Partners for good: how tech and the NHS work together
The COVID-19 pandemic revealed how the United Kingdom’s National Health Service (NHS), one of the largest public health systems globally, could benefit from the influence of an increasingly disruptive tech sector. For many, this has been a long time coming, as projects to bring technology into the NHS have been and gone over the last 20 years.
Joop Tanis is the Director of MedTech Consulting at Health Enterprise East (HEE), a company providing a bridge between the NHS and the industry. Tanis suggests the need for the NHS to collaborate with the tech sector has always existed. "A lot of research happens within the NHS, but many technological solutions are developed by industry. Arguably, technology has always played a part in the development of healthcare. But in the past 20 or so years, there has been a greater recognition that this partnership is mutually beneficial."
Solutions developed during the pandemic made this increasingly apparent. "Some of the testing and vaccination technologies have required industry to step up and really work very fast to develop these new ideas", Tanis says. "But for me, it's almost more interesting how we, as a healthcare delivery system, recognised that you [could] actually make quite rapid changes if you need to, that dramatically change the way we deliver care."
HEE delivers programmes such as the Clinical Innovators Network and the MedTech NAVIGATOR Innovation Grants. These help to identify unmet clinical or organisational needs in healthcare and provide a platform for SMEs to respond to those needs by developing innovative new products and services. The Innovation Grants are designed to help SMEs gain access to medical or other necessary expertise during the product development phase.
"Having access to clinicians, researchers and other key stakeholders in the public sector is an essential part of successfully developing and commercialising new products and services for the health and care market", explains Dr Anne Blackwood, CEO of HEE.
Blackwood says that the NHS has a much more open relationship with technology providers now compared to when HEE was founded in 2004. "This has accelerated during the pandemic, where the NHS and industry came together to solve problems, whether it was accessing teleconference technology for remote consultations or responding to the shortage of ventilator technology. The rapid adaptability of the NHS to the coronavirus pandemic shows what can be achieved in a time of national crisis."
"The interesting challenge now, as we emerge from this latest wave, is how do we make the changes ‘stick’ in terms of adoption of new technologies once the crisis is over?" she adds.
The pandemic has created additional challenges that will likely endure once the worst of the crisis is over. There are currently an estimated 5 million patients waiting for treatment that has been delayed due to COVID-19. Is there an opportunity for the tech sector to tackle these issues? Joop thinks there is. And that a lot of these solutions are already available. "A lot of funding goes towards research and development of new ideas and products, but on the adoption side, we've not had that seen the same effort towards implementation."
"Innovations and technologies that can support greater efficiencies in care, reduce the backlog on waiting lists, continue to support and monitor patients at home, and free up clinical time are all needed, and industry can help the NHS recover here", Blackwood says.
Examples include the grant recently awarded to Tekihealth Solutions Ltd to help fund the development of a telemedicine device to combat the effects of COVID-19 among care home residents. A hand-held device, which connects to a lightweight wireless router, has been designed to help care home residents who may have been struggling to access their doctor’s appointments due to poor IT infrastructure. Another grant has been given to a rehabilitation device called SoftPower, aimed at the elderly and partially able individuals whose ability to exercise has been affected by the pandemic.
Start-ups such as these are "engines of creativity and innovation", Blackwood says. "Like much of the public sector, I think the NHS has previously overlooked start-ups. Concern over lack of evidence when it comes to new technologies, and lack of a track record of delivering created a more risk-averse culture."
"I am hopeful that procurement channels will remain open to start-ups after this phase and that hospital trusts will recognise that within the communities that they sit, there is a wealth of creativity and innovation that they can tap into right on their doorstep."
What makes a successful collaboration?
Tanis says that for the NHS and tech sector to work effectively together, they need to understand each other's worlds. "HEE works for that reason", he explains. "We offer insight into the other side's world. For the NHS, that means we understand intellectual property, how to identify whether something is unique, whether it's practical in terms of development and production, and whether it would be value for money. From a clinical innovators' point of view, we can shine a light on what commercialisation, product development, and a successful rollout would look like."
It's also important that creators understand that in isolation, their product has no impact. "When I talk to technology innovators, they are quite rightly very enthusiastic about what their product or new technology can do, that's what they're passionate about, and I completely understand that. But often, the person using the product is not that interested in the technology. All they're interested in is whether it helps them deliver better care, more efficiently."
"As well as genuinely solving a problem, [the] tech must be something that can be adopted into clinical practice", Tanis adds.
This is where health economics comes in, which has proven to be crucial to ensuring the maximum benefits are gained from a new product. "If you're developing a product that would make a real difference but is simply unaffordable, you need to adjust the specification or the production so that when you get to the point of sale, it will actually have commercial viability", Tanis explains.
Another key area is how the technology will be rolled out. "You need to understand the environment in which it needs to be adopted", Tanis says. "We do a number of product evaluations where we look at a product being adopted by an organisation and what the learning points are, how it changes clinical practice and also how it changes operational practice."
In 2019 the NHS Long Term Plan was published, setting out the priorities for the health services over the following ten years ─ a crucial part of which is the digitisation of services. "Supporting integration of health and care services is critical", Blackwood says. "We saw the benefits of trusts collaborating across larger geographies during COVID-19, but vertical integration is desperately needed too, and digital technologies can be the enabler here. Also, AI technologies to stratify and case find in the community will enable the prevention and early diagnosis of diseases, saving lives as well as NHS time and resources." Tanis says that there has never been a better time for healthcare innovation. "History tells us that we very quickly go back to our old ways, simply because the need to do something differently has gone away."
"We were definitely running out of runway to match what was expected of us to what we could afford and what we could deliver within the capacity of the current workforce and infrastructure. This last year has really focused us on that and moved us along in terms of the willingness to make changes. We should use the time we have right now to transform the way we deliver healthcare permanently."
Introducing ClosedLoop - healthcare’s data science platform
ClosedLoop is an Austin-based, AI-driven data science platform built for the healthcare sector. The startup recently beat competition from IBM, Accenture and Deloitte among others to win the Centers for Medicare & Medicaid Services (CMS) Artificial Intelligence (AI) Health Outcomes Challenge.
This $1.6 million award was given to the startup for its work in a national competition aimed at addressing the healthcare gap in the US. Carol McCall, Chief Analytics Officer, tells us what sets the company apart.
What does ClosedLoop offer?
ClosedLoop’s data science platform is purpose-built and dedicated to healthcare and life science organisations. The platform integrates four technology-enabled data science workflows: data onboarding and normalisation, automated feature engineering, model training and validation, and model deployment and MLOps. Each workflow embeds numerous capabilities and functions that facilitate exploration, collaboration, oversight, and management.
What makes ClosedLoop different to other AI platforms?
ClosedLoop.ai is healthcare’s data science platform. We make it easy for healthcare organisations (HCOs) to use AI to improve outcomes and reduce costs. Purpose-built and dedicated to healthcare, ClosedLoop combines an intuitive end-to-end machine learning platform with a comprehensive library of healthcare-specific features and model templates.
ClosedLoop’s customers use explainable AI to drive clinical excellence, operational efficiency, value-based contracts, and enhanced business results. What sets us apart is our commitment and ability to deliver unbiased accuracy and explainable AI solutions at-scale in healthcare.
How does ClosedLoop avoid bias?
The ClosedLoop platform has built-in capabilities for helping to address bias and fairness. This is crucial for AI systems, particularly when they are used to inform decisions about allocating limited resources. Models that systematically underpredict risk for a particular group can lead to that group being unfairly denied resources.
With respect to algorithmic bias, our AI platform systematically assesses for bias in model design, data, and sampling, and makes sure to use measures that are insensitive to differences in disease prevalence between groups.
To assess fairness, ClosedLoop developed a new metric called Group Benefit Equality (GBE). Standard fairness metrics are completely unsuited to healthcare situations - they ignore “false negative” errors, which can leave individuals who would benefit from an intervention unable to get it, or use arbitrary benchmark thresholds that fail to adjust for instances where the alarm rate for the reference group is too low.
The GBE metric addresses these shortcomings. GBE is also easily explained, has transparent procedures, and uses clearly defined thresholds to assess when models are biased.
What is explainable AI?
For ClosedLoop’s customers, Explainable AI is key because it completely reimagines the concept of patient risk profiling. It shifts away from legacy risk “scores” to a comprehensive, personalised forecast that can be delivered directly into a clinical workflow.
Each forecast - which might harness several years of patient-linkable data - surfaces key variables and explains precisely what risks a patient faces and why. Each forecast integrates relevant clinical information and can link to specific interventions that clinical teams use to prevent adverse events, improve outcomes, and reduce unnecessary costs.
How digitally mature does a healthcare provider need to be to use the platform?
The flexibility of ClosedLoop’s platform allows our customers to leverage AI-based solutions no matter where they are on the maturity curve of data science and machine learning. This is because our product has two main pillars of technology.
The first pillar is used by organisations with robust technical or data science teams, and includes a fully-featured automated data science and machine learning platform. The platform’s capabilities allows data science teams to move quickly, build bigger and better models, and operate at the top of their license.
It automates a lot of the mundane work and deep operational details so data science teams can focus on asking the right questions, engaging with clinicians and care management teams, and communicating results.
The second pillar is used by organisations without large (or perhaps any) data science or technical teams. They are able to quickly leverage the platform’s healthcare-specific comprehensive enterprise feature store and an extensive catalogue of off-the-shelf model templates for common healthcare use cases.
These allow customers to quickly train models with data from their specific population, fine tune them to fit the context in which they will be used, and deploy them rapidly into their operational workflows.
The company has won a number of awards, to what do you attribute its success?
One reason is because we have been bold. In the CMS Challenge, for example, we believed we had the right and the ability to win against some huge names. That becomes important when, along the way, advisors become sceptical and suggest we stay focused on the core business. Today, those same people have said, "Hey, this is big!" You have to make bets. You have to decide. While it’s true that the work was aligned with where we were going, the amount of effort it took was unreal. There's no other way to really take a shot than putting every hour of all your best people on it.
What goals do you have for the next few years?
ClosedLoop’s customers share a common goal of achieving the Triple Aim: improve patient outcomes, reduce unnecessary healthcare costs, and enhance the experience of care.
Achieving the Triple Aim has become a national imperative - nearly one-third of Medicare beneficiaries experience an unplanned hospital admission or other adverse event each year, and the US spends almost $1 trillion on healthcare annually that does nothing to improve health outcomes. ClosedLoop’s goal is to make it easy and affordable for every HCO in the country to become AI-enabled and to use our explainable AI solutions to achieve the Triple Aim.