For good reason, we Brits are proud of our National Health Service. It’s free at point of access and provides very good value. People on the whole like it. Yet these positive feelings can lead to a Not Invented Here syndrome, especially for employees who have a semi-religious attachment to NHS. The sentiment runs along the lines of, “Why do we need the private sector to help us?” I sometimes think that if the NHS had its way, they would set off to build their own iPhone. 


This power of self-belief is admirable on one front, especially when you consider that NHS employs 1.5 million people. But it also means that you could be looking at the world as an Us versus Them problem. This becomes important in areas such as data analytics and AI, because insular, hermetic ways of thinking lead to limited perspectives about the latest innovations in the world beyond. Worse, you can begin to look at the private sector as offering only vendor relationships, where all you need to do is say, “You will go and do these tasks,” and the vendor takes care of it for you. The trouble with this mentality is that the data analytics and AI fields are maturing so rapidly, neither the public nor the private sector has all the answers. In fact, the two need to co-exist and work together.


Getting from Them to Us

Here are three approaches to more collaborative ways of working with the other sector, whether that’s public or private for you.


  1. Move beyond transactional vendor behaviors and encourage more proofs of value and concept. This approach may not make private sector companies a lot of money initially, but it will allow public sector data scientists to become familiar with their technologies, and potentially give vendors access to invaluable real-world datasets.
  2. Co-develop assets that mutually benefit both parties. Here NHS can offer some fantastic examples from the COVID pandemic. One happened when  Faculty, an experienced team of AI and machine learning specialists, worked with a local NHS team to develop the COVID-19 Early Warning System (EWS), which is one of the most sophisticated early warning system tools in the world. This tool helps us predict at a hospital level three weeks out what the admissions are going to look like and what the ventilation capacity is going to be. None of this would have been possible had the private sector been unwilling to give up its IP. In Faculty’s case, we developed a true working sense of an extended team.  


  1. Create easy mechanisms to unlock the value of sensitive data. This is a tall order for the public sector, because historically we’re not good at innovation or sharing our data. We can, though, create trusted analytical or research environments that can hold our patients’ data but also can allow private firms to come and unlock the value of that data with us. At NHS we don’t block access to data as long as it’s being used for the right purposes, and firms apply their algorithms in a safe and effective fashion. As part of this joint play, the private firms also commit to training our own public sector analysts in the process.

Working to each other’s mutual benefit is a theme that has become common among software ecosystem partners. It needs to become a more common reality among public sector entities as well. As data leaders, we all need to democratize the way we work with data and analytics. In the case of our latest public-private partnership, we wanted someone who would recognize that we were entering uncharted territory and that each would be likely to make some mistakes, but that we would also share a high tolerance of each other as we got through the process. 


The success of this endeavor makes me a believer that If we as a sector can work more collaboratively with private firms, we won’t lose out on innovation. AI in particular is a lot about startups who want to work in the public healthcare space. I think working together at the intersection of both is where truly significant results can come about.