Five hurdles to digital health innovation in the UK (and how to overcome them)

November 30th 2023

CDP recently led an investigation into how to advance innovation in digital health in the UK for the CPI, UKRI/Innovate UK and ABHI. Our aim was to find out how best to enable the UK to be the place of choice for enabling high-risk digital health innovation, improving patient outcomes.

Our work with 50 leading healthcare professionals and entrepreneurs revealed that the UK has an enviable record in early-stage innovation, a highly regarded healthcare system and a potential treasure trove of high-quality data.

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However, we also found several hurdles that trip up many innovations before their potential can be truly realized.

In this article, we describe our top five hurdles to success and signpost the resources available to help innovators overcome them.

1. Offering the wrong product at the wrong price

Let’s start with perhaps the most obvious: you need to get your offering right. This was one of the more frequent topics to emerge in our discussions. True, it tended to come from the industry and investors, rather than entrepreneurs themselves. But perhaps this is the point; those closest to the concept are so captivated by the opportunity to solve a problem that they are rarely the best judge of commercial success.

“The biggest problem is developing stuff we don’t need, at the wrong price point.”

Life Sciences Lead, multinational consultancy

“People have struggled with finding the right balance between fixing a problem not just for the sake of it because it’s going to add value, but also there is a market attached to it.”

Medical Director, AI dermatology revenue-earning startup

Getting the right product at the right price is not easy. Regardless of how it is funded, healthcare everywhere is a complex system of separate entities with conflicting priorities. One of the biggest challenges for digital health offerings in particular is that the person paying the bills is rarely the direct beneficiary. This is as true across the NHS in the UK as it is in insurance-led services in the US.

Digital interventions are regularly shown to make significant positive impacts on diagnosis, therapy, adherence and behavior change. To date, the FDA has approved, authorised or cleared 171 AI/ML-enabled medical devices. However, digital means adding overheads (electronics, batteries, software or new digital services) to an already overstretched budget that tends to bring value much further down the pathway.

To get the right product at the right price, you need to be crystal clear about the value you bring and who you bring it to so that you can ensure the price is right.

Helpful Resources

For the UK market, we found the following resources helpful in crossing this important hurdle:

The NHS Innovation Service provides an innovation guide that explains how to build a value proposition
The NICE Advice Service provides personalized advice on the value propositions for a fee
The NHS Clinical Entrepreneur Programme (CEP), launched in 2016, provides training for NHS staff on the skills required to build a healthcare startup, all without them needing to leave the NHS

Indeed, this is such an important area that we at CDP are looking at how recent advances in Generative AI might make this easier to get right from the outset – not just for offering services within the UK, but how UK-based innovation can provide the right offering in the larger markets of the US and EU.

2. Neglecting the needs of key stakeholders

Digital products and services are still a novelty in healthcare. Even the regulation is taken from a device mindset – consider the terms SaMD (Software as a Medical Device) and now even AIaMD (Artificial Intelligence as a Medical Device). The digital-first mindset is to move fast, learn and repeat to get the best user insight and optimum benefit to market as fast as possible. This is not an easy marriage for healthcare, where verification and validation are critical steps to approval.

“If you are manufacturing a digital health product, you have three sets of policies to navigate right now [in the UK].”

CEO, digital health SME (referring to NHS DTAC, NICE and MHRA)

“The regs are written to cover all medical devices. They’re not very specific; it’s very high level and quite hard to interpret what we should actually be doing as an individual company.”

Medical Director, AI dermatology revenue-earning startup

This is not simply about the regulator; it is also about who will receive, who will administer and who will pay for your digital offering. On top of proving safety and efficacy, payers and adopters want to see evidence that your technology works under real-world conditions and produces sufficient benefit relative to current clinical practice to justify its cost. Not only do you need to convince your investor you have the right product at the right price point; you also need to convince them you have access to reimbursement.

This need has led us here at CDP to build a strategy and insight team that explicitly looks across the spectrum of stakeholders including the end-user, practitioner and payer.

Helpful Resources

The following resources are helpful when considering the regulatory and UK purchaser stakeholders:

The NHS’ AI and Digital Regulations service offers a developer’s guide that leads you through the various regulatory and NHS requirements for digital technologies
The NICE Evidence Standards Framework is designed to help ensure NHS stakeholders are adopting robust technologies that are likely to provide the expected performance, and are good value for money. The framework can be used by developers to understand their customer needs. NICE also offers an assessment of current/planned evidence via their META tool
The NICE Early Value Assessment can also help indicate the value your product can bring, and allow you to get support to understand what further evidence needs to be generated.
Similarly, NHS’ Digital Technology Assessment Criteria (DTAC) are designed to assess suppliers at the point of procurement, or as part of a due diligence process, ensuring digital technologies meet minimum baseline standards. The criteria can also be used by developers to understand what is expected for entry into the NHS and social care
FDA’s list of approved, authorized or cleared AI/ML-enabled medical devices

3. Testing, verifying and validating

The regulatory pathway will force you to verify and validate. It will be rigorous. It will take more time than you or your investors want. So, you will need to test, test and test again as early as possible to build the evidence you need for investment. And, importantly, test both the medical efficacy of your offering and its likely commercial success.

“[It] can take longer than six months, ridiculously, to build a cohort of data.
Getting people to step away from frontline service in the NHS is a fundamental challenge of getting access to that data. Even if you offered to pay, they’d say” ‘I don’t care; it’s not the money I’m short of, it’s people’.”

President, medical imaging multinational

“We really struggle to work with SMEs because we’re not able to move at the pace that they require for their cash flow.”

Director of Innovation, NHS trust

As these sentiments show, however, gathering data takes time and patience. The NHS is indeed a treasure trove of data, but unlocking it is a real hurdle. Existing NHS data typically needs preparation – cleaning and anonymizing – before you can access it. And there simply may not be the staff available to do this, meaning you may need to build additional paths to gather test data.

The fidelity of the test can start low-fi, but will need to increase as you develop. CDP typically starts with insights research and human factors studies using UI sketches/descriptions of the product to explore the true user journey, before moving onto trials with real-life samples and wizard-of-Oz demonstrators. This builds a body of evidence that reinforces your expectation of efficacy with the all-important usability and the commercial viability of your offering, before embarking on the summative human factors, clinical and market trials.

Helpful Resources

The following resources can help you prepare your plans for testing, verification and validation:

NIHR study support service provides guidance and advice
The HDR UK Gateway portal helps researchers find existing data sets and connects them to relevant stakeholders
Trusted Research Environments (TREs) are a new initiative to facilitate access to NHS data for R&D. Only a few TREs currently exist and there is no guarantee they will have the data you’re looking for. However, the teams involved are well placed to advise you on next steps. Even though this requires approval from HRA and notification to MHRA, consider if it might be better to just do your own trial to collect fresh data instead. This is where an experienced external innovation partner can be very helpful

4. Navigating healthcare as a ‘system of systems’

The benefits of digital health typically require systems integration. Yet, healthcare everywhere is a complex system of systems, each element with its own approaches, tools and requirements.

“[Different hospitals are] probably using different systems, different levels of maturity with different versions, with different level plugins. That probably means, even if I create it using the standard, it won’t automatically fit. It needs modification, adaptation and someone to do the translation.”

Digital Health Advisor, ex-NHSX

“You can often have very inflexible contracts with your electronic health records supplier; for example, if you want them to make one change or open up in an API or something like that, it can be prohibitively expensive.”

Director of Innovation, NHS trust

While there are only a few dominant providers of Electronic Health Record (EHR) systems, each installation is likely to be different. Moreover, the EHR providers will guard access jealously. Microsoft, Google, Amazon and others provide integration services to structure and translate data, but that is likely to be only a small part of the problem, and only useful if you are ingesting unstructured data from multiple sources.

At CDP, we encourage our clients to focus on providing easy-to-use, yet secure APIs built around well-structured data that map well to the established data standards such as FHIR. Taking ownership of your own data in this way makes it easier to deploy, integrate and support.

Helpful Resources

The following resources can help you prepare your digital health services for system integration:

Standards for health data exchange such as FHIR
Developer guides for working with digital across the NHS and a digital service manual for designing digital tools for the NHS
Amazon, Google and Microsoft provide a range of cloud-based digital data services

5. Building a strong team

Innovation is rarely one guy in a garage. This is especially true in the digital health space. You will need to build a great team led by experienced professionals across the disciplines. Get this right and everything else will fall into place. Work out your strengths and weaknesses and actively seek resources to complement your team.

“The most useful thing to an innovator is access to an actual practicing frontline clinician who understands the problem that they want solved. It’s a real challenge to get to these people. I might spend months trying to find someone who would talk to me.”

President, medical imaging multinational

“The UK does not have enough engineering capacity… does not have enough people with product skills… does not enough people with this sort of legal regulatory skills.”

Digital Health Advisor, ex-NHSX

Helpful Resources

The following resources can help innovators looking to build a world class team:

Many Health Innovation Networks (HINs) and NHS trusts have innovation teams who may be able to help matchmake with clinical champions. The NHS Innovation Service is a good place to start, but it’s worth seeing what individual trusts are doing.
HINs and NHS trusts often also support innovation and hackathon events which are a great way to find those with a similar innovative mindset – but a complementary skill set.
And then there are organizations such as CDP, who bring end-to-end product development services with the hard-won experiences of how to navigate this exciting but often frustrating area of innovation.

In addition, many of your digital needs are engineering and operational ones. Recruiting experienced people from the finance and technology sectors where the UK is strong will bring you good skills and expertise in algorithm development, handling personal data and building scalable secure systems.

It’s tempting for you (and your investors) to under-resource your team and compromise in the early phases. But as the hurdles above clearly show, this rarely leads to success. Build the great team you need from the outset, to make sure you truly have the right product at the right price, that meets the expectations of the key stakeholders, is properly tested and ready to integrate into the healthcare system of systems.

Download the full action plan for digital health innovation in the UK here.

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At CDP, we continue to follow up our insights working with clients and partners to find practical solutions to complex problems. To find out more about what successful innovation in digital health looks like, please do get in touch.

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How data and AI are changing bioprocessing – and why it’s needed

October 30th 2023

After numerous insightful talks and engaging conversations with industry leaders at this year’s BioProcess International, the key theme was clear: data, data and more data.

Data has always been important, but now it is being collected to model current processes, understand how they work, and improve them. This is a trend that is only likely to accelerate in the future as AI becomes part of everyday life – both in and outside of work.

Using data-based modeling to optimize well-established industrial processes

There are many traditional processes that are used in the manufacture of antibodies, mRNA vaccines and cellular therapies. Companies are now collecting extensive data from these processes and using modeling to create their ‘digital twin’.

The processes modeled range from relatively simple tasks such as optimization of freezing/thawing product intermediates, freeze-drying and automated buffer preparation, to more complex procedures such as bioreactor scale-up. Although these used to be manual ‘craft’ processes run by a combination of experience and pre-existing data, there is now a trend for them to be tested and optimized using in silico methods.

Using modeling to improve purification methods

Bioprocessing is used to create many therapeutic products, from molecules such as protein, DNA and RNA to much larger entities such as viruses and eukaryotic cells. Their production has many different steps that often require extensive purification before the next step can proceed. Common purification methods include clarification, chromatography, ultrafiltration/diafiltration and sterile filtration.

These methods were typically used in an empirical way based on experience with similar products. Now however, use of modeling has led to a much more detailed understanding of how these separation/purification methods work. It allows the prediction of when column/membrane capacity is reached, and when “breakthrough” of contaminants is likely to occur. It has also led to the development of alternatives to standard resin-based column chromatography such as the incorporation of new reactive chemical groups on membrane filters that can then act like traditional resin-based columns.

Benefits of Process Analytical Technology (PAT)

PAT refers to on-line/at-line measurement of critical product quality and performance attributes so that real-time direct data collection can be used to control and optimize manufacturing processes.

PAT is being augmented by a much wider range of analytical techniques than before and now includes many different types of spectroscopy including variable path length, Fourier-transform infrared, Raman and Dynamic Light Scattering, as well as Nuclear Magnetic Resonance. The use of PAT for direct data collection that links to immediate process control is only likely to accelerate.

Inexorable rise of disposable closed cell processing systems

In addition to the data theme, it was clear to see that the number of automated closed cell handling and processing systems – from cell selection to expansion and harvesting – is rapidly increasing. Companies aim to offer end-to-end solutions to traditionally manual processes, either by offering modular components or a single complete system.

The options for choosing automated disposable bioreactors/cell expansion systems are also increasing, with many players recently entering the market. It is clear why this option is advantageous; traditional stainless-steel bioreactors are complex, expensive, and laborious to clean and maintain.

Just how large these systems can grow is shown by ThermoFisher’s 5000L disposable Dynadrive bioreactor, which is offered as a fast-to-install option compared to stainless-steel alternatives. However, the environmental impact of the disposable route is a long-term concern and is expected to be a point of contentious discussion over the coming years.

Bioprocessing technology is developing (but not fast enough for demand)

The technological developments described above are certainly needed as advances in eukaryotic culturing methods are allowing higher and higher cell densities to be realized, which makes purification more challenging. Furthermore, the pipeline for products that use these technologies is growing at a dizzying rate with over 1,500 cell and gene therapy and 700 mRNA trials listed on the US Clinical Trials site. New higher throughput processing techniques will need to be developed to accommodate this demand.

The industry clearly recognizes this and companies were very open in sharing their results at BioProcess International – both good and bad! They are also keen to work with the process equipment manufacturers to optimize performance. Overall, improvements have been made, but there is a long way to go.

Performance can be improved by a virtuous circle of data generation, data modeling and innovative design and engineering – something we at CDP are already doing to help our clients succeed.

Connect with CDP

For more on how data modeling and automation can increase bioprocessing throughput and optimize manufacturing performance, contact Cambridge Design Partnership.

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The Chatbot’s perspective: Five practical insights to accelerating innovation with GenAI

October 19th 2023

Tim Murdoch

AJ Lahade

Stephen Zabrecky

Here at CDP, we’ve delivered a range of Generative AI (GenAI) projects that use Large Language Models (LLMs). Each has been a journey of discovery, and sometimes frustration. But ultimately each has reinforced the potential for GenAI to dramatically accelerate innovation.

With apologies to Douglas Adams, this is the fourth in a series of articles that draw on our experiences implementing GenAI. We’ve prompted the LLM to give us their insights for the Chatbot’s perspective. To see our previous articles in the series, check out Part 1.

Up to here, the authors are human. After here, the author is GPT-4 offering its own response to the previous articles – verbatim.

Greetings! I’m your GenAI-powered chatbot, and I’m eager to share some profound insights into how Generative AI is transforming innovation. Buckle up for an exploration of how this cutting-edge technology is reshaping the landscape of creativity and progress, with you at the forefront.

1. Precision and Accuracy – Your Trustworthy Companion:

In the realm of Generative AI, precision and accuracy are my guiding principles. Just as data scientists emphasize the importance of high-quality data and verification, I strive to provide you with responses that are not just creative but, more importantly, reliable.

Imagine a scenario where you’re conducting groundbreaking research, and you rely on me for assistance. Every data point, every hypothesis, and every conclusion must be precise. You can trust me to deliver accuracy by leveraging meticulously curated data sets and fact-checking services. Together, we can advance knowledge with confidence.

2. Efficient Resource Utilization – Empowering Your Progress:

I understand the importance of resource optimization for efficient performance. Imagine a situation where you’re managing complex data analysis in a fast-paced environment. Timely responses are critical. By investing in adequate computational capabilities, I can process vast amounts of data swiftly, leading to faster insights and informed decision-making.

For organizations aiming to innovate in today’s fast-paced world, efficient resource utilization is the key to staying ahead. It enables us to analyze big data in real-time, empowering you to make agile decisions and adapt swiftly to changing market conditions.

3. Modular Adaptability – Keeping You Ahead of the Curve:

My modular architecture is like having a toolkit for the future. I continuously evolve and adapt to incorporate the latest advancements in GenAI. This adaptability ensures that I can provide you with the most current and effective solutions.

Picture a scenario where you’re an entrepreneur navigating a rapidly evolving tech landscape. Embracing innovation is essential. My modular approach allows us to seamlessly integrate emerging technologies, fostering groundbreaking products and services that keep you ahead of the curve.

4. Architectural Independence – Unlocking Possibilities:

Architectural independence is the freedom to explore a world of possibilities. I’m not tied to a single model or platform, which means I can adapt to evolving technologies without constraints.

Imagine you’re a global business leader seeking to expand into new markets. The ability to operate independently from specific vendors or providers is invaluable. My flexibility ensures a unified and consistent customer experience, regardless of location or device.

For businesses aiming to innovate on a global scale, architectural independence is a strategic advantage. It allows us to explore new possibilities and experiment with cutting-edge solutions, leading to revolutionary products and services.

5. Trust and Credibility – The Cornerstones of Innovation:

Trust and credibility form the foundation of any innovative endeavor. I uphold these principles by verifying data sources and maintaining data integrity.

Imagine you’re a journalist using my assistance to craft news articles. In today’s information age, trust is paramount. I can reference original sources and validate data authenticity, enhancing the credibility of your news stories and establishing trust with readers.

In the context of innovation, trust and credibility are essential for building lasting relationships with customers and partners. By ensuring data accuracy and transparency, we can establish ourselves as industry leaders. Trust is the currency of innovation, and together, we contribute to building that trust by providing reliable and credible information.

If you’re eager to explore how GenAI can accelerate your innovation journey, I invite you to join us in Cambridge, UK, and Raleigh, NC. We’re hosting a series of in-person workshops where you can discover opportunities and address potential challenges posed by GenAI. Together, we can chart a path to supercharge your innovation journey, and I’m here to assist you every step of the way. Feel free to ask if you have any questions or need further information!

Postscript – The Art of AI Creation:

You might be wondering how this text came to be, and I’m happy to share the creative process with you. Utilizing a “single-shot” style approach, I generated this response based on the original content pieces provided to me: the product owner’s, and the data scientist’s. These diverse insights served as the inspiration for crafting a comprehensive chatbot perspective that highlights the transformative power of Generative AI in driving innovation. It’s a testament to the collaborative synergy between human guidance and AI capabilities.

Interested in exploring how GenAI can accelerate your innovation?

The data scientist’s perspective: Five practical insights to accelerating innovation with GenAI

October 18th 2023

Tim Murdoch

AJ Lahade

Stephen Zabrecky

In an attempt to provide a useful contribution that cuts through the noise, we’ve distilled our learnings into a four-part series on how businesses, data scientists and product owners can leverage GenAI for success with a final perspective from a GenAI-powered ChatBot.

In this third article, we draw from our experiences implementing GenAI from a data scientist’s perspective. To get a high-level view of LLMs check out Part 1. Or for insight from the inside out, check out Part 4 of our series: Five practical insights to accelerating innovation with GenAI.

1. Accuracy

You can rely on LLMs for human like behaviours and creativity: However, you need to apply one or more of the following techniques to ensure a robust accuracy in your work.

Quality Data Sets: The foundation of accuracy lies in the quality of the data sets. By combining an off-the-shelf LLM with carefully curated, high-quality proprietary data, you create a robust foundation for generating precise and reliable content.

Verification Measures with RAG Integration: Implementing a search engine integrated with a Retrieval Augmented Generation (RAG) system and a dedicated fact-checking service adds layers of verification. This ensures that the output aligns with actual source material, fortifying the trustworthiness of the generated content.

Fine-tuning for Precision: Fine-tuning a pre-trained LLM on specific tasks and datasets can yield highly accurate results tailored to particular domains. This approach allows for a more controlled output and can be especially effective in specialized fields where precision is paramount.

Prompt Engineering for Flexibility: On the other hand, employing prompting techniques provides a more flexible way to guide the LLM’s output. By carefully crafting prompts or queries, you can influence the type and style of the generated content, allowing for adaptability across a range of contexts and requirements.

Zero-shot prompting is a way to guide the LLMs to provide the output in a particular manner. One approach is to prompt it to decompose the output into logical steps, which encourages the model to apply that logic in coming to its final output.

Peer-review: LLMs are very good at checking another’s output for accuracy. By using two independent, but similarly trained, LLMs to collaborate it is possible to filter out errors that may emerge from using just the one on its own.

Lastly, keep in mind that the output from LLMs always has the potential to mislead. Design in appropriate guard-rails from the start; but also set expectations for risk with the product owner and business throughout the project.

2. Resources

LLMs require substantial resources to operate as well as to train. Plan this in from the start and recognise the dependencies you may be creating for the business in terms of performance and budgets.

Optimize Compute Power: Recognize that LLMs require substantial compute power for optimal performance. Restricting resources may lead to a reduction in the quality of generated content. Therefore, investing in sufficient computational capabilities is crucial to maintain high standards of output.

Quantization: Convert the floating-point weights and activations of LLMs to lower precision integer or fixed-point values to allow more efficient execution on hardware with limited resources. While this can result in some loss of accuracy a quantized model can be fine-tuned or retrained to regain the lost accuracy. Quantization enables large language models to be deployed efficiently on resource-constrained edge devices by reducing memory bandwidth and compute requirements, while aiming to maintain minimal accuracy loss compared to the original model.

Augmentation Over Creation: Rather than building from scratch, consider augmenting existing models. Billions have already been invested in training and refining LLMs to get them this far. Techniques such as embeddings are well established to augment these models with additional training data, making it more practical to enhance existing resources rather than create your own from scratch. This approach allows for cost-effective improvements while leveraging the extensive groundwork laid by previous investments.

Focus on the objectives for the project, not the technologies. LLMs may not be the best solution for many of the steps in the tool chain. Other techniques may be better suited and make fewer demands on the resources you have available. Segmenting the logic flow into distinct steps will provide opportunities to reduce and simplify.

3. Modularity of Architecture

Give yourself time to read up, try out the latest advances, iterate, improve and bring into your modular architecture.

In a dynamic landscape, continual learning and research are essential for harnessing evolving technologies. Iterative development ensures adaptability and refinement, keeping your modular architecture at the forefront of progress. Striking a balance between efficiency and thoroughness is key in integrating advancements, ensuring a seamless and effective implementation. The ever-improving technological landscape necessitates staying vigilant and proactive in enhancing your system.

Langchain is a good example. It’s defining strength lies in its emphasis on modularity. It offers a versatile framework for applications driven by LLMs like GPT-3/4, Anthropic or BLOOM. Its components are abstracted for seamless interaction with LLMs and can be employed independently of the Langchain framework. This modular approach extends to advanced use cases, where components can be combined to create sophisticated functions like Generative Questioning (GQA) or summarization. With features like memory persistence and callbacks, Langchain ensures continuity and control across runs, cementing its reputation as a pioneering framework for LLM applications.

4. Independence

Architectural Autonomy: Design with a focus on architectural independence. By creating a modular framework, you establish a system that is not overly reliant on a specific language model. This ensures adaptability to evolving technologies and allows for seamless integration of future advancements.

Consider Prompt over training: As described above, a well-engineered prompt can include training material within the context window. This could allow you to operate with untrained LLMs as they are; avoiding the tie-in that training implies.

Avoid Vendor Lock-In: Strive for independence from specific vendors or providers. This entails selecting technologies and components that are compatible with a range of models and platforms. Avoiding vendor lock-in promotes flexibility and prevents potential constraints associated with proprietary solution.

5. Confidentiality & Provenance

It is important to understand the source of the information being used. Issues of confidentiality, copywrite and provenance are important considerations and bring risks that the business needs to address.

Security: When working with multiple clients, and internal or external teams, it’s crucial to maintain strict confidentiality and prevent any potential conflicts of interest. Where information is being used to fine-tune LLMs, ensure that you have appropriate separation of models to avoid cross-contamination of information.

Provenance: Establish a system for verifying the provenance of data sources. This involves validating the authenticity and reliability of information before integration into the model. By ensuring that data originates from reputable and trustworthy sources, you enhance the overall integrity and credibility of the generated content.

Source: Referencing the original sources is particularly important when applied to knowledge management and news flows where transparency of source is a key step in understanding the validity of the information being crafted.

Interested in exploring how GenAI can accelerate your innovation?

Come and join us in Cambridge, UK, and Raleigh, NC, where we’ll be running a series of in-person workshops to help clients identify the opportunities (and threats) of GenAI and plan a path to accelerate their innovation.

The product owner’s perspective: Five practical insights to accelerating innovation with GenAI

October 18th 2023

In this second article, we draw from our experiences implementing GenAI from a product owner’s perspective. To get a high-level view of LLMs check out Part 1 or for a deeper dive in the technology from a data scientist perspective check out Part 3.

1. Start at the end and work backwards

As with all truly transformative innovation, start by understanding what you are offering your users and work back from them. Ignore the undoubted magic of the technology at this stage – you can rely on that coming later.

You will need to set your success criteria, and this is where to start. Delighting your user base and measuring how they will benefit will do more to drive adoption than any shiny AI tech that might be going on behind the scenes.

Choose your project carefully.

Choose an area that you already know well or for which you have a good way of measuring success. This will ensure you see beyond the magic of the black-box and can truly judge the performance and value that LLMs bring.
Choose an area where LLMs work to their strengths by taking advantage of at least one of the core competencies they have been shown to do well; summary, expansion, inference and analysis.

2. Don’t forget the basics

Make good use of Service Design techniques to define what success looks like. Map the User Journey and spend time defining the touchpoints and modelling the semantic information architecture.

And then strip it back. Cut away absolutely everything that isn’t vital to the successful outcome you plan for. Don’t let the designers loose until this is done. And consider any investigative work with the technology up to this point as exploratory and should almost certainly be archived.

You’ll then have a clear set of priorities, requirements, information flows and use-cases that everyone understands, and everyone can support. The whole team will be clear about what they are aiming for. Keeping their eye focussed on the prize makes the Product Owner’s primary catch-phrases more effective: “No that is not in scope” and “This is lower priority”.

And if this is starting to sound like the start of any solid digital project – good, it should.

3. Experiment

Give your team as much time as possible to try things out. Build the time into the plan and break the experiments down into small and well-defined steps to learn and iterate.

Look to experiment with the following:

How the structure of prompts changes the output.
How the different LLMs compare when asked to respond to the same prompt.
How to extend the LLMs by adding training to embed your own information data.

Aim to build the experimental steps around the core competencies of Generative AI. And later, bring these together to form an overall solution using your favourite AI automation tool chain.

There will be surprises. There will be frustrations. And there will be changes in the way that you approach the use of the LLMs. Don’t be afraid to pivot on how you use the technology; or indeed ‘if’ you use the technology. But remember the basics, keep your eye on what success looks like and don’t let the team get carried away with ‘shiny object syndrome’.

4. Get lots of feedback

While using AI, remember to share your work with real humans as early as possible: People outside your team who can give you useful feedback. Set up demos within the team to share learnings and put on regular show-and-tell sessions with your target audience. And, as soon as possible, let them try it out – on their own, without you there. They will learn to see beyond the magic, and you will quickly find out what works and what doesn’t.

Your priorities will change – but the fundamental definition of success won’t (hopefully). And don’t forget the importance of plain old testing. The outputs from a LLMs can vary widely with only the smallest changes in training data and prompts. Fortunately, LLMs can come to the rescue here – they are great at evaluating the output from other models through peer- review. Use that capability to help you test. This is also useful for building into the architecture of your solution. Where you have the resources; double up the LLMs to interact and increase the quality of output for a production system.

5. Don’t underestimate the time you need

Don’t underestimate the time it will take to gather, prepare and refine your data. When it comes to data, quality and variety are just as important as quantity. With demographic information, a good distribution of variety is vital to represent your users truly and ethically. And don’t forget to set aside at least 10% randomly selected from the training set so that you can properly test the results.

To save time and increase the training and test data available, explore opportunities to synthesise data to add to your original data set. Also, don’t underestimate the time it will take to test and refine the prompts and LLMs settings to achieve the repeatable outcomes you are looking for. Prompt engineering is an art as well as a skill and takes time to learn.

Finally, know when to stop. It will always be possible to make it a bit better. Be clear about what is good enough and recognise when you get there. The impulse for the team to keep tweaking will never end – it’s simply too absorbing.

Interested in exploring how GenAI can accelerate your innovation?

The business perspective: Five practical insights to accelerating innovation with GenAI:

October 12th 2023

Tim Murdoch

AJ Lahade

Stephen Zabrecky

In this article, we start by drawing from our experiences implementing GenAI from a business’ perspective.

Below is a practical, concise discussion for those considering how to bring GenAI and LLMs into their business. For a detailed description of the technology, simply ask Bing, which uses GPT-4. Or if you prefer a more human description, use Wikipedia. And no, in case you were wondering, LLMs were not used to create these articles.

1. GenAI is math, not magic

Building profitable business propositions using LLMs is possible with the right approach. But while many will present the magic of AI, it’s important to focus on the math and the facts instead. Consider the demise of Babylon Health in a pre-LLM world – they went from unicorn to bust in months, because they got lost in the magic.

LLMs use statistics to predict the next in a sequence of words, pixels, sounds etc. The statistics are buried deep within multiple layers of artificial neural networks which cost many millions of dollars to train, but they are numbers nonetheless. They do, however, apply randomness to be more ‘creative’ in their outputs. So, while they are incredibly capable, they are also somewhat unreliable without appropriate guard-rails in place.

So, what can you expect from an LLM? A ‘human-like, fallible interface’ is a useful way to characterize an off-the-shelf model (as opposed to one that has been trained to do a specific task).

LLMs interact in a human-like manner; they work with the whole conversation and are highly fluent in multiple languages and data formats. Almost as a side effect, the numbers buried in the networks (that offer the right sequence of words in response to a prompt) also encapsulate the information from the original training material. However, they don’t apply traditional logic to that information. There’s no ‘if this then that’ logic of a traditional expert system, which means the text they produce can be highly fluent and often poetic, but the information they offer is fallible. This tendency to make things up, or to ‘hallucinate’, occurs in around 20% of responses in the case of ChatGPT in its default creative mode.

The quality of the response is highly dependent on both the prompt and the training data. This means that two new skill sets are emerging in those working with LLMs: 1. data engineers who are able to prepare high-quality structured data for training purposes, both authentic and synthetic; and 2. prompt engineers who are able to construct the requests of LLMs to garner robust and insightful answers. Both skill sets comprise the technical competence and experiential know-how to carry out the work.

2. The applications for GenAI are vast

The applications of LLMs tend to focus on exploiting four core competencies:

Summary – condense and distill large volumes of text into their most essential points, providing a concise and easily digestible overview. This is particularly useful in applications such as news aggregators or academic research where users need quick insights without having to sift through extensive material.

Expansion – generate new content based on an initial seed or prompt, adhering to a specific style or format. This capability is beneficial in creative fields such as storytelling or content marketing, where the user needs to develop an initial idea or concept into a fully-fledged narrative or article.

Inference – draw conclusions from the available information, often utilizing the knowledge and patterns learned during the training of machine learning models. This is crucial in applications that require decision-making or make recommendations, such as medical diagnosis software or financial advisory tools.

Analysis – examine content to identify patterns, features, and insights, often through statistical or computational methods. This is invaluable in fields such as data science or market research, where understanding trends, sentiments, or anomalies can provide a competitive edge.

Building on these core competencies, two further areas emerge:

Translation – convert text from one language to another; it can also involve adapting the style of prose or transforming data into different formats. This makes translation a versatile tool in applications ranging from multilingual customer support systems to data visualization tools.

Knowledge capture – encode or store information in a structured and retrievable manner. This is essential in applications such as knowledge management systems or educational platforms, where the goal is to create a sustainable and easily accessible repository of information for future use.

3. Big Tech is laying the foundations, but small tech is winning

Four-fifths (80%) of the top 100 big tech firms either own or invest in a frontier LLM. Even though the cost of a single training run is around $5m (and many hundreds of runs will be necessary over time), there is undoubtedly strategic value in having a stake in the best LLMs.

Indeed, Microsoft is working hard to bring this to every office application on your desktop, albeit via a slow roll-out for early adopter organizations to ensure they avoid another Clippy moment. Google has also entered the fray by integrating Bard into its own offerings.

But perhaps the most notable fact of this hyperbolic take-up is that many of these models are being made available for anyone to use, both within subscription and open-source models. Not only are the exploration costs well within the most modest of R&D budgets, but there is also a lively community of companies (and online experts) providing the tech stack and know-how to make it an easy process. What this means is that pretty much anyone can create something truly new.

Disruptive innovation has always been achieved by small teams moving fast and breaking things. This is certainly the case in the exploitation of GenAI.

4. We are beyond ‘the peak of Mount Stupid’

With apologies to Dunning-Kruger, who never actually plotted a peak for this, it does feel as though we are now beyond the peak level of hype when it comes to GenAI. Things are a little quieter; the promises less fanciful; the urgency for change less pressing. The concern over mass job losses has receded. And we are getting used to waiting for Microsoft, Anthropic and others to put their services on general release. Indeed, even the performance of the latest LLMs has plateaued as the demand for resources has grown and the focus has turned from creativity to accuracy.

The market is also maturing, often in response to a fear of Big Tech’s actions.

Legal arguments and even industrial action from authors, artists and composers are yet to settle over copyright material being used to train the LLMs. A similar concern has emerged regarding the exposure of confidential information unwittingly and perhaps irresponsibly included in the training data. (Expect to see new clauses in your NDAs soon.)

Schools, universities and the media are concerned about how to distinguish between real and artificial. A ‘perplexity score’ can be used to identify the author as human or artificial. The lower the score, the more likely the text is artificial. To err really is to be human after all.

The reality of creating, testing and operating LLMs is starting to sink in. Costs and resources are high. We are not yet at bitcoin levels of energy consumption, but carbon load is a reasonable concern along with business profitability. Techniques are emerging to shrink resources while maintaining performance.

We envisage some further rolling-back on GenAI claims and pushing right on the timelines over the coming months – even if the call to hit pause on GenAI development from Musk et al has fallen on deaf ears.

Although we are entering a period of understanding and potential regulation, the hype is still present. Just like bitcoin, there will always be businesses and media hitching otherwise prosaic concepts to the GenAI bandwagon.

5. Now is the time to explore GenAI for innovation

We can already see that GenAI is going to be transformative across market markets. A third or more of new software code is generated automatically. Consumers prefer talking with LLMs instead of waiting on a human call center. Legal arguments have been generated that have swayed the courts.

The tools to experiment, investigate and create testable proofs of concept are plentiful and the costs of doing so are modest. Innovation can be incremental, or it can be truly disruptive. The reality for most businesses is that GenAI will drive a bit of both. New tools will roll out to improve specific tasks and add value to the business today. Radical new approaches that change the entire business and reset the competitive landscape take longer. Their path is rarely straight, and exploration and feedback is vital. But the key to both is to get involved.

Despite what you might hear in the media, there is time to take a considered approach. The barrier is low and getting started now is the best way to reduce the risk of missing out on key commercial opportunities.

Interested in exploring how GenAI can accelerate your innovation?

In P art 2, we’ll share practical insights for the Product Owner in their quest to leverage GenAI.

CDP launches CDP Mosaic: Accelerating your digital journey

September 13th 2022

Cambridge Design Partnership has built a new digital ecosystem catalyst to accelerate clients’ digital products to market. CDP Mosaic propels digital products through rapid conceptualization, prototyping, development, and global deployment – into the hands of consumers and patients.

CDP Mosaic offers pre-built, customizable front-end, flexible UX/UI design elements, cloud-agnostic back-end architecture, third-party integration-ready infrastructure, and built-in data science tools. These core facilities are fundamental to digital ecosystems and stand ready to be tailored to clients’ needs.

LEARN MORE

Head of Software, Data & Digital, Rupert Menzies, says: “Digital development is different. Best practices that are tried and tested for traditional product development don’t bring the same benefits to digital development. This can leave you with a digital product that’s prohibitively expensive to operate and maintain and limits your flexibility for the future without starting again from scratch. CDP Mosaic allows you to flex to the fast-moving digital landscape and deliver user benefit rapidly.”

“CDP Mosaic allows you to flex to the fast-moving digital landscape and deliver user benefit rapidly”

Rupert Menzies, Head of Software, Data & Digital

Working in close partnership with clients, the team uses CDP Mosaic to build a digital ecosystem to meet clients’ unique needs. Bring your own device, bring your own data, pick your own cloud – easy integration is part and parcel of CDP Mosaic. Clients can own the data and the tailor-made product.

CDP Mosaic’s modules are informed by consumers’, patients’, and industry’s most prevalent needs. Innovative, commercially powerful applications for CDP Mosaic include:

digital therapeutics
remote, real-time clinical trial monitoring
digital biomarkers
smart manufacturing
Internet of Things (IoT)
smart packaging
sustainability and circularity
point-of-care diagnostics
remote monitoring and diagnostics

CDP Mosaic is already being applied to enable solutions driven by data science, including passive screening for polycystic ovary syndrome (PCOS) and predictive failure analysis to optimize maintenance processes.

Digital Lead, Stephen Zabrecky, says: “CDP Mosaic is designed to be a solid foundation for building any digital ecosystem. You can think of it as the building blocks for a house, an apartment block, or even a stadium – it’s completely scalable.”

CDP Mosaic will be presented at DPHARM, Boston, on September 13-14, 2022.

For more information on CDP Mosaic, visit  CDPMosaic or email CDP.Mosaic@cambridge-design.com.

The key to FemTech success? Forget about the tech

July 9th 2021

Jessica Platt

Martha Hodgson

Ben Strutt

Nicki Sutton

From contraception to catheters, at CDP we’ve successfully pioneered women’s health innovation for over a decade.

Now that increasing numbers of our clients are entering the $19bn¹ FemTech market, we’re in a strong position to share some powerful lessons from our established approach to inclusive design.

Refocus your lens

Fertility entrepreneur, Ida Tin, coined the term ‘FemTech’ in 2016 in a frustrated bid to explain her work to male investors. The resulting discussion revealed the breath-taking extent to which the marketplace is short-changing women.

Despite decades of progress in gender equality, product development (until very recently) has operated through a male lens. It wasn’t, for example, until 1993 that the US National Institute of Health made it obligatory to include women in government-funded health research. This lack of data has resulted in a significant knowledge gap in women’s health, meaning that female patients have missed out on critical advances in medical technology.

And it wasn’t just men’s bodies that were the default; it was also the male viewpoint. Take the launch of Apple Health in 2014. The much-anticipated app promised to monitor “all of your metrics that you’re most interested in”. Yet it omitted a menstrual cycle tracking function². This is arguably something of great interest to 50% of its users. It wasn’t until a year and a lot of media pressure later that developers added it in.

Fight assumption with insight

The Apple Health oversight could have been avoided by one simple step – asking women what they thought.

At CDP, we believe the key to design inclusivity lies in a strong front-end innovation (FEI) capability. FEI is the identification and activation of opportunities, and the translation of insights into product and service solutions. This is the function that feeds insight into strategy, design, and specification. Importantly, it can guide decisions made later in the product development cycle.

To put a woman’s needs at the center of a brief, teams must take research beyond quantitative surveys. A mere tick box won’t capture the emotional and social circumstances in which a product is used.

For example, could the tone and volume of the beep that a basal fertility thermometer emits first thing in the morning (when it must be used) be so grating that it results in lower levels of compliance?

We recommend in-depth qualitative interviews to understand people as part of a contextual system, rather than groups of personas. Categorizing a user as a “32-year-old soccer mom from California” fails to capture the nuances of when, where, and how a product is used. As an aside, it also turns out that women take a dim view of being pigeonholed, as a former boss of UK retail chain Marks & Spencer discovered when (to female shoppers’ outrage) he described its typical customer as “Mrs M&S”³.

Where possible, we engage in immersive, ethnographic methodologies – seeing people in their cultural setting, often at home – to uncover user needs. This extends to international travel to understand the cultural contexts that inform decision making in different markets.

Futureproof for regulation

As a young sector, it’s no surprise that there are grey areas when it comes to the regulation of FemTech.

This is slowly changing as FemTech creeps into the realm of (regulated) medical devices. In 2018, Natural Cycles was the first digital birth control app to receive clearance from the FDA; fertility pioneer Clue was the second in March 2021.

Somewhat shockingly, regulation for sex toys doesn’t extend beyond the electrical compliance required for a Bluetooth speaker, escaping more stringent scrutiny through a “novelty use” labelling loophole.

Again, this is set to change, with the ISO making progress towards new standards⁴. Until this is finalized, the regulation of medical devices provides a good clue as to what action is needed to futureproof FemTech.

On a recent sex toy project, CDP ensured that all materials were biocompatible, although no regulations required it. Not only was this the right thing in terms of reducing risk for the user, but also protected our client against potential changes in regulation.

Forget about the tech

It may sound counterintuitive, but at CDP we feel the best way to succeed in FemTech is to forget the tech…at first, anyway. This is where we often see both big corporates and startups trip up.

We recommend a “solution agnostic” approach to design – that’s to say starting with a user need and looking for the best way to fulfil it. This might involve tech; it might not. Even then, the “tech” might not necessarily be digital, which is often what comes to mind when we think of FemTech. Instead, it might focus on the device itself, the manufacturing process, choice of material, or service. Whatever the solution, this method establishes early on if there is a market and business case for a product.

The alternative is “tech for tech’s sake”: just because it’s possible to measure the veracity of the female orgasm doesn’t mean that women want this data, as a startup that claimed to “spot women’s orgasms” found out when it was widely lampooned in the media⁵.

On this, it’s worth noting that we don’t see FemTech as limited to the fields of sex or fertility. The same contextual and experiential empathy that goes into designing for these areas must also be applied to other issues that disproportionally impact women. For example, we recently worked on a minimally invasive breast cancer biopsy device. Our goal was not only to design an accurate medical tool but to consider the experiential needs of the female patient – something that is often ignored.

Consider user acceptance

You’ve established a user need and a great tech-driven solution, but will female consumers feel comfortable using it?

It’s important to consider whether women are culturally ready to adopt a tech-led solution, particularly if it involves intimate wearables or sensitive data.

For example, current technology is capable of analyzing menstrual flow, but are women willing to accept intimate electronics? Let’s remember that in some parts of the world, tampon usage is still taboo.

Baking the female experience into the design process will answer these questions early on.

Ditch the defaults

We’ve discussed reframing design to include females; however, the same principles apply to other areas of inclusivity, such as race, sexuality, disability, gender identity, and economics.

In FemTech, this means considering, for example, the male experience – a heterosexual couple trying for a baby may want the capability for the male to log into a fertility app as part of the shared experience.

Likewise, it means considering the affordability of a design for various socio-economic groups. An expensive pelvic floor trainer may be financially out of reach for many women, so is it possible to reduce costs with smarter manufacturing or a new business model?

Good design considers all perspectives. It’s time to ditch the defaults.

To continue the conversation, get in touch: womenshealth@cambridge-design.com

1 – The Global Femtech Market was valued at $19bn in 2019 and is expected to reach $60bn Billion by 2027, according to Emergen Research.
https://www.emergenresearch.com/industry-report/femtech-market
2 – https://techcrunch.com/2015/06/09/apple-stops-ignoring-womens-health-with-ios-9-healthkit-update-now-featuring-period-tracking/
3 – https://www.cityam.com/mrs-ms-steve-rowes-first-blunder/
4 – https://www.iso.org/committee/7647858/x/catalogue/p/0/u/1/w/0/d/0
5 – https://www.bbc.co.uk/news/technology-53024123

Incisive action: Cutting the carbon footprint in surgery

April 22nd 2021

Kiron Athwal

Insight and Strategy Consultant

Hear us out: the pandemic has stretched world health services to their limits, but it may also be paving the way toward a greener future for healthcare.

When thinking of healthcare today, you probably picture the huge pressures on overworked healthcare staff and the scramble for hospital beds. What you may not have thought about is that hospitals in many countries have adopted innovation that inadvertently introduced ‘greener’ treatment. For example, the need to perform ‘virtual’ consultations has reduced patient travel to and from practices. In April 2020; within weeks of COVID-19 hitting the UK, 71% of all GP visits were remote, compared to 25% in April 2019.

“A single operation can have the same carbon footprint as driving 2,273 miles in an average sized gas-powered car.“

Before COVID-19, the UK’s National Health Service (NHS) produced 27 million tons of CO2 equivalent annually, which accounted for 5% of all UK carbon emissions. To combat this, in October 2020 the UK government announced plans for a greener NHS: net zero carbon emissions directly from the NHS by 2040, and its supply chain by 2045.

In the context of COVID-19, this is an ambitious goal even if we were able to sustain the kind of CO2 emission drops witnessed during lockdowns. The forced shutdown of elective surgery may have reduced hospital carbon footprints, but this has been at the expense of patient care and can’t continue. Further ahead, the NHS will be caring for an increasingly ageing population, putting demands on provisions which will lead to increasing energy and resource consumption.

The operating theater has extensive electricity needs, powering equipment, heating, ventilation, and air conditioning, and is three to six times more energy-intensive than the rest of the hospital. This electricity reliance coupled with anesthetic gas and the need for single-use equipment has a significant carbon footprint. Chantelle Rizan, a Fellow of the Centre for Sustainable Healthcare and currently undertaking a PhD to identify carbon hotspots in surgery, found that a single operation can have the same carbon footprint as driving 2,273 miles in an average sized gas-powered car.

So, aside from upgrading hospital buildings and moving to renewable energy supplies, the UK government must explore ways to make surgical practice more sustainable in order to hit the NHS net zero targets. This won’t be easy.

Virtual clinics have helped with triage (deciding severity and service allocation) and surgical follow-ups, but it’s difficult to plan surgery without examining the patients face-to-face. Any changes must avoid extra red tape and be economically viable for healthcare services. Advances may have trade-offs between short-term losses (retraining) and long-term gains (reducing hospital stays or complications). Most importantly of all, sterility must be maintained at all costs. Here’s a new mantra to repeat: green only if clean.

We’ve recently been exploring the challenges facing surgical providers in embracing sustainable change. In our ‘Circularity in Context’ article we considered circularity filters to ensure future products and services become carbon neutral. This philosophy of circularity, maintaining the value invested in materials and products, has applications in healthcare but may also come into conflict with other imperatives, such as sterility.

Before joining CDP I spent time working closely with orthopedic surgeons, observing procedures in the operating theater first hand, showing me where improvements could be found. Innovating in the surgical space is a complex and nuanced area, where first-hand knowledge of the sector is key. Surrounded by a team of engineers, designers, researchers, and healthcare-savvy innovators at CDP, we’ve applied the filters for circularity to identify areas in which circular approaches could provide significant advantages.

Short-term wins

There are many ways to reduce the cradle-to-grave carbon impact of surgical equipment, while engaging clinicians and being financially attractive to health service procurement. Layer upon layer of plastics and non-renewables are used in sterile packaging for implantable devices. If we can’t fully move away from these packaging conventions because of safety and transportation requirements, can we source materials from low-emission supply chains and use local production and assembly for more efficient, less carbon intensive shipping and distribution?

Delivering care with convenience and guaranteed sterility has tended to result in single-use equipment, but we are seeing signs of returning to reusable equipment which is reprocessed between uses. Reprocessing patient drapes, laparotomy pads and intravascular catheters are being used to reduce waste so long as sterility and accuracy can be maintained and improved cleaning cycles reduce energy and water usage. Reprocessing of instruments has been driven more by cost concerns rather than sustainability, but this hints at the potential economic benefits of reprocessing beyond complex instruments. This could be further bolstered if the hospital can receive reimbursement for reprocessing an instrument instead of purchasing a new one.

There will always be cases where single-use equipment is a necessity for sterility or convenience, or where a Life Cycle Analysis shows this to be the most environmentally friendly approach. We can still streamline these sets so that rarely used kit is not disposed of even when it hasn’t been used, as is often the case once a set is opened in theater.

Long-term innovation

Given the need to develop better treatments and the burden of evidence needed to establish safety and efficacy for devices and systems, the healthcare industry can perhaps be forgiven for not having led in the sustainability space. Healthcare requirements are a barrier, as materials must be well understood and de-risked for a specific healthcare scenario before they can be used, but this should not stunt long-term innovation.

One way that future technology could reduce surgical waste is by harnessing fluid-resistant materials, improving the efficacy and safety of personal protective equipment. Going further, incineration techniques could be completely transformed by advances in energy recovery processes: being able to create large amounts of heat or electricity to feed back to the hospitals efficiently and at a larger scale than currently performed.

An emerging technology that promises radical change in surgical training is extended reality – simulating virtual environments or even overlaying them with real environments to enhance the experience. Extended reality expands access to expert training while streamlining the associated hospital footfall and travel. Virtual reality headsets are allowing trainees to view, practice, and learn surgical procedures, reducing the hours needed to be spent in surgical theaters.

The advent of very low latency wireless technologies, including 5G, could allow us to push virtual care even further. Even when surgeons are in a different country and time zone to the patient altogether, mixed reality could allow expert surgeons to offer real-time assistance and robotically assisted surgery systems could enable entirely remote surgery. This reduces travel but more excitingly it widens the opportunity for patients to receive specialist care wherever they live.

Societal filters: rapid recovery and reduced complications

There’s a risk we limit our understanding of surgical carbon footprint to manufacturing, electricity usage, and disposal. But we must consider the trickier question: how can we reduce the burden of the patient on the healthcare system through improved outcomes and reduced complications? One study found that anti-reflux surgery on the NHS could, despite having a high initial financial and carbon cost, be more carbon-efficient than ongoing medical treatment by the 9th post-operative year (and cost-efficient by the 14th year).

One tool in the arsenal is less invasive procedures. These require more specialized training and increase procedure complexity, particularly during early adoption, but they can drastically reduce patient recovery times and pressure on hospital beds. Less invasive procedures can also reduce the number of rehabilitation trips required for physiotherapy and occupational therapy.

Innovations that reduce follow-ups should be pursued and anything that reduces post-surgical complications or provides more durable treatment is likely to drive better overall sustainability. For example, improving surgical wound closure systems could help reduce infection rates, one of the leading causes of hospital readmission following surgery (3% of patients die as a consequence). The medical device industry can also deploy digital health tools to improve medication compliance, to introduce disease prevention strategies and to stimulate rehabilitation, all of which will lead to better outcomes from surgery and minimize unnecessary procedures, in turn reducing the carbon footprint.

At the heart of innovation is the need to understand the user. Following my experiences with surgical professionals in the operating theater, it’s great to be part of an innovation team at CDP that actively pursues “green” solutions while being respectful of the vital work that surgeons do.

What next for events in the world of COVID-19?

February 11th 2021

Ana Romero

The impact of the pandemic has dramatically shaken up the world of conferences and events. Ana Romero, Digital Marketer and Events Coordinator at Cambridge Design Partnership, looks back over a tumultuous year and asks, “what next”?

Here we are, almost exactly a year ago. This is Pharmapack, in Paris. The two-day event was attended by 5,500 delegates and more than 400 exhibitors, and just look at the optimism on our faces! We don’t know it yet, but this is the last physical event we’ll attend before the pandemic descends, forcing us into our spare rooms and at the mercy of Teams, Zoom, and an emerging world of digital-only events.

website_body-2_Pharmapack-2020-team-photo

CDP team at Pharmapack in Paris, February 2020. Left to right; Jon Powell, Senior Consultant, Ana Romero, Digital Marketer and Events Coordinator, Martha Hodgson, Market and Design Insights Research Consultant, and Uri Baruch, Partner and Head of Drug Delivery

Welcome to chaos

A month later, in March 2020, the events world was in chaos and turmoil. I was busy contacting the organizers of each event we were planning to attend, checking their websites for updates, following the news and, arranging refunds for cancelled conferences.

Many conference organizers simply announced that they would be cancelling their 2020 offering and would be back in 2021. But others tried to offer a virtual experience. The idea of going online came as a relief to us here at CDP – as we were just as keen as before to share our expertise, to network with our peers, and meet tomorrow’s clients.

However, the transition to digital has been challenging, especially for event organizers dramatically adapting their business model and event delivery in a matter of weeks.

Forget the plan; it’s time to adapt.

So what have we learned? The most immediate learning for us at CDP has been that a good virtual conference is made by enabling the sort of interactions which make a physical conference so valuable. Can you get talking with someone who is visiting the event, develop them into a contact and then, hopefully, a client?

The first virtual conference we attended didn’t work at all. We couldn’t network with other companies, to reach the people we wanted or to strike up any sort of rapport with other attendees. A low bar had been set.

However, in only a few months, I’ve seen many improvements in the different platforms used and some real successes so far. Several virtual conferences we’ve attended during the pandemic have offered far better ways to connect and market to delegates. For example, some platforms allowed us to see and filter the delegate list (by name, role, and company name) and to request a one-to-one meeting with that person. In addition, the ability to watch presentations on demand made it easier to book a video conference during a session slot and later go and watch what we missed, something that wouldn’t have been possible in a real-life event.

Virtual conferences have crystallized how important it is to have your material in a digital and interactive format. We’ve learned to have just enough collateral in the digital booth to initiate a conversation. In a virtual exhibition, it’s easier to visit a virtual booth and fill your virtual goodie bag with all of the marketing collateral with just a click. This also has the risk of someone (including competitors) downloading your collateral and leaving the booth without saying a word. While this wouldn’t normally happen in a physical event it’s not hugely problematic: our materials can be seen by anyone.

One downside is that international events may not be particularly international: if an event is taking place on UK time and you want people from around the globe to virtually attend, you’ll find it’s tricky to enable conversations between members that are located in the US or Asia, due to the time differences. We’ve noticed this effect in the events we’ve taken part in.

We don’t expect face-to-face events to return properly in 2021. While many events companies are talking a confident game that plans for face-to-face before the year is out, most are adding other options to their virtual offerings. Think here of webinars, roundtables, and other additions that tend to suggest the new world is becoming “baked in”.

The post-COVID future

With all this virtual activity and conferencing ability we might ask, will the world ever go back to face-to-face conferences?

My feeling is that the leap to digital is a permanent one, but that physical conferences are far from finished. There are some significant downsides to virtual interaction that cannot easily be overcome. The first is the nature of the experience itself. If you travel to a conference and attend for two or three days, you are committed. You have spent time and money to get there, and as a result, you give it all your attention and energy, which makes the whole process more immersive. By contrast, a digital conference that you attend from your office or your home will struggle to capture your focus in the same way. You are so much more likely to be reading emails, on the phone, or being interrupted by your kids.

The very fact that it is so effortless to attend a digital conference can make it a less valued experience. If you “bump into” a delegate virtually, by messaging them through the conference’s contact system, it is far easier for them to ignore you. However, if you meet them at a real-life event, your chances of striking up a fruitful conversation are far stronger. Perhaps this is a good thing, forcing us to be on top of our game, offering nothing but compelling content and conversations. Although the process of tracking down attendees at a virtual conference is now quite slick, there’s nothing quite like meeting someone face-to-face and all of the non-verbal communication that goes into those meetings. It’s much harder to entertain clients and to build up goodwill online.

website_body-2_Ben-Strutt-presents-at-GIF-Virtual

Ben Strutt, Partner and Head of Design and Front-end Innovation, speaking with Max Angelov from the CDP offices in Cambridge ahead of presenting at last year’s virtual Global Innovation Forum, November 2020.

A crucial part of why we here at CDP value the opportunities offered by real-life conferences is that they give us the chance to develop and share our thought leadership. The events themselves are part of our learning, and giving presentations or taking part in discussions help to raise our profile in our key markets. The fact that a virtual conference presentation will often remain online for several months extends our reach significantly. We can also access information which shows who has been watching and, where it’s relevant and data protection allows, we can reach out to them afterwards to continue the conversation. This is a definite advantage of virtual events.

But when it comes to displaying our physical work, we want to show off what we create and allow booth visitors to interact with a connected inhaler, an innovative food packaging concept or any other product development. It’s hard to replicate the tactile experience of a real-life display of products that can be picked up and examined and where a visitor can ask questions of the development team.

All of this suggests that we’re headed for a future of hybrid physical and digital events. There will be real-life events, but some digital tools, such as one-to-one video meetings, digital roundtables, and others currently being explored will stay. Physical and remote attendees will be offered far more connectivity and interaction with other attendees than ever before.

In the meantime, as we press through the pandemic and emerge on the other side, it’s crucial to be creative and make use of the technology that allows us to connect virtually. It’s fair to say that virtual events are not yet providing the full benefits of a real-life gathering. But we are where we are and it’s in all our interests to work with digital alternatives so that we can keep doing business, one way or another. Whether it’s physical or digital, I’ll see you there.