Clinicians and healthcare practitioners making a contribution every day to innovation in healthcare heavily influence our own work at Bitjam. With the NHS now in its 70th year, we’ve highlighted three key points that we believe will be the backbone for the future of the NHS, with particular focus on grass-roots innovation and discoveries that start with frontline staff.
Innovation is Imperative to Continuing NHS Success
Research shows that as a nation we’re getting healthier and living longer, but the demand for NHS services are increasing and becoming more complex. The Five Year Forward View has a focus on strengthening access to high quality GP services and primary care – the largest point of interaction that patients have with the NHS. Digital innovation is key to bridging the gap between demand and quality GP services.
A key point to make is that digital and technology transformation in the NHS has revolutionised patient interaction. Digital technologies have seen a shift in the way people are choosing to transact. Consumers are voting with their fingers rather than their feet, choosing to transact via mobile apps rather than retail parks and high streets. This also translates to healthcare – there is a demand for convenience such as online GP appointment booking. With a growing demand for appointments, and the technology available to provide such a facility, this is a clear example of the importance of digital innovation.
Frontline Healthcare Workers Need Support for Innovation
Digital innovation needs to be wrapped around the needs of patients. Truly innovative approaches to improving care and/or improving efficiency through technology often start on the frontline. Healthcare workers who take care of the general public and use the systems created to support them on a daily basis, making them the most qualified to make suggestions for innovation. They understand what people want, look at the evidence and get a strong business case. They are most willing to roll up their sleeves and actively make beneficial changes to systems and platforms to improve patient care.
We believe that a modern NHS means clinicians need to be more tech-savvy than ever before, to be able to understand how to respond to gaps in facilities that could be bridged with technology and digital innovation. With the support of the NHS Innovation Accelerator (NIA) – an NHS England Initiative delivered in partnership with Academic Health Science Networks (AHSN) – clinicians are gaining the confidence to recognise gaps and present innovations that bring greater patient benefit.
Technology for Improving Patient Care Pathways
There’s a lot of media coverage on data protection, but patients are happy to share their data when they know it’s for their own care. Focusing on building secure patient systems will increase their trust and confidence in how data is used in order to engage people in a way that resonates with them.
Our recent work with BeAble – a post-discharge system designed to improve patient-clinician relationships and the promotion of patient wellbeing – is an example of improving patient care pathways as it offers an effective solution to the problem of co-morbidity and poly-pharmacy. You can read the full case study here.
The Next 70 Years of the NHS
In the future we want to see more support for health professionals and not just through the entrepreneur leadership programme but also on the frontline, with basic digital awareness and training. Such training reduces the problems found in interoperability within healthcare as everybody receives greater levels of training.
At Bitjam we like to think that the future of digital and technology innovation in healthcare is a system built on recognition and response. We want our solutions to help everyone – patients and professionals alike – ensuring inclusivity, equality, diversity and control. We believe that the NHS at 70 shares these values and we’re looking forward to contributing the technology that supports all users of this unique healthcare system.
The final part of our three part AI Series will focus on the ways you can consider adopting AI for your own systems, how to approach decision-making and the ways in which Bitjam can assist you with the transition.
How Can You Introduce AI into Your Existing Technologies?
At Bitjam, we use the R&D cycle to understand the needs of your business, the technology you have available and the most effective way to introduce AI to your systems. The initial discovery phase helps us to understand your system and business objectives to provide an accurate and “right-first-time” solution.
Bitjam will then explore the possibilities of how machine learning can augment your existing technology, and push the boundaries to see if it can allow diversification of your business.
To prevent overwhelming both you as a business stakeholder and the actual technology available – man and machine, if you like – we always recommend starting with a smaller project within your existing technology-reliant infrastructure that could benefit from cognitive technology.
Using co-production techniques and agile methodology as a tool for collaboration, Bitjam and your business can then consider ways in which AI could be introduced to simply update your system rather than require a complete overhaul that might cause disruption to your workflow.
Bitjam are keen to introduce the idea of AI adaptation to move businesses – both locally and globally – forward in a positive way, and we understand that a huge undertaking both financially and in terms of re-organising massive infrastructure is not usually a viable option. We’re here to lend you our experiences and help you make small yet revolutionary changes to your business.
Call or email us for a chat! Maybe you’re not sure how AI will even fit in with your business, but with so many of our projects now demanding IoT solutions, we’re sure we can help you find the right solution to bring your services up to date.
Last year at Bitjam we launched a small in-house machine learning project to coincide with the ACAVA Studios: Spode Works open studios event. ANNA – a learning algorithm that analyses poetry and audibly delivers it in an old-fashioned regional Potteries dialect – was the result!
Once we had our idea we sat down and had a discussion about the possible complexity of the project. A fully fledged bespoke neural network is quite a lot of work so we decided to try to find some existing neural networks to base our work off of. We found a Recursive Neural Network (RNN) designed to take text input and after a large number of training cycles we then tried to get ANNA to output some meaningful ‘learned’ poetry.
ANNA was a python script based on a simple RNN. We fed in around 200 pages worth of potteries dialect poetry aiming to produce some sort of meaningful poetry. Anna ran through about 500 recursive cycles of the input text per “epoch” of learning for a total of around 30 epochs. An epoch is essentially a single full training cycle.
The main challenge we faced was a shortage of data to feed into the RNN. So the next step for us was to source plenty of potteries dialect based poetry from poets past and present. First we tried to source as much poetry as possible from an online source, the main works we used were by Arnold Bennett. We then tried sourcing further poetry from Wilfred Bloor‘s sons Roger & Ian. Wilfred Bloor wrote over 400 Jabez tales in Potteries dialect (the Jabez character is a countryman living in the shadows of industrial Potteries). Finally, enter, Alan Barrett. Stoke-born writer, storyteller, poet, and actor who helped us train ANNA on the quirks and the peculiarities of the Potteries dialect. Thanks to the kind contributions of these people we managed to collect plenty of poetry that has been fed into ANNA.
The project was a lot of fun – especially since Alan Barrett remained on hand to help us deliver and chat about ANNA on the day of the open event! Our objective was only to take a playful look at neural networks and how they might be trained to learn local dialect. We didn’t exactly expect to achieve it, rather we were curious to see what the results might be. We got ANNA to recreate snippets of prose and dialect, and at times she successfully pieced together and understood some of the dialect.
ANNA was an example of Internet of Things – the interconnectivity of physical devices such as smartphones, WiFi modems and software – to the internet. We’ve got more projects coming up working with sensor data, redesigning systems, working with data from different types of sensors to create an interactive product, and we’re able to use our experience and knowledge of neural networks to complement these projects.
You can read the original ANNA blog post and interview with Bitjam Senior Developer Liam Mountford, in more detail here.