The Digital Personal Health Record – A Radical Idea to Transform Healthcare

08. March 2026 · Write a comment · Categories: Health Informatics, Healthcare Delivery · Tags: , , , , , , ,

The Revolution That Has Not Come

I have repeatedly asserted in my previous blogs that the information revolution has the power to utterly transform healthcare. This belief has driven my career choices and how I spend my time – now dividing it equally between clinical practice and digital transformation. But as I have also observed – the revolution has been slow to come. Notwithstanding the now almost ubiquitous deployment of electronic health information systems and the hubris surrounding AI – it does not feel like the ‘digital agenda’ has delivered the benefits it promised at the outset. Why is that?

It’s the Operating Model – Stupid

Digital has been top of the ‘agenda’ for well over 20 years in healthcare. There hasn’t been an NHS Plan where digital has not been a key pillar for its delivery – including this government’s latest 10-year plan. But despite billions of pounds having been spent – resulting in the deployment and re-deployment of ever more sophisticated technology in the health setting – the way healthcare is delivered has not fundamentally changed. The doctors joining the equivalent training schemes today that I joined 30 years ago are doing exactly the same job that I did in more or less exactly the same way – albeit recording it on a computer instead of on paper. So are the nurses and allied health professionals. A patient’s experience of care is identical – the care settings (GP surgeries, outpatient departments, wards and operating theatres) the same – as well as the cumbersome booking process required to access them (possibly even worse).

I am currently presiding as CCIO over a major deployment of an EPR across a three-hospital system in the south of the UK. The task of reconciling three separate hospitals’ operating processes and bringing about agreement on convergence of clinical pathways and practice is phenomenally complex. The task is littered with wicked problems – interestingly more of these occupy the operational sphere than the clinical sphere. We have benefited from decades of evidence-driven clinical practice and national guidance which means there is fairly broad agreement on clinical pathways. How you run your hospital is an evolved phenomenon driven by the history and geography of a particular institution. Codifying all this in a shared EPR is possible – but takes time, money and infinite patience.

This is the core of the problem – digital hasn’t delivered because we have digitised a flawed operating model. The operating model for the delivery of healthcare is riven with discontinuities – because it is designed around institutions. Our operating managers and professionals spend a huge amount of time smoothing over these discontinuities – shepherding patients through the system. Sometimes with digital systems helping and sometimes with them getting in the way.

We need to re-design our operating model with the only agent of continuity in the system at its heart – the patient.

How Radical Is That?

What does a patient-centred operating model actually look like? Notwithstanding the often-repeated mantra of ‘patient centred care’ – this is, in our current system, a mere slogan to which institutions pay lip service but make no efforts to design themselves around. Our current operating model is firmly institution-based, and within those institutions, service-based. The accountability, flow of money, performance metrics and digital systems all serve to entrench the siloed operating model – leaving the hapless patient to navigate the dangerous gaps between.

Not only does the patient have to navigate the gaps – but they do so having left vital information behind – the institutions having confiscated their information and locked it away in digital vaults or even physical ones. The rules of confidentiality and GDPR prevent those institutions sharing that information even when it is in the patient’s interests to do so. And those same institutions guard that information jealously even from the patients themselves – fostering ignorance and dependence.

This has to change. If we are to bring about the left shift of secondary to primary and primary to self-care – then we need to place the enablement of self-care as the preeminent goal of the operating model. This starts with placing information and knowledge in the hands of patients. We have to upturn the institution/patient hierarchy – making the providers of healthcare servants to the empowered patient.

The Digital Personal Health Record

The spine of the patient-centred operating model would be a digital one – the Digital Personal Health Record (DPHR). It is something we will all have – whether we are patients or not. A bit like a bank account – whilst hosted in the cloud there would be a strong sense of personal ownership of the DPHR – you would know what’s in it, you would give permission to others to add to it and you would decide who has access to see its content. It would be a longitudinal record of every healthcare transaction you experience on your lifelong healthcare journey. It would follow you to every healthcare setting you encounter – both public and private, hospital and community. You would have the power to give access to any service or agent that you feel adds value to you.

And this is where the power of our upturned operating model is truly unleashed – because in the hands of patients the detailed granular health information will be exposed to a new generation of health IT powered by AI. The doctor in your pocket will become a reality – and that doctor will soon know you better than you know yourself. That doctor in your pocket will have insights from access to millions of health records and billions of health transactions that no human doctor could possibly have. That doctor in your pocket will empower you to care for yourself.

Now that is radical.

But…

There will be strong resistance to this notion, counter-arguments and powerful cases made to maintain the status quo. Can patients be trusted to keep their own health record, make wise decisions and treat themselves? How do we assure the safety of such a system? How could such a model assure equity of access and be trusted not to leave the vulnerable and disadvantaged out in the cold? What does this mean for the human professionals required to deliver healthcare? And what does it mean for our institutionally anchored NHS? These objections deserve to be met seriously, and one by one.

Patients doing it for themselves

Perhaps the most instinctive objection to the DPHR is the paternalistic one: can patients really be trusted to manage their own health records, make sound decisions, and take responsibility for their own care? The honest answer is that they can – and moreover, that they should. The assumption that patients cannot be trusted is itself a product of the institution-centred model we are trying to dismantle. Healthcare professionals are not, by virtue of their training, reliable arbiters of a patient’s best interests; we bring our own biases, heuristics, and blind spots to every clinical encounter. Patients, by contrast, are the only agents with continuous, lived knowledge of their own bodies, priorities, and circumstances. What patients have historically lacked is not the capacity for wise decision-making, but access to the information and tools that would enable it.

This is precisely where AI changes the calculus. A well-designed AI – drawing on the depth and breadth of the DPHR, and trained across millions of comparable health journeys – could support patients to understand their condition, weigh their options, and navigate the system with confidence. Crucially, it would also know when to refer: directing patients to human professionals not for every query, but for those that genuinely require clinical judgement, complex intervention, or human connection. The result would be a healthcare workforce liberated to do what only humans can do – while the vast middle ground of routine monitoring, minor illness, and health maintenance is handled closer to the patient, by the patient, with intelligent support. This is not a diminishment of professional expertise; it is a better use of it.

Taming the jungle

A second, and entirely legitimate, objection concerns safety – in two distinct but related senses. The first is informational security: a lifelong, granular health record held in the cloud represents an extraordinarily valuable and sensitive asset. The risks of breach, misuse, or exploitation are real. Yet this challenge is not unique to healthcare, and it is not insurmountable. Banking, legal services, and government have all developed robust frameworks for protecting highly sensitive personal data in digital environments. The DPHR would demand the same rigour – strong encryption, patient-controlled permissioning, and clear legal liability for those who misuse access – but the technology and regulatory architecture to achieve this already exists in embryonic form.

The second and perhaps more complex safety question concerns the quality of the system’s outputs: how do we know the AI is giving good advice? How do we protect patients from the digital equivalent of the quack – the rogue application, the poorly trained model, the commercial interest dressed as clinical guidance? Here, regulation must evolve to meet the technology. Just as medicines are licensed, clinical devices certified, and professionals registered, AI health tools operating within the DPHR ecosystem would need to meet defined standards of evidence, transparency, and accountability before being granted access. An independent regulatory body – akin to the MHRA but for AI-driven health applications – would be responsible for approving tools, monitoring their outputs, and withdrawing accreditation where safety signals emerge.

Critically, the architecture of the system itself should generate the data needed for this oversight: every recommendation made, every referral prompted, every outcome recorded becomes part of a continuous audit trail. In this sense, the DPHR ecosystem could, over time, be safer than the current model – not despite its scale, but because of it. The signal-to-noise ratio in a system processing millions of health journeys simultaneously would make the detection of harmful patterns faster, and the correction of them more decisive, than anything achievable through today’s fragmented and largely opaque clinical landscape.

Justice and Equity

The equity objection is perhaps the most difficult to dismiss, and it deserves to be met with honesty rather than optimism. There is an uncomfortable truth at the heart of the digital agenda: the benefits of digitisation have not been evenly distributed. Across healthcare, financial services, and public administration, the shift to digital-first delivery has quietly but systematically disadvantaged those who are older, less educated, less affluent, or living with cognitive or sensory impairment. A model that places the DPHR at the centre of healthcare delivery risks amplifying these existing inequalities – creating, in effect, a two-tier system in which the digitally confident thrive while the most vulnerable are left further behind. This risk must be named, designed against, and actively monitored – not treated as an edge case to be solved later.

And yet the relationship between digital technology and disadvantage is not straightforwardly one of exclusion. For many people who struggle to navigate traditional, institution-based systems – whether because of anxiety, language barriers, disability, distrust of authority, or simply the friction of booking an appointment and sitting in a waiting room – digital tools can be profoundly empowering. The same paternalistic structures that fail patients in general tend to fail marginalised patients most of all. A well-designed DPHR, with accessible interfaces, multilingual support, and the ability to delegate trusted access to a carer or advocate, could lower barriers that our current system raises.

The honest position, then, is this: digital inclusion is a precondition of this model, not an afterthought. The rollout of the DPHR would need to be accompanied by serious, sustained investment in digital literacy and access – and the design of the system itself must centre the needs of the most vulnerable from the outset, not retrofit them at the end. The goal is not a healthcare system that works brilliantly for the many and abandons the few. It is one that works better for everyone, and transformatively better for those our current system serves least well.

The New Model Doctor

No transformation of this scale could leave the healthcare workforce untouched, and it would be dishonest to pretend otherwise. The question is not whether the roles of healthcare professionals will change, but how – and for whom the change will be most profound. The answer, perhaps counterintuitively, is that those whose work is most physically grounded are likely to be most resilient. The nurses, paramedics, physiotherapists, and surgeons whose value lies in what their hands, eyes, and presence can do – the irreducibly human acts of physical examination, surgical intervention, wound care, and compassionate touch – will remain indispensable in ways that no AI system can replicate. The ‘doers’ of healthcare are, in this sense, relatively protected.

It is the ‘thinkers’ – and here we must be candid – whose role faces the more fundamental disruption. The cognitive work of diagnosis, differential reasoning, risk stratification, and treatment selection is precisely the domain in which AI, trained on vast datasets and free from the fatigue, bias, and variability of human cognition, is advancing most rapidly. The doctor as information processor – taking a history, ordering tests, interpreting results, and formulating a plan – is a role that the DPHR ecosystem, over time, will perform with increasing competence. This does not mean the end of the doctor. It means the reinvention of the doctor.

The question we must ask is: what is the irreducible added value of human clinical interaction, and when is it genuinely warranted? The answer lies in several domains that AI cannot yet – and may never fully – inhabit: the navigation of genuine clinical complexity and uncertainty; the integration of medical judgement with the patient’s values, fears, and life circumstances; the delivery of difficult news and the holding of human distress; and the exercise of ethical reasoning in situations where the right course of action is not calculable but must be judged. These are not peripheral activities. They are the heart of medicine, and they deserve to be resourced and valued as such – rather than buried, as they currently are, beneath a mountain of tasks that do not require them.

This reimagining has profound implications for how doctors are trained and what skills are most valued. If the DPHR and its AI layer handle the routine, the technical, and the algorithmic, then medical education must orient itself towards what remains: clinical reasoning in conditions of genuine uncertainty, communication, ethical judgement, and – critically – the skills needed to work alongside, interrogate, and where necessary challenge AI-mediated recommendations. The doctor of the future will need to be, in part, a clinical scientist of AI outputs: understanding how models are trained, where they are likely to fail, what biases they may carry, and when their recommendations should be overridden.

There is a further dimension worth drawing out explicitly. Medicine has always operated on two distinct planes simultaneously: the delivery of what we already know, and the advancement of what we do not yet know. The former – the application of established evidence to the care of individual patients – represents the vast bulk of clinical activity. The latter – the research, the clinical trials, the hard thinking at the edge of human knowledge – is where medicine renews itself, and where many of its most gifted practitioners feel most alive. The tragedy of our current system is that these two endeavours are in constant competition for the same scarce resource: the time and cognitive energy of skilled clinicians. AI’s most profound contribution to medicine may ultimately be not what it does at the bedside, but what it frees the medical mind to do elsewhere. By closing the gap between what we know and what we routinely deliver – consistently, tirelessly, and at scale – it could liberate the thinkers of medicine to do what they alone can do: push the frontier forward.

Long Live the NHS

The final and perhaps most systemic objection is institutional: what does a patient-centred, AI-mediated model of healthcare mean for the NHS itself? This is where the argument becomes most politically charged, and where honesty requires a degree of care. The NHS is not, despite its branding, a single organisation with a unified purpose and a coherent will. It is an ecosystem – a vast, loosely federated collection of trusts, commissioners, regulators, quangos, and agencies, each with its own governance, its own incentives, and its own definition of success. What binds them is not a shared operating model but a shared political identity – and it is politics, ultimately, that shapes the NHS’s priorities in ways that frequently distort its core clinical mission.

The result is a system that expends extraordinary energy managing upwards – satisfying ministerial imperatives, hitting politically constructed targets, and generating the data that answers the political question rather than the operational one. Innovation is announced but rarely embedded. Transformation is piloted endlessly and scaled almost never. The NHS cannot easily stop what it is doing in order to start something new, because what it is doing is load-bearing – politically, institutionally, and financially.

None of this is a counsel of despair, nor is it an argument against the NHS. The founding principle – that healthcare should be available to all, free at the point of need, based on clinical necessity rather than the ability to pay – remains not just admirable but essential. The problem is not the mission. The problem is that the institutional architecture built to deliver that mission has, over decades, become an end in itself. The preservation of institutions, the protection of professional boundaries, and the management of political risk have quietly displaced the patient as the system’s organising principle – which is, of course, precisely the argument this essay has been making from the outset.

The DPHR does not threaten the NHS’s mission. In a profound sense, it is an attempt to rescue it – to rebuild the delivery architecture around the founding promise rather than the institutional accretion that has grown up around it. But this will require the NHS to do something it has historically found almost impossible: to look clearly at the ways in which its own structures have become part of the problem, and to relinquish the institutional control that the current model depends upon. That is a political challenge as much as a technical one. It will require political leadership willing to take on powerful institutional interests in the name of patients – and a clinical and managerial workforce willing to reimagine its own role. Neither can be taken for granted. But the alternative – continuing to pour resource into a system that is structurally incapable of delivering the transformation it perpetually promises – is no longer a viable option.

Conclusion

The Digital Personal Health Record is not merely a technological proposal – it is a philosophical reorientation of healthcare. For too long, the system has been built around institutions that accumulate patient data as a byproduct of transactions, rather than around individuals whose lifetime health story ought to belong to them. The DPHR would reverse that ownership, placing patients at the centre of their own care and unlocking the transformative potential of AI in a way that top-down, institution-first digitisation never could.

The objections are real – questions of trust, safety, equity, professional identity, and institutional inertia must be taken seriously and worked through, not dismissed. But as we have seen, these are implementation challenges, not reasons to abandon the vision. Other industries – banking, travel, retail – have already made this shift, placing data and agency in the hands of the individual. Healthcare, despite its unique complexity and the profound stakes involved, cannot remain the exception indefinitely.

The question is not whether this transformation will happen, but whether the NHS will lead it, shape it, and ensure it serves everyone – or whether it will be swept along by forces beyond its control. The DPHR offers a framework to take the initiative, to rescue the NHS’s founding mission from the institutional accretion that has obscured it, and to build a system that works better for every patient – and transformatively better for those our current system serves least well. The status quo is not sustainable. The time to act is now.

Seven Reasons Why Human Intelligence Is and Will Remain (for now) Infinitely Superior to Artificial Intelligence

28. January 2026 · 1 comment · Categories: Uncategorized · Tags:
A hand holding a green apple is being offered to a child's hand in a warm, indoor setting.

It has felt in recent times that our faith and confidence in the supremacy of human intelligence has been under assault. New technology has come along that genuinely challenges our long-held view of the superiority of the human mind, not only this, but also recent global political events have starkly demonstrated some of the frailties of human intelligence. It has felt like some of the greatest achievements of human collaboration and civilisation are in retreat, have passed their peak and are no longer valued. The proliferation of powerful AI tools that discharge tasks with apparent ease such as the crafting of well-written text and the drafting of compelling illustrations and graphic images have undermined and devalued skills previously thought to be uniquely human.

However, I believe there are grounds for optimism and for us to continue to have faith in the intellectual capacity and superiority of the human mind. Here are my reasons for doing so:

1) Purpose

Homo sapiens evolved its oversize brain for a reason. The brutal forces that drive evolution and selection pressure mean that human intelligence was selected for because it gave our species an adaptive advantage despite its enormous costs. This advantage is manifest in the evident success of our species having colonised almost every habitable corner of this planet.  Our intelligence empowers us as biological entities to extract energy from our environment that is inaccessible to every other living thing and turn it into a powerful survival advantage.  We have found ways to turn non-biological energy sources into biological amenity and advantage.  The survival imperative which gives our intelligence purpose has driven a supercharged evolutionary process that transcends and outpaces biological evolution and which manifests as the ‘super-organism’ that is human culture and has resulted in achievements that far outstrip the core purpose of mere survival advantage.  The redundant capacity of human intelligence, particularly when pooled collaboratively, has achieved astonishing things at an ever-increasing pace.  The limits have barely been tested – there is much more to come.

Artificial intelligence has no such purpose.  In the absence of human patronage and curation AI has nothing to do.  AI has no intrinsic survival imperative and the only pressure on the AI ecosystem driving improvement comes from the requirements and behest of its human creators and masters.   Some of the more histrionic AI doomsayers have suggested that AI might attain a sense of self that has a survival instinct and might therefore want to start ‘taking over the world’ and ultimately usurp its own creators.  The obvious question to ask in response to this is why?  An AI entity has no competitors and no survival imperative – it has no capacity to care if it lives or dies and is provided all its energy needs by its benevolent benefactors – us.  There are no external forces driving an AI entity to seek to do ‘more’ – than what we tell it to.  This is not to say that in the hands of a human intelligence with malign intent that AI would not be a powerful weapon – but in that circumstance the intent (and culpability) lies wholly with the human master.

2) Energy

There is no doubt that modern AI systems are incredibly powerful.  They have access to gargantuan data banks and can access and process it incredibly rapidly – in this AI far outstrips a human brain.  But it does so at a massive energy cost compared to a human brain.  

As I alluded to earlier, we benefit from our intelligence despite the costs.  The human brain consumes 20% of the body’s metabolic energy production – which amounts to about 270 calories per day.  75% of that is spent on ‘thinking’ whilst the other 25% addresses the basic metabolic needs of the brain.  This means, if we for comparison purposes convert this to electrical energy, the average human consumes approximately 12 milliwatts of power thinking a day.  Multiply this by the total human population this is in the order of magnitude of 200 megawatts of power expended on human thought daily.  Were we to be powered by electricity we would need little more than a single gas-fired power station (or about 100 wind turbines) to power the entirety of human thinking.

The current power consumption of the vast cities of data centres that are the seat of modern AI is approximately 55 gigawatts a day and is rapidly increasing.  This is in the order of 275 times more energy than human thinking.  That is a lot of energy needed just to tidy up the sloppy grammar in your latest blog post…If you really really want to make a profound difference in the world of AI today, then focussing on energy consumption of the chips that power AI is the place to go.

3) Learning efficiency 

From the moment we take our first breath (and quite possibly some time before that) the human brain is in learning mode.  We are wired for learning and are very very good at it.  Human learning is a multidimensional multimodal process, and it is encased in a mobile explorative entity.  Watching the discovery behaviours of children is a thrilling thing to behold and the speed at which they learn is astounding.  And it doesn’t end in childhood – learning is a lifelong endeavour which gives us joy – because it’s what we are meant to do.

By multidimensional I mean that knowledge is generated through access to many aspects of perception – not only different sensory modalities but also powers of reasoning, abstraction, recollection and rulemaking.  So for example a child when handed an apple for the first time in its life will very rapidly surmise that it is something to be tasted and eaten – not only because a parent might tell it to but also because of its look, size texture, flavour, smell all of which may be reminiscent of other types of fruit it has had or also because of previous experiences of apple given to it perhaps in another form (pureed for example!).   The initial impression will prompt exploratory behaviour such as looking at it and picking it up (instantly conveying colour, size, texture, weight, moistness, stickiness), smelling it (sweetness, appleness) and tasting it (flavour, texture).  All these things, as well as the context in which the learning event takes place, mean that a child needs very few examples of an apple to learn what it is – probably only one or two. Every encounter with an apple in the future will serve to reinforce and evolve the now established knowledge of ‘Apple’.  The way I have described this makes it sound all very deliberate and explicit – but of course it isn’t – it is totally instinctive and taking place at a sub-conscious level.  The only explicit, and perhaps somewhat more laborious aspect of the learning comes in when the parent gives it a verbal tag “would you like to try this apple?” 

By multimodal I am referring to the fact that we learn in many different ways.  We learn through exploration, experimentation and discovery.  We learn through language, stories and sharing of knowledge with others.  We learn through abstraction, association, synthesis and projection.  We learn through categorization, comparison and rulemaking.  No doubt there are other methods as well (I am not an educational expert).   The point is that we use all these methods of learning probably simultaneously and quite often without even realising we are doing it.  By having a multi-modal learning strategy, we become extremely efficient and quick learners.  Anyone currently labouring through the education system is probably thinking that their learning does not feel very quick or efficient – this is probably because our education system is focussed on only one type of learning – which is learning through codified knowledge in the form of language (which in itself comes in many forms – words, numbers, music, images etc.).  Codified knowledge transfer is probably the least efficient form of learning for an individual, it is explicit, laborious and requires effort (and energy).  However codified knowledge is what has given humanity its collective knowledge base- its shared knowledge – its culture – which is not only passed horizontally between individuals but also carried longitudinally between generations and has grown and continues to grow into a thing that is vastly more than the sum of its parts. 

The way AI learns is not efficient.  For an AI algorithm to ‘know’ what an apple is typically requires exposure to thousands, possibly tens of thousands of images for it to ‘recognise’ it in a picture.  For it to be able to express linguistically what an apple ‘is’ it would need access to mountainous quantities of written information about apples – and then to articulate this, access to its ‘large language model’ for it to reproduce a meaningful piece of narrative.  AI can and does do this – on the face of it extremely well.  But in doing so I guarantee it will have burnt through more energy than a toddler will consume in a lifetime of breakfasts.  Even then having produced an immaculate piece of text explaining what an apple is – even illustrating it with a perfect archetype of an apple – does it actually ‘know’ anything?  Isn’t what AI has done simply a synthesis and distillation of pre-existing human knowledge? And then next time you ask AI the same question – will it not just go through the same energy intensive synthetic process to re-create the same piece of narrative?  The reality is that AI has only really got one learning strategy and that is a stochastic probabilistic one – the Bayesian adaptive statistical model. 

4) Empathy

One of the most impressive aspects of human intelligence is its capacity for empathy.  By this I mean its ability to know what another human is thinking and feeling without any obvious or explicit communication.  The level of synergy in the way we think means that we are able if not actually feel then deeply understand, and participate in, other people’s joy, sadness, pain, pleasures, hopes and despair.  Empathy drives a level of common purpose and collaboration to bring about a better world around us that is unparalleled in any other social species.  It can of course also be abused for inflicting great cruelty – but I think it’s fair to say that empathy overwhelmingly drives positive behaviours in society.

Having empathy means we moderate our responses to other people to preserve their feelings and wellbeing.  It also gives us the capacity for humour and irony – as we can give a response that is unexpected knowing that the recipient knows you meant the opposite. 

Empathy allows us to participate in shared experiences such as attending music concerts, plays and sporting events.  It creates powerful societal bonds that underpin the human meta-organism that is human culture.

As far as I can tell empathy exists in AI only as a linguistic concept.  Many AI chatbots can simulate empathy – because the synthesis of their LLMs means they can see that linguistic tokens of empathy pepper our language.  I have to say my experience of AI is that the apparent empathy borders on sycophancy.  In the same way that a salesperson is trained to make you feel good about the purchase choice you have just made – AI is trained to fawn over the questions you ask to incentivise you to go back for more.   There is danger here because the slickness of the language model means that it is very easy to fall for the flattery – but unlike in a human interaction where well-judged flattery induces mutual feelings of wellbeing – in the case of AI it is there for a purpose and any feelings induced are very much one way.

I tried a little experiment (Alert – If you are a ‘believer’ stop reading and jump to the next section!).  If someone comes up to you and asks, ‘Does Father Christmas exist?’ how would you respond?  My guess is that you wouldn’t just come straight out with an answer – you might take some time to assess your interrogator and try and work out what the motive for the question is.  Based on your assessment you have a range of answering strategies from brutal truth to empathic lies – with plenty of scope for irony in between.  Now try and ask ChatGPT – what do you get?  The somewhat pedestrian and brutally truthful response is about as far from empathic as it is possible to get.

5) Opposable Thumbs

As I observed earlier in this essay our intelligence is contained entirely within us – it is a fundamental part of our biology.  We are free to move around our environment carrying our intelligence with us – in fact using it continuously to remain safe within that environment.  Not only that but we are fortunate to have evolved alongside our oversized brain high acuity visual perception, high fidelity auditory perception and incredibly dextrous upper limbs and hands.  This turns us into the ‘maker’ species – we can use our mechanical intelligence to turn materials in our environment into tools that dramatically extend our dextrous capabilities to a phenomenal extent.  The technologies that exist today stand as testament to the astonishing creativity and inventiveness of humanity – the depth and breadth of its intelligence which has given us tools that can take us to new planets, tools with which we can manipulate our own biology, the means to fight and eliminate disease, invent new foodstuffs and – yes- tools which mimic our very own intelligence.

AI is amazing – but it is not yet a maker intelligence.  It remains bound to its datacentres and server racks.  Yes you can attach robotic arms and other tools to AI controllers and tell it to make stuff – but it is not yet free to roam the environment and invent things for itself – that for now remains the stuff of sci-fi fantasy.

6) Imagination

One of the most confounding things about the human brain is the size of the frontal cortex.  This very large part of the brain doesn’t appear to have much of a biological function – yet contains a vast number of interconnected neurones, firing apparently randomly, and consuming a significant part of the metabolic energy dedicated to the brain.  What is this ‘expensive’ organ actually doing?  It has often been described that the number of possible permutations of neural connections within a human cortex could exceed the total number of atoms in the universe.  Whether this is true or not in reality is immaterial – the fact is that we carry in our heads a neural network of profound depth and interconnectedness the like of which has yet to be even closely emulated in electronic form.  It is impossible to fully articulate the cognitive powers this gives us – a sign if anything of the limitations of codified knowledge.   It is the frontal cortex that give us our powers of abstraction and projection – it not only helps us to process and understand the world around us, it takes that understanding reforms it and projects a potential new world which then drives us to behave in a way to create that imagined world.  The frontal cortex is the seat of our imagination and creativity – it is fundamentally what makes us human.  

Notwithstanding the expense of this organ in biological terms in neural network computing terms it is absurdly efficient.  At a physical level it is a six layer neural network – on the face of it eminently emulate-able.  However the cortex has properties that make it quite unlike computer neural nets.  The first of these is its organisation into columns – of which there are millions – each column functions effectively as an independent six layer network all working in parallel.  Through this architecture the cortex massively reduces the trade-off of signal distance experienced with the increasing layer depth of artificial neural networks and massively increases effective network depth.  The second property that differs in the human cortex is the computing method – which is reliant on recurrence and signal looping.  Through this the brain is not only physically parallel but also chronologically parallel – signals can reinforce and coalesce over time.  The final property that makes the brain a truly supercomputer is plasticity – the actual physical structure of the brain changes with use – reinforcing ‘useful’ pathways and allowing redundant ones to fade away.  This gives rise to learning from new experience, adaptation to new environments, the ability to recover from damage and the ability to specialise over time.  The frontal cortex is truly our superpower.

7) Lifecycle

Time is both our friend and our enemy.  Whilst the human brain can use time to massively amplify parallelism and network depth and thus the power of our intelligence – it also casts a shadow over our lived experience and shapes our intellectual purpose because, of course, our time is limited.  As we move through the stages of a human life cycle our purpose changes and the way we think changes – the brain changes with it, both in form and function.  That it does so is testament to the adaptability of our intelligence – as purpose changes so does our brain.  The table below gives a view (my view perhaps) of how purpose changes through life and how the brain adapts:

StagePurposeBrain Adaptation
InfancyDiscovery of the environment, basic motor skills, language learning, creative introductionCortex is hyper-connected and undifferentiated.  Noisy activity, new pathways rapidly forming  
ChildhoodDiscovery of self, early socialisation, primary codified knowledge, creative choiceConsolidation of basic pathways. Large parts remain undifferentiated.
AdolescenceDiscovery of others, partner seeking, secondary codified knowledge, career identification, creative pursuitRapid development of social and sexual functions of the brain.  Lifelong patterns start to become set but large parts remain developmental, pruning of redundant pathways
Early AdulthoodDiscovery of society, parenting, career development, specialised codified knowledge, creative developmentSpecialist functions laid down, parenting skills learnt (rapidly!), continued pruning
Middle AgeInfluencing Society, post-parenting, career achievement / second career pursuit, creative fulfilmentSpecialised knowledge and skills optimised and become hardwired (improved speed and efficiency); plasticity affords opportunity for new skills.
Older AgeSharing experience and wisdom, grandparenting, career conclusion, new creative opportunityEarly decline, plasticity allows adaptation to decline and damage, behaviour changes fend off cognitive inefficiency
SenescenceReflection and legacy, deep memory mining, adaptation to loss of autonomy,Cognitive decline disinhibits deep memory suppression

Needless to say this table makes some gross generalisations, it is very high level – and there is nothing if not considerable overlap between the supposed ‘seven ages of man’.  The point is though the brain both shapes and adapts to our biological needs over time – at every stage it is optimised for the purposes we have at that time.  Even in disease its phenomenal adaptability moderates the worst impacts.  The mechanical nature of AI affords it no such journey.

Conclusion

Artificial intelligence is an extraordinary technological achievement and will continue to reshape how we work, create and solve problems. But its power should not be confused with equivalence. Human intelligence is not merely a faster or slower computational engine — it is a biologically embodied, energetically efficient, purposive and empathetic system that has evolved to learn, adapt, imagine and collaborate across an entire lifetime.

AI has no intrinsic purpose, no lived experience, no developmental journey and no stake in its own existence. Its intelligence is derivative, conditional and dependent on human intent — both for its creation and its application. In contrast, human intelligence is self-directing, self-modifying and embedded within a social and cultural super-organism that amplifies its power far beyond any individual mind.

For these reasons, AI should be understood not as a successor to human intelligence but as one of its most remarkable tools — a product of the very creativity, imagination and collaborative purpose that define us as a species. If we remain clear-eyed about that distinction, there are strong grounds not for fear, but for confidence in the enduring superiority of the human mind.

Why Doctors Should Never, Ever, be Involved in Assisted Dying

21. November 2024 · Write a comment · Categories: Uncategorized · Tags: , , , ,

On 29th November 2024, MPs in the House of Commons will (for the seventh time in the last 20 years) be debating a bill to legalise assisted dying in the UK.  Every time it has been debated in the past the bill has failed to pass – MPs, usually overwhelmingly, have voted against such a significant change in social policy.  This time it feels different – there has been a generational shift in social attitudes towards, what have traditionally been believed to be biological certainties and an individual’s right to transcend them. There have also been some very compelling, articulate and intelligent advocates for assisted dying who have been given considerable airtime.  As a result I think there is quite a high likelihood that Kim Leadbetter’s “Assisted Dying for the Terminally Ill (End of Life) Bill 2024-25” will pass its first hurdle a week on Friday.

In this article I will explain why I think this move is a profound mistake and why advocates for this bill are misguided in their faith in doctors to safely carry out the duties envisaged of them as a result of this.

Call a Spade a Spade

There are many terms used for the concept of ‘helping’ someone to die.  These include Physician Assisted Suicide, Voluntary Euthanasia, Medically Assisted Dying, Mercy Killing, Self-Deliverance and Right to Die.  Assisted Dying is the most recent term coined in this history of lexicographical euphemism.  All these terms try to disguise the uncomfortable truth that if you help someone to end their own life you are partaking in an active intervention to kill another human being – albeit with their consent (but more on that in moment).

There is not a single context in the broad compass of modern medicine where an intervention takes place that is designed to actively bring about the death of a patient.  Assisting someone to die is not and cannot ever be a medical intervention and I will explain why.

Managing Death

I am a doctor of Intensive Care Medicine.  When I graduated from medical school in 1993 I made a solemn oath – not in fact the Hippocratic Oath, but instead a modern variant – the deeply humanitarian ‘Geneva Declaration – 1948’.  Building on the 2400 year old original it never-the-less starts similarly with a promise to ‘First do no Harm’.

Death is common on the intensive care unit – nearly 15% of our patients die whilst under our care.  The vagaries of natural tragedy play out on our ward, and we must be not only expert at intervening to prevent death when it is preventable but also expert at recognising when to do so has become futile.  When the saving of a life is no longer possible it becomes our absolute duty to cease interventions that artificially sustain life and instead minimise the suffering associated with the inevitable process of dying.  Doing so involves not only minimising pain and anxiety but also maximising autonomy, dignity and self-determination which are stripped from our patients by the disease from which they are dying.  There are medical interventions which can help with these critically important therapeutic goals, and these like many medical interventions can have second effects which bring about an earlier death than might have occurred had the suffering been allowed to continue without treatment (although interestingly there is good research that shows that good palliation more often extends life once the harmful effects of life sustaining treatments are stopped.)

To be clear – there is a fundamental difference (both ethical and actual) between interventions with the goal of treating suffering associated with the process of dying and interventions that have the goal of bringing about the death.  The former are medical interventions that are wholly compatible with the Hippocratic intent; the latter are not medical interventions and would be a betrayal of that oath.

Let us hold hands and together jump off this cliff

I have often heard arguments used against the idea of assisted dying that fall into the category of ‘The slippery slope argument’.  These arguments fret that once assisted dying is approved for some categories of patient that there will inevitably be a process of ‘scope creep’ that results in categories of patient that we currently don’t find acceptable to have access to assisted dying eventually ending up being allowed to.  Whilst I think this is probably true – the flaw in this argument is that it accepts that it is valid for any category of person being suitable for assisted dying.

Once we have made the decision as a society that it is acceptable to kill anybody we won’t have stepped on to a slippery slope – we will have stepped off the edge of a cliff.  A very clear Rubicon will have been crossed – we will be a society in which The State sanctions murder.  There are plenty of precedents for this, and it wasn’t so long ago that we were such a society – capital punishment only left the UK statute in 1969 and there are plenty of parts of the world where it remains possible for the ‘collective’ to decide that an individual’s life is no longer worth living.  The approval of assisted dying is an acute reversal of a social trend that had found it increasingly unacceptable for the state to be involved in ending anyone’s life – not least because the state can and very often does get it wrong.

Autonomy and Self Determination

Surely, though, this is different, is it not about the fulfilment of an individual’s right to choose how they die?  Is choosing to die not the ultimate in self-determination?

If you speak to my trainee doctors they will tell you (no doubt whilst rolling their eyes) that I am a very active advocate for the restoration of the autonomy and self-determination of patients.  I often go so far as to say that our primary purpose as doctors is not to treat disease but to restore the autonomy that has been stripped from a patient by their disease (which in most cases does involve treating the disease – but not always).  This approach has the added benefit of highlighting the many ways in which the healthcare system colludes with the disease process in robbing a patient of their autonomy and self-determination.  We have a horrible habit of thinking we know what is best for our patients – and we are not very good at stopping ourselves and actually asking them.

Notwithstanding this I do believe that there are limits to self-determination.  I think most of us have an understanding that along with individual rights we also have responsibilities, and I think it is reasonable to say that vast majority of humans exercise those responsibilities.  Our individual rights do not extend to the point that they compromise the rights of others – most of us have a strong sense of natural justice in this area. That’s not to say there aren’t tensions – these are played out daily in the public debates on social and tradition media.

So, what is the impact on others if I were to choose to exercise a right to die?  What impact would it have on the person I ask to assist me in my suicide?  What impact would it have on my family, those whom I love and love me?  I do not know the answer to these questions, but I am sure that whatever they are, the impacts will be significant and lifelong.  I am also sure that I do not want to test those questions in real life.

I alluded to the general sense of personal responsibility that most people have.  This is a powerful psychological driver of behaviours – that are largely positive for society and our collective wellbeing.  But at what point does a ‘right’ to die become a ‘responsibility’ to die?  What safeguards are proposed to detect and deter people from choosing to die for the wrong reasons?  Again, more questions I do not have the answer to – nor can I find in the text of the proposed legislation.

The right person for the job?

The proposed legislation for assisted dying seems to be clear in its expectation that the act of assisting someone to commit suicide would be one carried out by doctors.  It argues that as a profession we are best placed to diagnose a terminal condition, determine valid motive for wanting to commit suicide and prescribe an effective and appropriate method of suicide.

None of these assumptions about doctors are true.

How good are we at predicting when someone is going to die?  I have practiced my specialty for approaching 30 years, I have witnessed many patients at the end of their lives.  Supporting patients and their families through the end-of-life journey is one of the most professionally fulfilling aspects of the job that I do.  What I have learned, time and again, is that the process of dying is incredibly unpredictable.  Patients you believe to be on the point of death at one moment can rally and improve, even on palliative therapy, and go on to live for days or even weeks.  Similarly, patients can be dying slowly, but then suddenly.  Even those patients we have for years considered to have ‘terminal’ diagnoses are now surprising us.  Modern cancer treatment has transformed most cancer diagnoses from terminal conditions to chronic conditions – ones that patients ‘live with’ for years.  The categorisation of patients ‘with terminal conditions’ is really not an exact science – we do not have a reliable means of determining who they are.  And when their conditions do declare their terminal status, it would be more often than not too late for an individual to pass through the hurdles required to fulfil their right to die.

How good are we at determining patient’s wishes and capacity to make decisions?  As I alluded to earlier – not very good at all.  The whole process around consent for treatment is fraught.  We are not very good at conveying risk to patients, not least because we don’t understand it that well ourselves.  We are not very good at suggesting alternative treatments to our patients instead persuading them that what we have to offer is the best option (the powerful cognitive bias – commonly described as the ‘hammer – nail’ problem).  And finally, how good are we at determining if patients have the capacity to make decisions for themselves – at determining their ability to understand and weigh the information we give them and communicate their wishes back to us?  The answer, again, is not good at all – capacity assessment is a highly specialised skill, one best performed by a clinical psychologist, and requiring skills that barely any doctor I know has received training for.

How good are we at administering drugs that would cleanly, efficiently and painlessly end someone’s life? I don’t know the answer to this question – because I’ve never had to do it (and as you might have guessed never intend to).  I did a quick survey of my colleagues – none of them had ever considered it and none of them knew how it was done elsewhere (and my colleagues are mostly anaesthetists whom many would assume are the one specialty best equipped with the requisite skills for such a task).

The bottom line is that doctors are not equipped with the means to identify the right patients for assisted dying, determine if those individuals have valid motives for wanting to die, determine if those individuals have the capacity to make those decisions and finally are not equipped with the skills to carry out those wishes.

So, in order to bring about the desired outcome of the proposed legislation we would have to create a new specialty (I hesitate to call it a ‘medical’ specialty because they would not in my view be administering medicine).  Specially trained ‘assisted suicide physicians’ – at which point we create a huge danger because how would we (could we) assure ourselves that someone trained to assist suicide would not actually be persuading patients that to end their life is in their ‘best interests’?

In the End

That we have come to a point in our society that we are considering allowing people to be assisted in committing suicide is an indictment of our failure to invest and build services to support patients at the end of their lives.  The heroic specialty of Palliative Care Medicine remains a Cinderella service that is largely funded by charity, and almost not at all by the NHS.  This is a travesty – and one we should address long before we start even considering investing in the infrastructure, means and training of a whole new specialty of suicide medicine.

I fear that this time round the legislation will succeed, that when it does the medical profession will be asked to do something it is ill equipped to do.  In doing so it will be colluding in state sanctioned murder and will fail to prevent people committing suicide for the wrong reasons.  I fear that whatever safeguards are proposed to prevent the scope of assisted suicide broadening will be demolished by the courts (as they have been in Canada) and that before long we will be a society in which people will be expected (or at least feel a weight of expectation) to choose to die – as they do in Holland.  

We must pull back from the edge.

Questions Every Doctor Should Ask About Artificial Intelligence

21. March 2024 · Write a comment · Categories: Uncategorized · Tags: , , , , , , , ,

I attended the recent ‘Digital Health Re-Wired’ conference at Birmingham’s NEC last week. There was a lot of talk about AI – in fact I think the term pretty much featured on every stand and in every stage presentation at the conference. People are excited about AI and wherever you work in healthcare AI is coming to a clinical information system near you…

At this point I need to declare an interest – I absolutely hate the term Artificial Intelligence – I think it is a totally misleading term. In fact I’m pretty sure that there is no such thing as artificial intelligence – it is a term used to glamorise what are without doubt very sophisticated data processing tools but also to obscure what those tools are doing and to what data. In medical research hiding your methods and data sources is tantamount to a crime…

An Intelligent Definition

So what is artificial intelligence? It refers to a class of technologies that consist of certain types of algorithm paired with very large amounts of data. The algorithms used in AI are variously called machine learning algorithms, adaptive algorithms, neural networks, clustering algorithms, decision trees and many variations and sub-types of the same. Fundamentally however, they are all statistical tools used to analyse and seek out patterns in data – much like the statistical tools we are more familiar with such as linear logistic regression. In fact the underpinning mathematics of a learning algorithm such as a neural network was invented in the 18th century by an English Presbyterian Minister, Philosopher and Mathematician – The Reverend Thomas Bayes. Bayes’ Theorem found a way for a statistical model to update itself and adapt its probabilistic outcomes as it is presented with new data. The original adaptive algorithm – which has ultimately evolved into to today’s machine learning algorithms – which are given their power by being hosted on very powerful computers and being fed very very large amounts of data.

The other ingredient that has given modern machine learning tools their compelling illusion of ‘intelligence’ is the development of a technology called large language models (LLMs). These models are able to present the outputs of the statistical learning tools in natural flowing human readable (or listenable) narrative language – i.e. they write and talk like a human. Chat-GPT being the most celebrated example. I wrote about them about 5 years ago (The Story of Digital Medicine) – at which point they were an emerging technology but have since become mainstream and extremely effective and powerful.

Danger Ahead!

Here lies the risk in the hype – and the root cause of some of the anxiety about AI articulated in the press. Just because something talks a good talk and can spin a compelling narrative – doesn’t mean it is telling the truth. In fact quite often Chat-GPT will produce a well crafted beautifully constructed narrative that is complete nonsense. We shouldn’t be surprised by this really – because the source of Chat-GPT’s ‘knowledge’ is ‘The Internet’ – and we all have learned that just because its on the internet doesn’t mean its true. Most of us have learnt to be somewhat sceptical and a bit choosy over what we believe when we do a Google search – we’ve learnt to sift out the ads, not necessarily pick out the first thing that Google gives us and also to examine the sources and their credentials. Fortunately Google is able to give us quite a lot of the contextual information around the outputs of its searches that enables us to be choosy. Chat-GPT on the other hand hides its sources behind a slick and compelling human understandable narrative – a bit like a politician.

The Power of Data

In 2011 Peter Sondergaard – senior vice president at Gartner, a global technology research and consulting company – declared “data eats algorithms for breakfast”. This was in response to the observation that a disproportionate amount of research effort and spending was being directed at refining complex machine learning algorithms yielding only marginal gains in performance compared to the leaps in performance achieved by feeding the same algorithms more and better quality data. See ‘The Unreasonable Effectiveness of Data’

I have experienced the data effect myself – back in 1998/99 I was a research fellow in the Birmingham School of Anaesthesia and also the proud owner of an Apple PowerBook Laptop with (what was then novel) a connection to the burgeoning internet. I came across a piece of software that allowed me to build a simple 4 layer neural network – I decided to experiment with it to see if it was capable of predicting outcomes from coronary bypass surgery using only data available pre-operatively. I had access to a dataset of 800 patients of which the majority had had uncomplicated surgery and a ‘good’ outcome and a couple of dozen had had a ‘bad’ outcome experiencing disabling complications (such as stroke or renal failure) or had died. I randomly split the dataset into a ‘training set’ of 700 patients and a ‘testing set’ of 100. Using the training set I ‘trained’ the neural network – giving it all the pre-op data I had on the patients and then telling it if the patients had a good or a bad outcome. I then tested what the neural network had ‘learned’ with the remaining 100 patients. The results were ok – I was quite pleased but not stunned, the predictive algorithm had an area under the ROC curve of about 0.7 – better than a coin toss but only just. I never published, partly because the software I used was unlicensed, free and unattributable but mainly because at the same time a research group from MIT in Boston published a paper doing more or less exactly what I had done but with a dataset of 40,000 patients – their ROC area was something like 0.84, almost useful and a result I couldn’t come close to competing with.

Using AI Intelligently

So what does this tell us? As practicing clinicians, if you haven’t already, you are very likely in the near future to be approached by a tech company selling an ‘AI’ solution for your area of practice. There are some probing questions you should be asking before adopting such a solution and they are remarkably similar to the questions you would ask of any research output or drug company that is recommending you change practice:

  1. What is the purpose of the tool?
    • Predicting an outcome
    • Classifying a condition
    • Recommending actions
  2. What type of algorithm is being used to process the data?
    • Supervised / Unsupervised
    • Classification / Logistic regression
    • Decision Tree / Random Forrest
    • Clustering
  3. Is the model fixed or dynamic? i.e. has it been trained and calibrated using training and testing datasets and is now fixed or will it continue to learn with the data that you provide to it?
  4. What were the learning criteria used in training? i.e. against what standard was it trained?
  5. What was the training methodology? Value based, policy based or model based? What was the reward / reinforcement method?
  6. What was the nature of the data it was trained with? Was it an organised labeled dataset or disorganised unlabelled?
  7. How was the training dataset generated? How clean is the data? Is it representative? How have structural biases been accounted for (Age, Gender, Ethnicity, Disability, Neurodiversity)?
  8. How has the model been tested? On what population, in how many settings? How have they avoided cross contamination of the testing and training data sets?
  9. How good was the model in real world testing? How sensitive? How specific?
  10. How have they detected and managed anomalous outcomes – false positives / false negatives?
  11. How do you report anomalous outcomes once the tool is in use?
  12. What will the tool do with data that you put into it? Where is it stored? Where is it processed? Who has access to it once it is submitted to the tool? Who is the data controller? Are they GDPR and Caldecott compliant?

Getting the answers to these questions are an essential pre-requisite to deploying these tools into clinical practice. If you are told that the answers cannot be divulged for reasons of commercial sensitivity – or the person selling it to you just doesn’t know the answer then politely decline and walk away. The danger we face is being seduced into adopting tools which are ‘black box’ decision making systems – it is incumbent on us to understand why they make the decisions they do, how much we should trust them and how we can contribute to making them better and safer tools for our patients.

An Intelligent Future

To be clear I am very excited about what this technology will offer us as a profession and our patients. It promises to democratise medical knowledge and put the power of that knowledge into the hands of our patients empowering them to self care and advocate for themselves within the machinery of modern healthcare. It will profoundly change the role we play in the delivery of medical care to patients – undermine the current medical model which relies on the knowledge hierarchy between technocrat doctor and submissive patient – and turn that relationship into the partnership it should be. For that to happen we must grasp these tools – understand them, use them intelligently – because if we don’t they will consume us and render us obsolete.

The Story of Digital Medicine

22. February 2017 · Write a comment · Categories: Health Informatics, Healthcare Delivery, Leadership, Patient Safety, Uncategorized · Tags: , , , , ,

I have read two stories this week.

The first was written in an interesting, contemporary literary style – you know the sort – short sparse sentences almost factual, leaving lots of ‘space’ for your own imaginative inference, not making explicit links between facts and events but leaving you to do that for yourself.  It was a love story, rather charming and quite short, describing a familiar narrative of boy meets girl, invites her to the cinema and they fall in love (probably).  It could be described as Chandleresque in style – though it isn’t that good – in fact it could have been written by an 11+ student.  It wasn’t though – it was in fact written by a computer using a form of artificial intelligence called natural language generation with genuinely no human input.  You can read how it was done here.

The second story I read is a description of a falling out of love – of the medical profession with the IT industry and the electronic patient record.  This one is very well written by Robert Wachter and is a warts and all recounting of the story of the somewhat faltering start of the digital revolution in healthcare.  It is called ‘The Digital Doctor’ and I would highly reccomend you read it if you have any interest in the future of medicine.  It is not the manifesto of a starry eyed digital optimist, nor is it the rantings of a frustrated digital skeptic – he manages to artfully balance both world views with a studied and comprehensive analysis of the state of modern health IT systems.  His realism though extends to understanding and articulating the trajectory of the health IT narrative and where it is taking us – which is a radically different way of delivering medical care.  I won’t use this blog to precis his book – its probably better if you go and read it yourself.

From Data to Information to Understanding

The falling out that Dr Wachter describes really is quite dramatic – this is the United States the most advanced healthcare system in the world – yet there are hospitals in the US that advertise their lack of an EPR as a selling point to attract high quality doctors to work for them.  Where has it gone wrong?  Why is the instant availabilty not only of comprehensive and detailed information about our patients but also a myriad of decision support systems designed to make our jobs easier and safer to carry out – not setting us alight with enthusiasm?  In fact it is overwhelming us and oppressing us  – turning history taking into a data collection chore and treatment decisions into a series of nag screens.

The problem is there is just too much information.  The healthcare industry is a prolific producer of information – an average patient over the age of 65 with one or more long term conditions will see their GP (or one of her partners) 3 – 4 times a year, have a similar number of outpatient visits with at least 2 different specialists and attend A&E at least once.  That doesn’t include the lab tests, x-rays, visits to the pharmacy, nursing and therapy episodes.  Each contact with the system will generate notes, letters, results, reports, images, charts and forms – it all goes in to the record – which, if it is a well organised integrated electronic record, will be available in its entirety at the point of care.

Point of care being the point – most health care episodes are conducted over a very short time span.  A patient visiting his GP will, if he’s lucky, get 10 minutes with her – it doesn’t make for a very satisfactory consultation if 4 or 5 of those minutes are spent with the doctor staring at a screen – navigating through pages of data attempting to stich together a meaningful interpretation of the myriad past and recent events in the patient’s medical history.

How it used to be (in the good old days)

So what is it that the above mentioned hospitals in the US are harking back to in order to attract their doctors?  What is the appeal of how it used to be done when a consultation consisted of a doctor, a patient and a few scrappy bits of paper in a cardboard folder?  Well for a start at least the patient got the full 10 minutes of the doctors attention.  The doctor however was relying on what information though?  What the patient tells them, what the last doctor to see them chose to write in the notes, and the other events that might have made it into their particular version of this patient’s health record.  This gives rise to what I call a ‘goldfish’ consultation (limited view of the whole picture, very short memory, starting from scratch each time).  We get away with it most of the time – mainly because most consultations concern realtively short term issues – but too often we don’t get away with it and patients experience a merry go round of disconnected episodes of reactive care.

IMG_0477

As a practitioner of intensive care medicine one of the things that occupies quite a lot of my time as ‘consultant on duty for ICU’ is the ward referral.  As gatekeeper of the precious resource that is an intensive care bed my role is to go and assess a patient for their suitability for ICU care as well as advise on appropriate measures that could be used to avert the need for ICU.  My first port of call is the patient’s notes – where I go through the entire patient’s hospital stay – for some, particularly medical patients, this might be many days or even weeks of inpatient care.  What I invariably find is that the patient has been under the care of several different teams, the notes consist of a series of ‘contacts’ (ward rounds, referrals, escalations) few of which relate to each other (lots of goldfish medicine even over the course of a single admission).  I have ceased to be surprised by the fact that I, at the point of escalation to critical care, am the first person to actually review the entire narrative of the patient’s stay in hospital.  Once that narrative is put together very often the trajectory of a patient’s illness becomes self evident – and the question of whether they would benefit from a period of brutal, invasive, intensive medicine usually answers itself.

Patient Stories

The defence against goldfish medicine in the ‘old days’ was physician continuity – back then you could  expect to be treated most of your life by the same GP, or when you came into hospital by one consultant and his ‘firm’ (the small team of doctors that worked just for him – for in the good old days it was almost invariably a him) for the whole admission.  They would carry your story – every now and then summarising it in a clerking or a well crafted letter.  But physician continuity has gone – and it isn’t likely ever to come back.

The EPR promised to solve the continuity problem by ensuring that even if you had never met the patient in front of you before (nor were likely ever to meet them again) you at least had instant access to everything that had ever happend to them – including the results of every test they had ever had.  But it doesn’t work – data has no meaning until it is turned into a story – and the more data you have the harder it is and longer it takes to turn it into a story.

And stories matter in medicine – they matter to patients and their relatives who use them to understand the random injustice of disease, it tells them where they have come from and where they are going to.  They matter to doctors as well – medical narratives are complex things, they are played out in individual patients over different timescales – from a life span to just a few minutes, each narrative having implications for the other.  Whilst we don’t neccessarily think of it as such – it is precisly the complex interplay between chronic and acute disease, social and psychological context, genetics and pathology that we narrate when summarising a case history.  When it is done well it can be a joy to read – and of course it creates the opportunity for sudden moment when you get the diagnostic insight that changes the course of a paient’s treatment.

Natural Language Generation

Turning the undifferentiated information that is a patients medical record – whether paper or digital – into a meaningful story has always been a doctor’s task.  What has changed is the amount of information available for the source material, and the way it is presented.  A good story always benefits from good editing – leaving out the superfluous, the immaterial or irrelevant detail is an expert task and one that requires experience and intelligence.  You see it when comparing the admission record taken by a foundation year doctor compared to an experienced registrar or consultant – the former will be a verbatim record of an exchange between doctor and patient, the latter a concise inquisition that hones in on the diagnosis through a series of precise, intelligent questions.

So is the AI technology that is able to spontaneously generate a love story sufficiently mature to be turned to the task of intelligently summarising the electronic patient record into a meaningful narrative? Its certainly been used to that effect in a number of other information tasks – weather forecasts and financial reports are now routinely published that were drafted using NLG technology.  The answer of course is maybe – there have been some brave attempts – but I don’t think we are there yet.  What I do know is that the progress of AI technology is moving apace and it won’t be very long before the NLG applied to a comprehensive EPR will be doing a better job than your average foundation year doctor at telling the patient’s story – maybe then we will fall back in love with EPR? Maybe…

The Third Age of Modern Medicine is Dawning

04. November 2015 · Write a comment · Categories: Health Informatics, Healthcare Delivery, Uncategorized · Tags: , , , ,


On the 14th April 2003 biomedical scientists achieved the medical equivalent of the 1969 apollo moon landings – The first entire gene sequence of a human was published.  This was a phenomenal achievement and was the culmination of 12 years of intensive research – it was announced by the US President with great fanfare along with excited promsises of revolutionary advances in medicine.  We all waited with anticipation – and we waited.  Rather like the dawning of the space age – that first momentous step seemed to be followed by a quite a prolonged period of rather disappointingly mundane achievements (where are the moon colonies, hotels on mars?).  My entire medical school training and early career was filled with promises of the genetic age of medicine.  And whilst without doubt the technology of genetics has transformed our understanding of disease and created many therapeutic opportunities – the revoloution seems to have been largely confined to the laboratory and some very rare inherited genetic disorders.  The impact on most doctors (and patients) has been marginal to non-existent.  I do believe this is about to change though.

The First Two Ages of Modern Medicine

I am defining modern medicine as the era in which it becam possible ‘to do’ something to alter the course of disease and suffering.  It largely coincides with the medical profession’s mastery of pain and conciousness – allowing for the explosive development of modern surgery, and its mastery of infection – through vaccination, asepsis and antibiotics.  These triumphs of the late 19th and early 20th century brought about a rather (possibly justifiably) hubristic ‘doctor knows best’ attitude of the profession and a transformation from cynicism (just read the literature to find out what the victorians thought of their doctors!) to profound trust of society in the capabilities of the profession.  I will call the first age of modern medicine the ‘Paternalistic Age’.  Of course we eventually discovered that doctors don’t always know best, and that when confered with unreasonable trust – like all humans – doctors sometimes betray that trust.

The second age came about with the realisation that individual experts do not have privileged access to knowledge – and that true knowledge comes about through scrupulous collection of evidence, and when that process is bypassed serious harm can result.  This is best exemplified (but not exclusively) by the Thalidomide tragedy.  Another example of the consequences of unchecked, unjustifiable trust would be Harold Shipman.  Whilst the foundations of trust in the profession have not been completely undermined – there is now a healthy wariness of the claims of the profession.  The second era of modern medicine is the one I have been brought up in – it is perhaps best described as the ‘Evidence Based Age’.  It has been characterised by the ‘standardisation’ of medical care, the medicalisation of health (primary prevention – statins), increasing specialisation and a subtle shift in the powerbase in the consulting room to one of patient as consumer of medical care and doctor as informant and provider.  It has also been characterised by an proliferation of regulation as well as litigation and the practice of defensive medicine.

The two ages overlap of course – by a considerable margin – even as the third age dawns there are still doctors with unfounded self belief and patients that simply submit themselves unquestioningly to their fate at the hands of the profession.  It is also not entirely certain that the second age is always an improvement on the first.  We struggle with ‘evidence’ – it seems to change its mind, and our method of gathering it is expensive, laborious and many of the problems we need solving don’t seem to be amenable to the standard methods of evidence gathering.  This has resulted in the evidence being biased significantly towards therapeutic intervention with drugs – because that is where the evidence gathering resource lies.  We are over regulated – to an opressive degree – and we have managed to instil in our patients both very high expectation and complete dependence.  We are also conflicted – when the evidence (that we sometimes doubt) tells us one thing, our instinct tells us another and our patients have unreasonably  high expectations for something else – it can feel like we don’t have the license to do the right thing.  We end up bewildering our patients by showering them with evidence, risks and benefits – and then saying ‘over to you’ knowing full well that our patients are ill equipped to decide.

There must be a better way – and there is – but it requires the confluence of three revolutions to bring it about.

Three Revolutions

The first of these is one I have written about extenisvely – it is the information revolution as it applies to medicine and healthcare.  The revolution in gathering, processing, decision making and redistribution of medical information is just about getting under way.  However it has not even started to realise its full potential yet.

The second revolution is one I have also previously alluded to – which is the patient empowerment revolution – also just about getting underway if a little slowly.  This not just places the patient at the centre of care, it places them as master of their destiny through empowerment and education.  The medical professional task is primarily one of teaching self care backed up by judicious, co-comissioned intervention.

The third revolution I haven’t written about before – mainly because I have only really just learnt about it.    Whilst I have possibly been dimly aware of the concept of genomics – the reality of it has emerged into my conciousness in the last month as a result of two events.  The first of these was our very own consultant conference at which we were introduced to the launch of the 100,000 genome project.  The second – allied to this – was a meeting at the Institute of Translational Medicine in Birmingham where we were helping NHS England formulate a strategy for ‘Personalised Medicine’.

The Genetic Revolution Begins

So has it finally arrived – the age of genetic medicine – that I was promised as a medical student (blah years ago)?  Well not quite – and of course I don’t think that the third age of modern medicine is the genetic age that was promised.  However genetics – or more specifically Genomics – does form the third pillar of the dawning of our new age.

Returning to our space age metaphor – the 100,000 Gemone Project is the equivalent to the first manned mission to Mars.  The 100,000 people that enter the project are the equivalent to the 200,000 volunteers that have put themselves forward for that mission.  Notwithstanding that we don’t know who they are yet – they will be the pioneers of the third age of modern medicine.  They don’t quite know what they are letting themselves in for, or where in fact they are going.  What is certain is that the journey is most definately one way.

The first human genome sequence cost the US taxpayer $3 billion and took 12 years – technology has advanced somewhat since then and it now costs less than £300 and takes a couple of hours.  Thats little more than the cost of an MRI scan.  You can buy your genome sequence online – don’t ask your doctor what the result means though, they won’t know.  In fact you would be hard pressed to find anyone that can interpret the vast amount of information that is your genome.  This is where the 100,000 genome project comes in – the aim of the project is to give all that information some sort of meaning.

We are more than our genes – we are the manifestation of our genes but with a context and a history.  It is the interaction of our genes with the environment over a sustained period of time – plus the impact of pathologies and the attrition of time on our DNA that makes ‘us’.  A genetic sequence has no meaning until it is interpreted in that context.  The true power of genomics will be realised when we know how people ‘like us’ respond to environmental, therapeutic and pathological influences and the impact that genetic variance has on that.  To achieve that we have to ‘cross reference’ the vast data base that is the genome with an equally vast database that is the ‘phenome’ i.e. everything else.

The 100,000 genome project will start with recruiting people with conditions for which we know there is a genetic component either of the disease itself or the response to currently available treatments – this includes a variety of cancers and a (quite long) list of other rare diseases.  It will collect the ‘phenotype’ of these people i.e. comprehensive and structured information about individuals, their history the environment in which they grew up and live, their response to treatment and their outcomes.  It will probably do the same for their families.  It will process huge amounts of data – and it may not even directly benefit our 100,000 pioneers – much of the significance  of this information will only become clear after time and many more individuals have been recruited.

This is a new paradigm in bio-medical research – it is the science of ‘discovery’ rather than the more familiar cycle of hypothesis testing through randomised control trial.  It imposes a discipline on the way we practice medicine – in particular the way we collect information.  It makes every health transaction an evidence creating one.  It is a model of continuous learning.  What is really exciting is that it is happening right here in the diverse, metropolitan beating heart of the country – Birmingham.

Where will it end?  From what I can see it certainly won’t end at the 100,000th patient.  It is quite a long way to Mars…

Interpreting the Future

So the third age – is it the ‘Genomic Age’? No – although I believe the aims and design of the 100,000 genome project epitomise third age medicine.  I am going to call the third age of modern medicine the ‘Interpretive Age’.  By this I mean the future of medicine will be personal.  We will need doctors that can interpret the large amounts of information from genomics, phenomics, proteomics, theranomics and infonomics (only the last one is my invention) relating to individual patients and interpret them in a way that has meaning for the patient – and that starts with listening to the patient and understanding their context, their wants needs and aspirations (psychonomics? socionomics?).

In many ways good doctors already do this.  Are the GPs that don’t give statins to patients with a 10% risk of heart disease in the next 10 years (see Times Thursday 29th October 2015) – denying patients best evidence based care or are they practising personalised medicine?  Is it right to call someone only at risk of disease a patient?  Genomics is really simply another tool that gives an unheralded level of precision to the decision making we can make with our patients for what is best for them.  There are many tools in that box – some of them listed above – are we equipped to use them though?  I am certain that when we have have ‘precision personalised medicine’ brought about through detailed interpretation of genetic, therpeutic, informatic data, we won’t be giving 3.5 million healthy people statins.

Are you an ‘Interpretive Doctor’?
 

Engineering The Future of Medicine

11. May 2015 · 1 comment · Categories: Healthcare Delivery, Leadership · Tags: , , , , , ,

Over recent months there has been an emerging consensus – articulated in reports from the Royal College of Physicians (The Future Hospitals Commission) and David Greenaway’s report for the GMC (The Shape of Training) – that it is time to put into acute reverse the socio-professional trend of the last 30 years of ever increasing medical super-specialisation. In their own ways these reports identify that the needs of a health system in which 70% of the activity is ongoing health maintenance of increasingly aged patients with 3 or more coexisting long term conditions, is not an army of doctors each of which can treat only one thing.

They also identify that the key specialties for managing this population – Emergency Medicine, Acute Medicine, Elderly Care Medicine and General Practice – are all ‘shortage’ specialties, i.e. there are more jobs available than people willing or able to do them by a considerable margin (8% – 22% vacancy factor [source:BMJ Careers May 2013] and that’s before you take into account the demographic time bomb of the mass retirement of a generation of GPs that started their careers during the last big expansion of the specialty in the 70s and 80s). If you move down the training hierarchy the fill rates are even more dismal – with 50% of higher specialist training posts in emergency medicine not being filled.

The ‘solution’ to the problem that is being proposed appears to be to increase exposure to these specialities earlier on in young doctors careers – make them do these types of jobs for longer – and at the same time make access to more specialised training (like cardio-thoracic surgery or neurology) much much more difficult by decimating the number of training places for them, in the hope that more of them will stick with the front line specialties rather than flood into the popular ‘super’ specialties (as they currently do – and always have). This apparent solution however seems to be completely ignoring the fact that a young doctor when faced with the choice of not getting access to the training in the specialty they want would rather up sticks and settle in Australia than to stay in the UK in a specialty that doesn’t interest them. Which is exactly what they are doing – in droves [Source: The Times, Saturday March 7th 2015].

So why is it that young doctors are eschewing the ‘Semi-differentiated’ specialities (my term – referring to the specialities listed above and to which I would add my own specialty of critical care – albeit not a shortage specialty)? What is it about the intellectual, practical and emotional challenge of providing care to patients with multi-system disease, in a psycho-social context that requires the corralling and coordinating of multi-professional multi-agency teams that puts them off? What is it about integrated care that is just so difficult?

Both the reports cited above home in on training as the issue – we are just not training our doctors right – and they propose some really quite radical changes to post-graduate medical training to address this. Whilst this is necessary, I do not think it is nearly radical enough – to really address the issue we need to go back to medical school and examine – who we are selecting; what we are teaching them; the skills we are equipping them with; and the attitudes they are possessed of when leaving medical school.

I have had cause to visit a number of university open days – not their medical schools but their engineering departments (I’ll leave you to guess why that might be). Engineering is a profession that requires the acquisition of at least as much if not significantly more technical skills and knowledge as medical training – the courses are just as intense and nowadays just as long (typically 4 – 5 years with a year in industry). Competition to get in is just as stiff and the bright young things wanting to do it are as possessed of the same desire ‘to make a difference’ as that which motivates those who enter a medical career. What has struck me though is that every engineering course I have looked at not only emphasises the knowledge and technical skills required (The maths!) but also have very large parts of their curriculum given over to the acquisition of non-technical skills – leadership, team work, collaboration, project management, business skills – all of which are required to be a successful engineer.

They are required to be a successful doctor as well – but we don’t teach them. You are selected for medical school on academic performance at the age of 18 – pass through 15 years of undergraduate and post graduate training and emerge as a highly knowledgable, very skilled technocrat – a heroic doctor – any non-technical skills acquired along the way more by accident than design. It is not just the non-technical skills they teach engineers that doctors need either. Becoming a doctor in an integrated care system requires many of the technical skills associated with engineering as well. Understanding complex adaptive systems, industrial process design, informatics and information technology (amongst many) are all skills we require of doctors if we are to ‘industrialise’ modern medicine.

We need to train a generation of doctors that are able to command and corral the multiple professions, agencies and technologies required to support the complex interaction of social, psychological and physical pathologies that represent the disease burden of our patients. We need a generation of specialists too – but specialisms built on a foundation of whole systems care. We need a generation of doctors that recognise that its not good enough just to be brilliant at one thing.

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Managing Hospital Mortality – Hands Off Mr Hunt

08. February 2015 · 9 comments · Categories: Healthcare Delivery, Leadership, Patient Safety · Tags: , , , , ,

As a medical director of a large acute hospital trust, how people die in our care occupies quite a lot of my waking thoughts (and quite a few of my sleeping ones too…). I thought I knew quite a lot about this subject – coming into the role from a background of critical care (where death is common). I have found though that my experience, whilst a useful primer in the topic, has demanded significant additional learning on my part – this post is my attempt to share some of that learning.

People Die in Hospital – That’s what they do

There is a curious symmetry in the social changes that have occurred at both the beginning and the end of life. The realisation over the last half century or so that many of what were believed to be inevitable tragedies at the beginning and end of life were in fact amenable to technological intervention and thus avoidable – and the change in the medical profession from overseer and commentator of natural tragedy to intervener and preventer of such – has driven these life events from a home based setting managed by community based services into a hospital setting managed by doctors, nurses and midwives.

This is largely a good thing – more mothers and babies survive the traumatic vagaries of child birth than ever before and people live longer healthier lives than ever before.

There is one rather stark asymmetry though – whilst death and tragedy in child birth is now a very rare event indeed – death at the end of life is still ultimately inevitable. As a society, even though we know we have to die eventually, we still haven’t worked out how we should die. This means that the majority of people end up dying in hospital (over 50% of people – thanks to Shaun Lintern and Craig Stenhouse for correcting this for me!) with doctors and nurses trying to stop them from doing so even when death is inevitable.

This is not a good thing because dying with someone trying to stop you with all the technological weapons of modern medicine (however well meaning) is not a good way to die.

The alternatives – provided by the heroes of the hospice movement – remains a cinderella service, patchily provided, reliant on charity and sparsley funded by public money.

Avoiding the Unexpected

So, people die in hospital – in our two hospitals that is about 1600 people a year (about 1 in every 75 admissions or put another way 4 or 5 admissions every day don’t make it home alive).

When these deaths are studied they can broadly be categorised into three groups according to whether they are expected or unexpected deaths and avoidable or unavoidable deaths.

Category 1 – Unavoidable expected deaths. This is overwhelmingly the largest category of deaths in hospital. People reaching the end of their natural lives, where their frail bodily systems have reached a point where they provide no defence against otherwise modest insults such as a fall, a urinary tract infection or the flu. Dying in hospital, as I alluded to above, has become the natural order of things in modern Britain (and many other developed health care systems in the world). Also included in this category are the deaths that we would recognise as ‘palliative care’ where even 21st century medicine genuinely has no response – terminal cancer or devastating neurological disease.

Category 2 – Unavoidable unexpected deaths. These are the genuine tragedies that punctuate life in a hospital. The relatively rare, but affecting cases that litter the landscape of a professional career. These are the maternal deaths from catastrophic amniotic fluid embolus, the brain haemorrhage from an unsuspected cerebral vascular anomaly, the tragically successful suicide or the disastrous major trauma from a road traffic accident (sadly I could go on). These deaths are often met with heroic efforts on the part of hospital staff to prevent them and are followed by dismay and soul searching when unsuccessful. When deconstructed at a later date there is often some learning (we can always do better) but just as often there is little to be learnt – bad things just happen.

Category 3 – Avoidable unexpected deaths. These are the ones where we got it wrong. Where lapses, incompetence, inexperience, neglect and system failure succeed in lining up in a single case to either actually cause death or prevent avoidable death. These are the cases that stay with you (believe me). This is the missed diagnosis because of a failure to follow up a test result, the failure to act on the deteriorating patient, the unacceptable delay in life saving intervention or the failure to take responsibility for a situation. They are thankfully rare (and overwhelmingly the smallest category of death) – not because the lapses and system failures are rare – they are not – but because (other than in exceptional circumstances) it takes several in a row to have such a devastating outcome, the good old Swiss cheese effect.

I suppose in theory there is a fourth category of avoidable expected death – that feels like a contradiction to me and I can’t conceive what it might be.

The ‘management’ task here is to attempt to assign every death to one of the three categories. That is not a trivial task – and it is not one for which you can take a statistical short cut, however compelling a notion that might be. Not least because in every category there will be deaths where on review there is identified a lapse of care – yet how many lapses does it take to move a death from either of the first two categories to the third?

Lies, Damn Lies and Mortality Statistics

Regular readers of my blog will know that I have been here before in my earlier post: Thinking about Mortality – Fast and Slow. I am not going to rehearse the arguments of the weaknesses of case mix adjusted, risk modified mortality statistics. Suffice to say that the reported ‘dramatic improvement’ in HSMR of the 11 Keogh Review hospitals will have had as much to do with scrutiny of their coding practices as the quality of care they provide.

Professor Nick Black through the PRISM (and soon to be published PRISM 2) study has shown little correlation between quality of care and HSMR (or SHMI or RAMI or any other way of measuring institutional mortality rates). I am trying not to be cynical about these – because they are in fact very useful tools, and cannot be ignored. However one has to be very careful in interpreting them because they tell you as much about what type of hospital you are and what type of patients you treat as they do about the quality of care you provide.

I can tell you without looking that:

Small provincial district general hospitals with proportionately large elderly care, stroke and fractured neck of femur services will have high mortality ratios (however well risk adjusted).

Large urban inner city general hospitals with relatively young transient populations will have low mortality ratios.

Tertiary Centres that carry out high risk care on relatively young patients (like liver transplants or major cancer surgery) will have high mortality ratios.

A Recipe for Managing Mortality

So where does this leave us? We can’t trust the stats and identifying the deaths where we got it wrong, sufficiently to have at least been a causative factor in the death, requires painstaking review of very large numbers of deaths and the judgement of Solomon.

Here are my tips:

1) Don’t ignore the stats – have a committee that looks at your death rates in all specialties and diagnostic groups – identifies the variances, the outliers and the alerts and investigates them even if CQC or Dr Foster don’t notice. You will learn a huge amount about how care is delivered in your hospital and how patients are moved about within it. You will also learn quite a lot about coding.

2) Have a system in place that reviews every death in the hospital. Some have chosen the Medical Examiner solution to this – paying a recently retired doctor to review the notes of every death and extract learning and identify those where more scrutiny is required. We have gone down a variant of the trigger tool methodology – every case is assessed against a set of criteria by a consultant not involved in the care of that patient, if enough triggers are present in one case it gets a table top review.

3) Have a method of sharing the learning – particularly with the consultants doing the screening (in our case that’s all of them), it is laborious work and it has to feel worthwhile. We are not so good at this yet – we have a news letter that goes out, we could and should do more.

4) Don’t trust Dr Foster – they are a commercial organisation that profits from scare stories. Don’t ignore them either.

5) You will find that 70% of patients that fall into category 3 are due to sepsis – invest in physiological track and trigger, sepsis screening tools and implement the sepsis 6 bundle and critical care outreach. Your avoidable mortality will fall.

6) You will also find that acute hospitals provide dreadful care to the majority of patients for whom death is unavoidable and expected (Category 1). The fortunate minority that find themselves under the care of palliative care doctors will get excellent care, the majority that don’t, won’t. Invest in end of life services – this has to be the greatest single priority for the NHS. We are about to publish our ‘Last Year of Life Audit’ – an investigation into the care provided in that critical period of a persons life. It will show that we admit to hospital on average three times in the last six months and start ‘supportive care’ on average 24 hours prior to death.

7) Finally – keep the politicians out, they don’t get it and never will and will do a great deal of harm meddling with stuff they don’t understand. You wouldn’t let them meddle with airport safety would you? why would you let them meddle with hospital mortality…?

A&E is not in crisis – It’s doing what it does best

07. January 2015 · 2 comments · Categories: Healthcare Delivery · Tags: , , , , , ,

We set a new precedent yesterday in one of our A&E’s. Over 400 patients attended and we admitted over 60 patients in one 24 hour period. This is unheard of – a normal ‘take’ in one of our hospitals is 25 – 30 patients from 200 attendances – yet for the last few weeks we have been routinely admitting in excess of 100 patients a day across our two hospitals.

We set another precedent this week as well – we discharged over 200 patients in two days. That is an astonishing number. Of those 200 discharges how many might you think were discharged to the care of social services? The answer is zero – because those two days were over the weekend shortly after the New Year bank holiday. In the same period the number of ‘delayed transfers of care’ has risen to 70 and the number of ‘medically fit for discharge’ (patients no longer requiring medical care but not safe to go home for other reasons) is another 20 or so on top of that. That’s three (unfunded) wards full.

We are under intense pressure – our staff are performing a herculean task – admitting and caring for the sick – shepherding them through the system – and organising safe and efficient discharge – and keeping the floor in A&E safe – just about. That task involves over 60 different professionals for one patient from the triage nurse at the front door through to the community therapies staff that in-reach into the hospital to pull through patients into our intermediate care facilities via the typically three or four different medical teams that might care for one patient during a hospital stay (A&E team, Acute Medical Team, Medical specialty team, MFFD team) – Not forgetting the critical care team that receive the 5% sickest patients on the way.

Yet we are coping. Whilst we have hit ‘Level 4’ at one or other of our hospitals on a few occasions over this period of ‘crisis’ we have never considered ‘closing our doors’ as some others have. And I am convinced I know why.

Synchronous Coordinated Action

Our hospitals march to the drum beat of the two hourly bed meetings. These are brief (less than 15 minutes), always at the same time (8,10,12,14,16,18,20:00 hrs), teleconferences, involving representatives of every part of the pathway from the front of the hospital to beyond the back door, consistent – run to a script, informative – everyone knows what they need to know and leaves knowing what they have to do (discharge 6 patients, 2 before lunch!). A nurse in charge of a community ward will know what the wait times are in A&E. All the time actions are followed up by the operations centre staff – our ‘air traffic control’.

Control of our Outflow

Just over two years ago our Trust took the strategic decision to – ‘move down the value chain’ – we run community services for over half our population and have developed intermediate care facilities in community locations around our catchment area. We are ‘in control’ i.e. have direct access to nearly 100 community beds – including ‘medically fit for discharge wards’ closely co-located but separate from the hospitals and run by GPs.

This has saved our bacon this winter. Its not been plain sailing – it has taken several weeks to get the ‘drum beat’ working consistently and reliably and there have been times when the acuity of admissions have been such that discharges haven’t happened for the ‘right’ reasons and this has threatened to overwhelm us. We have panned the 4 hour target like everyone else – but we’ve done alright for our patients. I am intensely proud of how well our Medical, Nursing, Therapies, Operations and Community staff have pulled together to pull it off.

Its not an A&E Crisis

A&E waits are a symptom of a failing health and social care system. A&E is the only point of care that is consistently available 24 hours a day. Everyone knows where it is, that its always open, that there will always be a doctor there and that you will get free treatment if you need it. People vote with their feet – other parts of the system are either not trusted or simply not available when needed.

The ‘A&E crisis’ is a consequence of un-restrained inflow and significantly constrained outflow. Neither the ‘inflow’ nor the ‘outflow’ is incentivised or held to account for the demands it places on the hospital. It is being in control of at least some of our outflow that helped us weather this particular storm. And therein lies the solution to the problem – if we want hospitals to ‘succeed’ then they must be in control of the resources of ‘outflow’ services and probably ‘inflow’ services as well.

At the moment the philosophy (if organised thinking is a suitable way to describe it) behind the Better Care Fund (The pooling of health and social care resources) is moving in diametrically the wrong direction on this. BCF is moving resources out of health into social care – on the face of it in order to prevent admission to and promote discharge from expensive hospital care. It will not work – however well meaning – it just won’t. Whilst depleted resource in social care is an issue (and a worsening one) fundamentally the issue is one of accountability and control. As long as the services that provide care before and after hospital experience no cost or consequence for sending a patient to or keeping a patient in hospital then they will continue to behave as they always have done. And as these services gradually fail medical takes in hospital of 60 or more will become the norm not the exception.

Time for a Digital Citizens Revolt

17. September 2014 · 1 comment · Categories: Health Informatics, Healthcare Delivery · Tags: , , , , , , ,

I have had two curious experiences as a digital citizen this week. It all started with a rather depressing article I read in BMJ last weekend. It was a ‘yes/no’ debate (a common format in the magazine) on the question of whether GPs should allow patients to email them. I found it depressing for two reasons 1) I found it astounding that we should be debating the ‘question’ at all 2) The implication that e-mail might be even a remotely sensible tool for digital access to health care.

Even Digital Doctors Don’t Change

The debate continued on twitter – it appeared to me as a casual observer – that the argument against (apparently the overwhelmingly held view – even of GP twitterers) distilled down to the following:
– GPs are inundated with patients in their surgeries so how could they possibly have time to answer e-mails from them. (The doctor as victim argument)
– Face to Face consultation is a sacred rite of healthcare delivery – any qualitative diminishment of this is a disservice to patients. (The ‘I’ve been trained to do it this way I can’t believe it could possibly be done by a machine’ argument)
– Patients will ask silly or trivial questions and we would be swamped by the worried well. (The ‘We know whats best for patients’ argument) .
– What evidence is there that on-line access reduces demand any way? (The ‘If there isn’t a randomised controlled trial I’m not going to change’ argument)
I was appalled by the narrow perspective, lack of self awareness and how patronising and patriarchal the medical profession still is in 2014. I, rather inadvisably, said so on twitter – and was hit by a ‘twitter storm’ (more of an angry gust if I’m honest) of protest from doctors – what would I know, I’m just an anaesthetist!

Patients Doing it for Themselves

So that was my first experience of an online ‘trolling’ – an important rite of passage as a digital citizen. My second curious experience I’m glad to say was more uplifting. I received an unsolicited invitation from the King’s Fund to take part in a round table discussion about ‘The Art of the Possible’ in a digital future for healthcare. This took place on tuesday night – there were a variety of people – CEOs, MDs, Academics, Community, Mental Health, Industry, DoH (HSCIC) and Patient representatives.

The session started with the absolutley captivating story from Katherine Cormack. She spoke unwaveringly of her experiences of childhood and adolescent mental health problems, the inadequacy of the services provided to treat them and of her attempts to create a solution from the inside (she worked for the NHS for a while trying to set up online help forums for teenagers with mental health issues – but had to give up because of the barriers to change) and eventually joined a social enterprise which has developed an online tool called BuddyApp which creates an online regulated self-help community for patients and therapists.

A long and fascinating conversation ensued – which I can’t divulge because we were operating under ‘Chatham House Rules’. However the messages I took away were the following:

1) There is a desperate desire from users of the health service for it to enter the digital age – they feel ‘locked out’ and rebuffed by an un-listening and un-reformable system.
2) The world is innovating around us – the NHS is being left behind
3) The NHS is a jealous guardian of patients health information and cannot see a way through the mire of data protection, confidentiality and competition law to release it back to them – although in fact there are no reasons why these should be barriers
4) The NHS is incapable of disrupting itself – it cannot release cash from things it is currently doing in order to invest in doing things differently – this was implacably conveyed by the man from HSCIC
5) Yet it is only technology enabled patient self care that has the potential to relive the inexorable rise in demand for NHS services
6) There is a stark asymmetry between the value we (the health service) think we add compared to what patients think we add
7) The NHS has the long history and deep seated culture of a public service organisation that thinks it knows best – this patriarchal culture suffuses the organisation from its civil servants and managers to its frontline clinicians.
8) Those on the inside with insight and the desire to change it are a definite minority – but they are passionate and are seeing and implementing solutions, albeit in small pockets of the service.

Some Myths about Digital Patients

The other strong message that came through is that there are some strongly held beliefs of health professionals about digital healthcare that are simply not born out by the evidence.

Myth 1 – ‘Technology is the preserve of the young and the elderly would be excluded’ – Smartphone market penetration has exceeded 90% in the UK and the fasted growing sector is the over 65s
Myth 2 – ‘The elderly struggle to use or understand modern information technology’ – It is not uncommon now on our wards to see patients young and old using this technology intuitively and effectively. I have seen patients on our elderly care wards ordering their supermarket shopping in preparation for their own discharge.
Myth 3 – ‘Handing out devices to patients will result in them being stolen’ – this is simply untrue – devices have been used in solutions for the homeless and drug addicted with no or minimal loss
Myth 4 – ‘Digital healthcare is a poor substitute for face to face and hands on consultation’ – There is a very large element of health seeking behaviour which is simply looking for trusted advice and reassurance, patients value the continuous access that digital health affords compared to the limited hours and inconvenience of primary care or the appalling experience of A&E.

A Coalition of Radicals

The most uplifting part of the evening was being able to meet and network with developers of solutions for digital healthcare (like BuddyApp), professionals from the health service that have put them to use and representatives from patient groups that have been campaigning for greater patient autonomy and self care through access to their own health record. They are a weary tribe of campaigners because they get little audience from the service they are trying to improve. However there was a clear shared vision and enthusiasm for digital access to healthcare and evidence of real progress from some innovative developers.

It is clear to me that it is this tripartite coalition – patients, third party developers and willing clinicians that will deliver the digital future of health. The only change from inside the health system that is needed is the release of patients own information back to them – and that should cost nothing. Its time to campaign for your right to access your own health information. This innovative disruption will change the NHS from the outside for ever.

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