The past decade has seen rapid development and adoption of technologies that change the way we live. But which technologies will have a similarly transformative impact on health and care?
The King’s Fund has looked at some examples of innovative technology-enabled care that are already being deployed in the NHS and internationally to transform care. Now, we examine the technologies most likely to change health and care over the next few years.
Some of the technologies we discuss are on the horizon – others are already in our pockets, our local surgeries and hospitals. But none are systematically deployed in our health and care system. Each could represent an opportunity to achieve better outcomes or more efficient care.
It’s been eight years since the launch of these pocket-sized devices we now know so well. We take them for granted but our phones combine: computing power that could steer a spacecraft, a connection to the internet, a host of sensors for health-relevant data like movement and location tracking, plus a touch-screen interface.
Two-thirds of Britons use them to access the internet (Ofcom Technology Tracker 2015), and few would regard these devices as ‘new’, yet the smartphone’s potential is yet to be realised in health and care.
App stores already feature thousands of health apps, though their uptake for health and care has been patchy. Efforts to curate the best quality apps, for example in the NHS App Library, have had little success so far (Huckvale et al 2015).
One of the more sophisticated apps in use in health care is Ginger.io. In this depression programme, people track their own mood and this is combined with data collected from the sensors in the smartphone about their movements, social app or telephone use. The data can be shared with clinicians and offers people an intervention when their data suggests they might benefit from support.
Smartphones can serve as the hub for sophisticated new diagnostic and treatment technologies. So, for example, people with type 1 diabetes dissatisfied with the progress of medical technology companies are driving the development of an artificial pancreas. This links continuous glucose monitoring and insulin-delivery systems that are all controlled by the smartphone. It will adapt its algorithms for insulin delivery to a person’s physiology.
Smartphones are highly effective data collection devices and they can record a lot of detail about people’s lives. As well as tracking their own health status, people can also help researchers gather large amounts of data on health problems and their determinants using their smartphones.
The first long-term and large-scale opt-in disease studies are just beginning. Apple seeks to support large-scale studies using patients’ iPhones by providing its ‘ResearchKit’ software platform for researchers to tackle any research question. uMotif is seeking eventually to build a 100,000-person study into Parkinson’s disease, tracking variables using a smartphone app.
Hospital-level diagnostics in the home
These include portable x-ray machines, blood-testing kits and other technology that can provide more and more of the diagnostics required to support health care, with profound consequences for the way we configure our health care system.
At a recent conference at The King’s Fund on emerging primary and acute care systems, Dr Michael Montalto described how these technologies and others enable the safe, high-quality acute care service that his team has provided for people in Victoria, Australia, in their own homes for 20 years. One recent innovation in this area is the AliveCOR ECG embedded in a smartphone case that helps interpret test results via an app and facilitates secure sharing with clinicians (NICE evidence review).
Smart assistive technology
Many people with disabilities or long-term conditions use assistive devices to help them perform tasks or activities made harder for them by their disability or their condition. These are often available as part of NHS and social care packages. The prospect of using these to gather information in addition to achieving a specific task is motivating several new developments.
Verily (formerly Google’s life sciences arm) has invested in a tremor spoon already on the market for use by people with Parkinson’s disease, for example. By incorporating sensors and deploying its data analytic expertise, the aim is to provide people or health professionals with information about how someone’s tremor characteristics and severity change over time – and to understand more about the disease across a population. Smart inhalers like those in development by Propeller Health work on a similar idea, passively detecting each use, location and the surrounding air quality, allowing insights into what triggers asthma attacks.
One company has developed sensor technology so small it can be swallowed and combined with drugs in pill form. When the pill dissolves in the stomach, the sensor is activated and transmits data to a wearable patch on the outside of the body and on to a smartphone app. This enables patients and their clinicians to see how well they are adhering to their prescription.
Proteus Digital Health began the US Food and Drug Administration (FDA) regulatory process for this technology in 2015. The treatment now undergoing review combines the technology with an anti-psychotic drug, raising questions about how health systems could use the technology and how privacy and autonomy for patients will be affected. The company are also investigating other potential applications including assisting those with long-term conditions such as dementia and Parkinson’s disease to remember to take their medications.
Implantable drug delivery
New automated drug delivery technology is under development by a firm set up by researchers and engineers from the Massachusetts Institute of Technology (MIT). They are developing an implantable device with hundreds of tiny, sealable reservoirs that open when a small electric current controlled by an embedded microchip is applied (Farra et al 2012). The team developing the device say it could provide a way to automatically release doses for more than 10 years from a single chip. They are developing the technology for long-term condition medication as well as for contraception.
Many digital therapy platforms include a way for people to connect with peers and share their experience, or to connect with health professionals remotely. Whether they are fully automated or blend automation with supervision, the therapy offered can be tailored to the needs of the specific user. Digital therapeutics are often cited as a solution to help manage long-term conditions that call for behaviour changes or to prevent diseases in the long run.
Computerised cognitive behavioural therapy
The use of computerised cognitive behavioural therapy (CBT) in the NHS has a relatively long history. Two recent independent studies looking at early-generation computerised CBT suggested that the main limitations in effectiveness were due to people failing to complete the course. Adolescents were more likely to finish the programmes and so benefited more from them. (Gilbody et al 2015, Smith et al 2015).
Recently, a new generation of automated digital therapies based on CBT has been developed that aims to deliver CBT at scale with better engagement. Sleepio is one example, a six-week tailored programme delivered via the web, designed to treat insomnia, and in doing so help alleviate anxiety and depression. There have been positive early results in randomised controlled trials (Espie et al 2012, Pillai et al 2015). The therapy is personalised in response to data provided by the patient and by using the latest practice in design and delivering the therapy via an animated avatar, the course is made more engaging. Design and personalisation are key elements likely to improve engagement, and therefore outcomes, in digital therapies of all types.
New preventive digital therapies
Another class of digital therapies are in development to help people make changes to reduce the risk of developing long-term conditions. Interventions to change lifestyles through regular coaching and group sessions can reduce the risk of developing diabetes. Sean Duffy, CEO of Omada Health, which delivers online therapies for a range of conditions, gave a presentation at The King's Fund Annual Conference, showing how the company has achieved positive results in its early evaluations in the United States.
Falling sequencing costs
Twenty years have passed since the first complete genome sequence of a living organism was produced and twelve since the first human genome was sequenced. In that time, the economics of genome sequencing has changed significantly. The US National Human Genome Research Institute estimates that the marginal cost of sequencing a single person’s genome has now come down to $1,000. However, the upfront costs are still high and likely to remain so for a long time.
The cost of sequencing could fall further thanks to new sequencing techniques using nanopores developed over the past few years. Nanopores are very small holes that DNA molecules can pass through. When an electric current is induced through the pore, variation in the current as DNA molecules are passed through can be used to infer their make-up. Oxford Nanopore Technologies uses this approach to offer very small genome sequencing devices, far more portable than the larger, fridge-sized machines used in traditional laboratory-based sequencing.
Major projects are under way internationally to gather large databases of genomes and analyse them to find relationships between genetic make-up, people’s disease risk and experience, their physical characteristics and their behaviour.
In the United Kingdom, the government is sponsoring the 100,000 Genomes project in England. Human Longevity Inc in the United States promises to build a database featuring 1 million genomes by 2020 and currently has 20,000 sequenced genomes linked to other data about the person’s physical characteristics. Verily aims, with its Baseline study (a research collaboration between the company and Stanford and Duke medical schools), to analyse large amounts of volunteers’ linked genome, lifestyle and physical data to develop a better understanding of how all that data looks when a person is healthy and identify the changes that indicate disease at an earlier stage.
New insights into big datasets
Several new businesses hope to use these techniques to provide diagnostic support. Enlitic in the United States has created a tool for radiologists that uses previous findings and other data associated with existing images in its databases to spot patterns in images and the data to help spot likely mistakes and rule out extremely unlikely options. Both IBM’s Watson and Google’s DeepMind – the two most famous artificial intelligence organisations – have started to explore potential applications in health care. For example, IBM Watson is studying whether applying machine learning to large amounts of unstructured data like clinical guidelines, scientific literature and treatment protocols could help optimise cancer treatment.
Here at The King’s Fund, we are working with colleagues at Demos’ Centre for Analysis of Social Media to see what is practical and ethical in terms of applying machine learning techniques to user-generated content on the internet. We are hoping to understand the insights that health systems can glean about patient need and how services meet that need.
Blockchains are decentralised databases, secured using encryption, that keep an authoritative record of how data is created and changed over time. Their key feature is they can be trusted as authoritative records even when there is not a single, central, respected authority updating them and guaranteeing their accuracy and security. This derives from the mathematical properties of the way the data is recorded and the difficulty it would take to break the rules and successfully alter the record.
Decentralised health records
Electronic records for health care are now widely used, but they are stored on centralised databases, secured and provided by a small number of suppliers. Some commentators have described how a decentralised database using blockchain technology to contain all or some of patients’ health information would work, with the patient or clinician given the keys to control who else sees the data.
They argue that the system would be more resilient as no single organisation houses the data and that switching to or incorporating blockchains into existing systems could help to speed up the transition to interoperable patient records. The technology could be applied to create accurate records of health interventions and eventually verified outcomes, which could be used as the basis for reimbursing providers for the health outcomes they achieve for their local population.
Behind all technologies, there are people. The internet and the devices and technology it has enabled have facilitated the development of many communities, bringing together people around a common interest, a shared identity, a social movement, or even just hashtags.
Peer-to-peer support networks
Connected communities for health are growing in their membership and their diversity. Several platforms bring together people with interests in health and care within countries and across the world to support each other, share learning and even provide a platform for tracking their health data or helping them manage their condition.
MedHelp, PatientsLikeMe and HealthUnlocked are just three of these social networks for health. Alongside these dedicated networks, platforms such as Twitter and Facebook that dominate the social network market in the United Kingdom have also become key places for disseminating and discussing health and care information and best practice – as Daniel Ghinn of Creative Health told our Digital Health and Care Congress in 2015.
Communities contributing to research
Some online communities are already contributing to research about their health conditions, offering people the chance to be ‘data donors’ and providing a simple way to share their data with researchers. PatientsLikeMe has already been used to contribute to nearly 70 published studies, including a study credited with new discoveries about the disease progression of amyotrophic lateral sclerosis (ALS).
Healthbank offers a different model, and is described as ‘the world’s first citizen-owned health data transaction platform’. Members pay a one-off fee to store health data securely and control who it is shared with. The organisation is a co-operative, so profits made using the patient data are paid out in dividends to its members.
With new technologies like these come new opportunities for our health and care system: improving the accuracy and usefulness of information we can gather on our health as citizens and patients; changing how and where care is delivered; and offering new ways to prevent, predict, detect and treat illness.
But along with these opportunities come challenges:
- how to ensure universal access to any benefits through the NHS, ensuring the system doesn’t get left behind by a consumer market and fail to provide poorer or excluded citizens with their benefits
- how to encourage uptake of new care methods and models built around them throughout our system
- how to deal with the great volume of health information these technologies can generate.
The technologies we’ve highlighted here are not an exhaustive set. Many of them could transform health and care but more evidence is needed on their costs and benefits to deliver on their promise. Ask too much or give too few opportunities for real-world testing and we risk protecting an outdated status quo. Ask too little and we risk spending public money on something ineffective.
Above all we must not lose sight of the people behind the technology and their needs – the patients, citizens and communities for whom it will be put to work.
- Attend our digital health and care conference in July 2016
- See more of our work on technology and data
The authors would like to thank the following: Eric Topol for his insights into blockchain and new directions for digital health; Craig Venter, Ruby Gadelrab and Brad Perkins for a vision of the future of genomics; Pat Saxman, Peter Hames and Sean Duffy for their insights into digital therapeutics; Donald Jones for his insights on drug delivery and patient-driven data; Michael Pellini, Luke Hutch and Joon Yun for helping develop our thoughts on precision medicine; Nick Dawson, Rebecca Hope, Chris Natt and Scott Noppe-Brandon for their expertise on innovation. Rupert Dunbar-Rees and David Ewing Duncan for reviewing early drafts and providing valuable feedback; and Vishal Gulati, Jack Kreindler and Daniel Kraft for their support and assistance in the research phase.