simple desire. However, there are loads of nuances that the cardiologists take into account when doing PCI. Now lets say that the emergency medicine doctor want to lead the innovation on this. He knows why and when people need PCI. However, I think all clinicians who are reading this will understand why it’s not effective for the emergency medicine doctor to try and lead innovation in PCI. Although the cardiologists are performing a particular function they are constantly refining it. They are tuning the parameters of their department. They take part in research and evaluate the latest evidence to see if they can incorporate it into their practice.

However, when it comes to tech their appreciation for nuance and complexity of the subject goes out the window. Tech is like a speciality. Every new piece of hardware or software aims to perform a function and solve a particular problem. The complexity behind that is a world in itself. What language are you using? What framework are you using? How did you code the data models? What security measures have you coded? How is the architecture of your code? How did you code your sorting algorithms? How did you code any other algorithms for that matter? How are you collecting user data? How are analysing the data? The list goes on and on. Knowing what context the software is used and knowing why we need it does not mean you are qualified to lead innovation. It’s as efficient as the emergency medicine doctor claiming that he is going to lead the innovation in PCI because he knows what context it’s used and when it’s used.

Now we’ve set the scene lets look at what this means. Have you ever wondered why the app store is littered with NHS funded apps that barely get used whilst companies like Facebook and Google are changing the world? It’s because Google and Facebook are constantly refining their systems. They are adding features, testing them, altering them and testing them again. This testing will even go down to the shade of blue that they are using. They are using advanced data analytics to measure the interactions of the users. It’s a full time job. Not surprisingly if you’re in the tech industry the main focus of your industry is tech. Now we could just say let’s soup but the IT departments. Aside from the fact that there’s a difference between IT and coding there is an advantage of understanding clinical context. Like when the cardiologist understands some emergency medicine context, understanding clinical context will enhance the coding process. Coding is about breaking down the steps of the problem and converting it into executable lines of code for the computer to process. It’s the same with mathematical modelling. If you understand advanced math and you understand the subject material of what you’re trying to model you get an edge. Your refinement process speeds up. This is why in the big data industry a unicorn is someone who can code, knows advanced math and stats and has a deep understanding of the subject their analysing.

There you have it. Not surprisingly, if you want to have a meaningful impact on a cutting edge field that could change the world you will have to put a lot of work in. You will have to learn some new skills that are not conventional. Seems like common sense but for some reason I have lost count of the amount of clinicians who simply ignore this truth because it’s hard to swallow. If you could get by on your standard traditional set of skills without any coding skills everyone would be doing it. This may seem like I am looking out at clinical tech innovation with anger or frustration but this simply isn’t true. Because the majority of clinicians don’t really know what they’re talking about when it comes to tech and hold little to no tech skills this field is a new playground to explore. I’ve started a coding for medics meetup group in order to teach them the basics of coding. Within two weeks I had over 50 doctors sign up. When these medics get into full swing the clinicians who blow a lot of hot air about tech innovation will see what clinicians are truly capable of when it comes to tech innovation.