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The future of healthcare is prevention, rather than cure

COVID-19’s impact can be felt in every sphere of life, but nowhere more obviously than in healthcare. The scale of the challenge means the pace of innovation is rapidly accelerating as tools and technologies are used in new ways. While the pandemic is temporary, the surge in use of technology in healthcare is a huge jump forward and one that will likely become a long term trend. We’re now seeing some of the most valuable uses of digital in enabling preventative care. In the first article of this healthtech series, we’re going to look at how technology innovation in tracking and monitoring, and analysis and prediction is opening up new use cases.

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Preventative healthcare is now the number one priority of governments across the globe

Digital tools are helping enact social distancing, trace contacts, and save lives. Even in normal times, the difficulty of treating any ailment increases over time. There is a huge incentive for healthcare providers to be able to intervene early, and prevention is a step better than early intervention. However, it’s impractical for everyone to visit a GP every day, and difficult to spot the micro changes in diagnostics that tell us where problems might arise before they do. What technology will help us to do is drive down the cost of monitoring our health, and make it achievable at scale.

Healthcare will move from something we access at the point of need to a feature of everyday life

Advancements in sensor technology and machine learning will radically improve our ability to continuously gather data on public health, accurately predict individual risk factors, and measure the impact of preventative care on the health of individuals and society at large. We’ll be able to deliver better, more personalised care, improving outcomes and efficiency.

Tracking and monitoring

Contact tracing and social distancing are powerful tools for fighting COVID-19, but we’re rapidly moving to a world where we can use these tools to monitor the health status of individuals at scale. An increasingly sophisticated range of Internet of Things (IoT) sensors and health tech wearables can be used to gather and transmit our health data. The falling cost of IoT sensors and the increasing power of mass-market consumer-grade devices, such as Apple Watch, means that equipping everyone with the tools to gather more data about their health is becoming more cost effective every day.

Apple Watch’s ECG functionality is able to detect rapid or skipped beats - even sending a notification if an irregular heartbeat appears to be an atrial fibrillation. Patents and partnerships show they’re looking at blood pressure and contact free glucose monitoring, while rival Fitbit is pushing ahead with blood oxygen monitoring. It’s not just wearable devices, with researchers working on how to use ‘smart toilets’ to enable continuous analysis of samples that are only otherwise taken in a clinical setting.

As consumer devices become more sophisticated, so too do medical devices, which are coming online. Examples include smart Continuous Glucose Monitors which allow diabetic patients to monitor their insulin levels via a connected app on their phone, and connected inhalers which - amongst other benefits - promote medication adherence.

As well as adding more sensors, we’re also finding new ways to use devices we already have. Researchers are exploring the value of ‘digital phenotyping’, using the data we have on our smart phones about our interactions with others and our activity to identify people at risk of mental health problems.

Wearable devices can connect with each other and other devices to create an IoT network for performing highly personalised healthcare activities, such as diagnosis, monitoring and treatment. Increased data access and data sharing via these devices has the power to facilitate highly personalised care adapted to the individual patient’s profile.

Analysis & prediction

All of the data collected by new ways of monitoring ourselves is no use without giving patients and HCPs the ability to draw insights from that data and to take meaningful action; whether that’s through lifestyle changes or medical interventions. Around 70% of healthcare decisions rely on diagnostic data. But care journeys are frequently fragmented, and often rely heavily on the patient as the single source of truth to make the right decisions - initially about their health and then about monitoring conditions as well as treatment and medication compliance. Being able to analyse more data at scale, and deliver it to HCPs who can make the right treatment decisions, will improve the quality of that decision making.

The ability to make predictions based on massive amounts of patient data is underpinned by progress made in the field of Artificial Intelligence, in particular Machine Learning (ML). ML is already being used to support HCPs in medical applications in clinical settings, with early image- based diagnosis. For example, checking a CT scan for patients who are showing signs of respiratory issues and chatbots that support triage decisions have grabbed the most headlines. It’s being used to predict which patients are most at risk from coronavirus so we can take extra measures to protect the most vulnerable.

When combined with continuous monitoring of a broad spectrum of factors, ML can help us find patterns in this huge volume of data and identify combinations of indicators that predict which patients will need more significant medical interventions in the future. Patients can then be guided to make either lifestyle changes, or receive care now to fix or manage small issues before they become significant issues, improving quality of life and reducing the number of people who become critically ill. It can also help us make better therapeutic decisions, enabling doctors to identify treatments that have the best chance of success for a given patient and their ailment.

Personalised, preventative care will enable us to deliver better health outcomes for everyone, at a sustainable cost.

However, there are some key considerations for organisations that want to start making use of the innovations we've identified.

User-Centred Design:

No matter the potential of an innovation, if it doesn’t deliver clear value to users - patients and HCPs - uptake will be limited, and the benefits will never be realised. When identifying how to harness technology, it’s critical that we start with the needs of patients and professionals, and identify how to use technology to solve them - not the other way round.

Collaboration:

Taking a collaborative approach to problem solving through partnerships will enable healthcare organisations to make use of the capabilities of suppliers throughout their ecosystem. Creating open innovation ecosystems that enable the organisation to easily work with partners will reduce duplication of effort and let each partner focus on delivering their core value.

Privacy and Security:

The data we’ll be able to gather and use has the power to do enormous good, but without proper protections in place the privacy ramifications are enormous. To get the most value out of monitoring and ML, people need to be willing to share that information - to ensure they feel secure doing so and HCPs feel comfortable recommending it, security and privacy need to be fundamental considerations at the beginning of the solution design process.

Coming up

In future articles in this series, we’ll explore more of the healthtech trends that we’re seeing accelerate, and helping you understand the key considerations to making them work for your organisation, your staff and your patients.

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