Does your organisation want to have a happier, more self managed, empowered workforce. The “Buurtzorg” model explained
You may have already heard of the Dutch ‘Buurtzorg’ model of care? It uses community oriented, self-managing teams typically made up of 10-12 people who decide amongst themselves how to organise their work, share responsibilities and make decisions. It is non-hierarchical. Instead it encourages autonomy, flexibility and faster decision making.
Buurtzorg also puts patient/service user self-management of care at the heart of how it operates. Personalisation is central, as is care teams having smaller, stable groups of people they support, with whom they develop a closer relationship and understanding of their needs and preferences.
Benefits
Across multiple studies Buurtzorg has helped deliver:
improved job satisfaction
better staff retention
fewer staff absences
client (service user) satisfaction
improved outcomes
greater independence and reduced ‘care consumption’
lower admissions to care homes, nursing homes or hospitals
greater productivity
UK Examples
Buurtzorg or similar models using self-managing teams and related principles are in use in both domiciliary care and community nursing in a number of services across the UK.
One great example is Wellbeing Teams. Founded in 2016 they are a domiciliary care provider that works in locations including Oxfordshire, Wigan and Thurrock. They use small neighbourhood-based teams, made up of staff that have autonomy and are trusted to build relationships with the people they care for, in line with each persons’ preferences.
Home care startup BelleVie has recently secured £2.1m of funding from a range of investors to scale up their operations in the North East of England and expand into Buckinghamshire. BelleVie have been inspired by the Buurtzorg model.
They use 10 person teams of self-managing ‘wellbeing support workers’. There is a much smaller layer of wellbeing support leaders and coaches, and an even smaller team managing IT, strategy, finance and marketing. Unlike in other organisations the pyramid is inverted, with people being cared for at the top, followed by the support workers, then their support team and finally the ‘Leadership Circle’
Changes required
Staff pay
Alongside the changes in how care services are structured (being much less hierarchical) Other changes are needed in order for implementation of Buurtzorg or similar models of care to be feasible. Wellbeing Teams use a salaried model for their care staff rather than paying by the hour, while BelleVie do not use zero hours contracts and are an accredited living wage employer.
This is going to be tricky for most home care providers to achieve, as most would already like pay staff more if they could, but can’t due to funding being so tight.
Even if pay isn’t increased right away, giving staff more autonomy and ownership can improve satisfaction and reduce stress.
Commissioning
The key change in making Buurtzorg feasible across the UK is a change in how care is commissioned, away from contact time and towards an outcome based model. This has long been talked about but still very patchy in implementation in the UK.
Artificial Intelligence (AI) and Machine Learning
The rise of ChatGPT and similar machine learning driven chatbots has led to acclaim and alarm in equal measure about their potential impacts on every sector, and on many jobs.
The use of AI and machine learning in home care is still in its infancy, but there are a few examples being either tested or imagined right now:
Schedule and route plan optimization for care workers (one pilot scheme in Bristol and Cornwall saw a 40% mileage reduction and 25% increase in workforce utilisation with no reduction in care for service users).
Machine learning to identify signs of deterioration or early indicators of ill health less visible to humans
Identifying signs of pain using facial recognition and AI in people who cannot reliably self-report their pain
Assisting in calculating risk (for example in falls risks assessments)
Automating processes, for example recommending follow-up actions following an assessment, audit or incident
These are just some examples of where AI and machine learning have been used in home care to date. The sophistication of these technologies is now speeding up and given they have uses across almost every sector you can think of, including the military, they will receive even more funding and interest to further increase how effective they are.
Details on how much militaries are investing is a little hard to come by as you might expect. But the competitive wrangling and billions of dollars being pushed into AI by Google and Microsoft should be evidence enough that the technology has recently taken on a new level of serious backing.
These developments will inevitably result in technologies for social care at some point.
AI and machine learning can cause concern. Staff may fear for their jobs or how they might change. Everyone concerned with the welfare of people being cared for may fear people being overridden in decisions about their care or treated more coldly, as a number rather than an individual.
However, where AI and machine learning has been deployed elsewhere, such as in healthcare, the focus has been to support and assist health and care professionals, as a useful tool rather than an overbearing, all-knowing master.
AI and machine learning does what computers do best, but better. Processing data and making calculations, much faster than us humans could ever do so. It takes this burdensome, time consuming work off our shoulders and serves us up insights based on the numbers and patterns it has processed, giving us genuinely useful information to help us make the right decisions more easily and reliably.
But the decision making power still lies with us.
Combining AI and machine learning with next generation telecare/telehealth is going to really take things to a new level. Helping domiciliary care providers and local authorities to plan, manage and deliver care with much greater safety, quality and efficiency.
Next generation telecare/telehealth
One of the ground-breaking innovations in home care right now goes by a number of different names; digital telecare, remote monitoring, technology enabled care are some of its monickers.
One prominent, particularly innovative example that I know well incorporates elements of AI and machine learning, which was first created by Alex Nash in 2015 while he was studying at Loughborough University.
After his grandfather was diagnosed with Dementia, Alex wanted to use the latest in technology, both physical devices and data analytics, to help keep his grandfather safe, maintain his independence and give peace of mind to himself and his family members.
As the solution he created developed further, enabling proactive care became a focus, as well as enabling care providers to make more intelligent decisions around care and enable a more preventive approach.
Local authorities across the UK are now adopting Alex's solution.
For more info on digital healthcare visit
https://www.digihealthcare.scot
Measurable Impact
A 2020 study (part funded by NHSX) into the impact of digital healthcare for domiciliary care service users recently discharged from hospital found that:
40% of care plans were updated as a result of the intel they gained – usually to be more personalised to the user
83% of families said it provided an increase or significant increase in reassurance and peace of mind
Robots
The use of robots in social care has always come with a whiff of controversy and suspicion. Understandably. Social care is a complex field and requires more human warmth perhaps than any other. The use of robots is often suspected to be a way to try and overcome financial and staffing shortages, in a way that has little regard for the actual mental and physical health of people being cared for.
Big in Japan?
Japan has been trying to create robots to care for older people for around two decades. By 2018 the Japanese government had spent over $300m on research and development for robotics in social care.
Many different robots have been developed as a result, including:
‘Hug’ and ‘Robear’ - robots that assist care workers with lifting people
Paro – a robot seal (yes like the animal) designed to offer a robotic form of animal therapy (good natured pooches must have pushed up their prices in Japan)
Pepper – a robot that runs exercise sessions with residents in care homes or people in their own homes
Despite the enormous investment and push from government to make robot carers a reality, they haven’t really taken off. A national survey in 2019 revealed that only 10% of social care providers had introduced any form of robotic assistant to care and there’s little sign this will change anytime soon.
What’s the problem?
The key drawback with robots in care, besides the ethical, quality, of safety considerations, is that where they have been tried (most notably in Japan) they have actually created additional labour for staff, who have to move, maintain, intervene, set up and learn to use the robots. Similar attempts to introduce robots in Sweden, Denmark, Italy and other countries have met with similar problems.
The robots themselves have had limited effectiveness. Lifting robots proved uncomfortable for most and impractical even in a residential care setting. Whereas companion robots like Paro (the robotic seal) seemed to have stimulated more repetitive or obsessive behaviours in some people with dementia. It was hoped that the robot would reduce these kind of behaviours.