Are we forgetting how to care?

The profound act at the heart of nursing.

Helen Cowan

Helen is a registered nurse and freelance writer, writing for audiences ranging from the general public to practitioners and scientists.

A nurse bends beside a bed and talks to a patient

Marie Curie.

Recently, at a nursing leadership programme in Oxford, attendees focused on the fundamentals of care. Have we forgotten how to care? What can we re-learn from those who pioneered an ordinary yet profound act that affects millions?

Anam Cara is an old Gaelic term for ‘soul friend’, a person with whom you can share your innermost self, your mind and your heart. It is a term that Tom Hill, former chief executive at Helen House Hospice in Oxford, used to describe the relationship between his staff and the thousands of children and their families who passed through their ‘big red door’ in its first twenty-five years. The hospice (or ‘loving respice’ as it became known) had been founded by Sister Frances Dominica in 1982.

Other care in this country can also trace its religious roots. Between 1048 and 1070 in Jerusalem, the Order of St. John was founded for the purpose of helping pilgrims (“our Lords, The Sick”) who had become lost, weary, or beset by other difficulties while on their way to the Holy Land. Today, in the United Kingdom, the British Association of the Order has extended care to older people first in almshouses and later in care homes. A trustee for ten years was John Monckton, a man of ‘considerable talent, enormous integrity and deep religious conviction’; his tragic murder in 2004 led to the creation of the John Monckton Memorial Prize, which recognised and rightly celebrated commitment to care by care workers.

Today, across the world, seen and unseen, nurses, carers and families continue to provide compassionate care. “Assisting individuals, sick or well, in the performance of those activities contributing to health or its recovery (or to peaceful death) that he would perform unaided if he had the necessary strength, will or knowledge” is the very essence of nursing, captured by ‘architect of nursing’, researcher and author Virginia Henderson in 1966. Meeting more than basic needs such as breathing, eating, drinking and eliminating bodily waste (which are of essential importance), Henderson recognised the role of the nurse in enabling humans to communicate with others, worship according to their faith, satisfy curiosity and sense accomplishment.

In the desire for modernisation and professionalisation, have we lost sight of the core values and activities central to patient care?

An uncomfortable truth brought out in healthcare reports such as the Final Report of the Special Commission of Inquiry (The Garling Report) 2008, and the Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry (The Francis Report) 2013 is though that this type of nursing is too often done badly or even missed, leading to pressure injury, medication errors, hospital-acquired infection, falls, unplanned readmission, critical incidents and mortality. According to nurse scientist and scholar Professor Debra Jackson, “missed care occurs much more frequently than we might think”. She cites a systematic review in which ‘care left undone’ on the last shift ranged from 75 per cent in England, to 93 per cent in Germany, with an overall estimate of 88 per cent across 12 European countries’.

In one offensively-titled paper, “Shitty nursing - the new normal?” (in which the authors apologise for the title but not the questions raised), real-life pen portraits are drawn of patients lying for hours on hospital trolleys, immobile through infection or injury, ignored by staff. Whilst acknowledging contextual factors for poor care, such as a shortage of nurses and resources, the authors argue that circumstances cannot be the sole cause of missed nursing care.

A report published by the University of Adelaide, School of Nursing, has called for nurses to ‘reclaim and redefine’ the fundamentals of care. It asks whether the cause of the problem (of missed nursing care) lies “deep in the psyche of the nursing profession itself?” “Has something happened to the way modern nursing views and values caring?” it continues. “Indeed, is nursing in danger of losing its claim to care? In the desire for modernisation and professionalisation, have we lost sight of the core values and activities central to patient care? Or is this a broader social pattern where individuals are less inclined to show kindness, compassion, and care for others even if it is a necessary requirement of the job?”

Compassion, he emphasises, is more than empathy - and way "less fluffy" but much more measurable than kindness.

Writing in the British Medical Journal, Professor of critical care medicine Peter Brindley and Consultant in intensive care Matt Morgan wonder whether doctors also “too often default to high-tech and low-touch” when patients are dying – a time “when community and connection matter most”. They powerfully begin with a mother’s comment: “Humans are gardens to tend – not machines to fix.”

Professor Sir Al Aynsley-Green, the first National Clinical Director for Children in Government and former Children’s Commissioner for England, and past president of the British Medical Association, suggests that we as a society need a “momentum for compassion”. Struck by the extremes of compassion witnessed during his wife’s treatment in the last years of her life, Sir Al wants to see a cultural transformation in healthcare: for compassion to be a key operating principle in NHS and care settings, led by the Chief Nurse’s Office; for every organisation to promote the importance of compassion at the professional level; for the views of patients and families to be sought regularly; for much earlier and better focus on compassion in undergraduate and postgraduate teaching programmes for all staff; for compassion to be inspected against by the Care Quality Commission; and for a willingness to encourage staff at all levels to expose poor practice as well as celebrating excellent care.

Compassion, he emphasises, is more than empathy - and way "less fluffy" but much more measurable than kindness. “It’s putting yourself into somebody else’s shoes – and doing something about it.” Recently appointed the UK’s first Visiting Professor in Compassionate Care at Northampton University, at the age of 80, Sir Al certainly is doing something about it. He has made it his new purpose in life to “embed compassion into every aspect of care”.

Like Sir Al, Queen Elizabeth II, the UK’s longest serving monarch, espoused compassion, in word and deed. Living a life of compassionate service, the Queen made clear that her Christian faith was her guiding principle. She speaks of Jesus Christ as ‘an inspiration,’ a ‘role model’ and ‘an anchor’. “Many will have been inspired by Jesus’ simple but powerful teaching,” she said in her Christmas Broadcast, 2000. “Love God and love thy neighbour as thyself – in other words, treat others as you would like them to treat you. His great emphasis was to give spirituality a practical purpose.”

When nurses do unto others as they would have done unto themselves, and act as role model to colleagues, not only do patient experiences of care and their outcomes improve – but so does job satisfaction for nurses: a critical factor in nurse recruitment and retention – the biggest workforce challenge faced by healthcare organisations. Across the UK, there are currently more than 40,000 nursing vacancies, and thousands of burnt-out nurses are leaving the profession early. Whether nurses decide to stay or go is driven in part by their daily experience at work. The late Kate Granger, Consultant in medicine for older people, inspired Compassionate Care Awards in her name, envisioning that such a legacy would drive up standards in care - and surely also help retain nurses, through restoring a sense of pride, achievement and fulfilment to the nursing workforce.

Article

Culture

26 March 2024

10 min read

We’ll learn to live with AI: here’s how

AI might just help us with life’s dilemmas, if we are responsible.

Andrew Briggs

Andrew is Emeritus Professor of Nanomaterials at the University of Oxford.

Two construction workers stand and talk with a humanoid AI colleague.

Nick Jones/Midjourney.ai

Anxiety about algorithms is nothing new. Back in 2020, It was a bad summer for the public image of algorithms. ‘I am afraid your grades were almost derailed by a mutant algorithm’, the then Prime Minister told pupils at a school. No topic in higher education is more sensitive than who gets a place at which university, and the thought that unfair decisions might be based on an errant algorithm caused understandable consternation. That algorithms have been used for many decades with widespread acceptance for coping with examination issues ranging from individual ill health to study of the wrong set text by a whole school seems quietly to have slipped under the radar.

Algorithmic decision-making is not new. Go back thousands of years to Hebrew Deuteronomic law: if a man had sex with a woman who was engaged to be married to another man, then this was unconditionally a capital offence for the man. But for the woman it depended on the circumstances. If it occurred in a city, then she would be regarded as culpable, on the grounds that she should have screamed for help. But if it occurred in the open country, then she was presumed innocent, since however loudly she might have cried out there would have been no one to hear her. This is a kind of algorithmic justice: IF in city THEN woman guilty ELSE woman not guilty.

Artificial intelligence is undergoing a transition from classification to decision-making. Broad artificial intelligence, or artificial general intelligence (AGI), in which the machines set their own goals, is the subject of gripping movies and philosophical analysis. Experts disagree about whether or when AGI will be achieved. Narrow artificial intelligence (AI) is with us now, in the form of machine learning. Where previously computers were programmed to perform a task, now they are programmed to learn to perform a task.

We use machine learning in my laboratory in Oxford. We undertake research on solid state devices for quantum technologies such as quantum computing. We cool a device to 1/50 of a degree above absolute zero, which is colder than anywhere in the universe that we know of outside a laboratory, and put one electron into each region, which may be only 1/1000 the diameter of a hair on your head. We then have to tune up the very delicate quantum states. Even for an experienced researcher this can take several hours. Our ‘machine’ has learned how to tune our quantum devices in less than 10 minutes.

Students in the laboratory are now very reluctant to tune devices by hand. It is as if all your life you have been washing your shirts in the bathtub with a bar of soap. It may be tedious, but it is the only way to get your shirts clean, and you do it as cheerfully as you can … until one day you acquire a washing machine, so that all you have to do is put in the shirts and some detergent, shut the door and press the switch. You come back two hours later, and your shirts are clean. You never want to go back to washing them in the bathtub with a bar of soap. And no one wants to go back to doing experiments without the machine. In my laboratory the machine decides what the next measurement will be.

Suppose that a machine came to know my preferences better than I can articulate them myself. The best professionals can already do this in their areas of expertise, and good friends sometimes seem to know us better than we know ourselves.

Many tasks previously reserved for humans are now done by machine learning. Passport control at international airports uses machine learning for passport recognition. An experienced immigration officer who examines one passport per minute might have seen four million faces by the end of their career. The machines were trained on fifty million faces before they were put into service. No wonder they do well.

Extraordinary benefits are being seen in health care. There is now a growing number of diagnostic studies in which the machines outperform humans, for example, in screening ultrasound scans or radiographs. Which would you rather be diagnosed by? An established human radiologist, or a machine with demonstrated superior performance? To put it another way, would you want to be diagnosed by a machine that knew less than your doctor? Answer: ‘No!’ Well then, would you want to be diagnosed by a doctor who knew less than the machine? That’s more difficult. Perhaps the question needs to be changed. Would you prefer to be treated by a doctor without machine learning or by a doctor making wise use of machine learning?

If we want humans to be involved in decisions involving our health, how much more in decisions involving our liberty. But are humans completely reliable and consistent? A peer-reviewed study suggested that the probability of a favourable parole decision depended on whether the judges had had their lunch. The very fact that appeals are sometimes successful provides empirical evidence that law, like any other human endeavour, involves uncertainty and fallibility. When it became apparent that in the UK there was inconsistency in sentencing for similar offences, in what the press called a postcode lottery, the Sentencing Council for England and Wales was established to promote greater transparency and consistency in sentencing. The code sets out factors which judges must consider in passing sentence, and ranges of tariffs for different kinds of crimes. If you like, it is another step in algorithmic sentencing. Would you want a machine that is less consistent than a judge to pass sentence? See the sequence of questions above about a doctor.

We may consider that judicial sentencing has a special case for human involvement because it involves restricting an individual’s freedom. What about democracy? How should citizens decide how to vote when given the opportunity? Voter A may prioritise public services, and she may seek to identify the party (if the choices are between well identified parties) which will best promote education, health, law and order, and other services which she values. She may also have a concern for the poor and favour redistributive taxation. Voter B may have different priorities and seek simply to vote for the party which in his judgement will leave him best off. Other factors may come into play, such as the perceived trustworthiness of an individual candidate, or their ability to evoke empathy from fellow citizens.

This kind of dilemma is something machines can help with, because they are good at multi-objective optimisation. A semiconductor industry might want chips that are as small as possible, and as fast as possible, and consume as little power as possible, and are as reliable as possible, and as cheap to manufacture as possible, but these requirements are in tension with one another. Techniques are becoming available to enable machines to make optimal decisions in such situations, and they may be better at them than humans. Suppose that a machine came to know my preferences better than I can articulate them myself. The best professionals can already do this in their areas of expertise, and good friends sometimes seem to know us better than we know ourselves. Suppose also that the machine was better than me at analysing which candidate if elected would be more likely to deliver the optimal combination of my preferences. Might there be something to be said for benefitting from that guidance?

If we get it right, the technologies of the machine learning age will provide new opportunities for Homo fidelis to promote human flourishing at its best.

By this point you may be sucking air through your intellectual teeth. You may be increasingly alarmed about machines taking decisions that should be reserved for humans. What are the sources of such unease? One may be that, at least in deep neural networks, the decisions that machines make may be only as good as the data on which they have been trained. If a machine has learned from data in which black people have an above average rate of recidivism, then black people may be disadvantaged in parole decisions taken by the machine. But this is not an area in which humans are perfect; that is why we have hidden bias training. In the era of Black Lives Matter we scarcely need reminding that humans are not immune to prejudice.

Another source of unease may be the use to which machine learning is put for commercial and political ends. If you think that machine learning is not already being applied to you, you are probably mistaken. Almost every time you do an online search or use social media, the big data companies are harvesting your data exhaust for their own ends. Even if your phone calls and emails are secure, they still generate metadata. European legislation is better than most, and the Online Safety Act 2023 will make the use of Internet services safer for individuals in the United Kingdom. But there is a limit to what regulation can protect, and 2024 is likely to see machine learning powerfully deployed to sway voters in elections in half the world. Targeted persuasion predates AI, as Othello’s Iago knew, but machine learning has brought it to an unprecedented level of industrialisation, with some of the best minds in the world paid some of the highest salaries in the world to maximise the user’s screen time and the personalisation of commercial and political influence.

Need it be so? In some ways advances in machine learning are acting as the canary in the mine, alerting us to fundamental questions about what humans are for, and what it means to be human. The old model of Homo economicus—rational, selfish, greedy, lazy man—has passed its sell-by date. It is being replaced by what I like to call Homo fidelis—ethical, caring, generous, energetic woman and man. For as long as AGI remains science fiction, it is up to humans to determine what values the machines are to implement. If we get it right, the technologies of the machine learning age will provide new opportunities for Homo fidelis to promote human flourishing at its best.

Whatever the future capabilities of machines, they cannot be morally load-bearing because humans are self-aware and mortal, whereas machines are not.

Paul Collier and John Kay

Christians have been thinking about what it means to be human for two millennia, building on what came before, and so they ought to have something to contribute to how humans flourish. In It Keeps Me Seeking, my co-authors and I ask our readers to imagine that they were writing about three thousand years ago for people who knew nothing of modern genetics or psychological science about what it means to be human. ‘You are writing for a storytelling culture, and so you would probably put it in the form of a story. Let’s say you set it in a garden. The garden is pleasant, but it is also designed for character formation, and so there is work to do, and also the possibility for a hard moral choice. You want to convey that humans need social interactions (for the same reason that solitary confinement is a severe punishment), and so you try the literary thought experiment of having one solitary man and letting him encounter animals and name them. Animals can be useful and they can be good company. But ultimately no animals, not even a dog, are fully satisfactory as partners in work and companions in life. Humans need humans. An enriching component of human relationships is sex. So, the supreme gift to the solitary man in our story is companionship with an equal who is both like and unlike; a woman. It is hardly a complete account, but it is a good start. Oh, and there is one other aspect. They should be free of the shame which lies at the root of so much psychological disorder.’

As far as it goes, would you regard such an account as complete? If not, what would you add next? You can see where this is going. To be human you need to be responsible. So, you let the humans face the moral choice. You can even include an element of disinformation to make the choice harder. And then when it goes horribly wrong you let them discover that they are responsible for their actions, and that blaming one another does not help. If you have God in your story, then (uniquely for the humans) responsibility consists of accountability to God. This is how human distinctiveness was addressed in early Jewish thought. As an early articulation that to be human means to be responsible, the story of Adam and Eve is unsurpassed.

In Greed is Dead, Paul Collier and John Kay reference Citizenship in a Networked Age as brilliantly elucidating the issue of morally pertinent decision-taking. They write, ‘Whatever the future capabilities of machines, they cannot be morally load-bearing because humans are self-aware and mortal, whereas machines are not. Machines can be used not only to complement and enhance human decision-making, but for bad: search optimisation has already morphed into influence-optimisation. We must keep morally pertinent decision-taking firmly in the domain of humanity.’

The nature of humanity includes responsibility—for wise use of machine learning and much more besides. Accountability is part of life for people with widely differing philosophical, ethical, and religious world views. If we are willing to concede that accountability follows responsibility, then we should next ask, ‘Accountable to whom?’