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…

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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Blameworthy Acts in a Blame Free Safety Culture

18. November 2012 · 4 comments · Categories: Leadership, Patient Safety · Tags: , , , , , , , , ,

As a medical director I am routinely required to assess, grade and act on the results of serious adverse events that have occurred in hospital. Often these events have resulted from failures of care through lapses, oversights, errors or neglect. This is often accompanied by a clarion call for some form of disciplinary action and or restitution – usually most insistently from within the organisation rather than by those directly affected, either carers or the patients themselves.

Bad things happen in hospital all the time. Healthcare is the only industry where for a significant minority of users the outcome is death or injury, either expected or unexpected. The overwhelming priority in this situation for both the recipients and providers of the care is learning: learning the truth of events, learning if it was avoidable, learning how it might be avoided in the future, and sharing that learning so it might be avoided elsewhere.

Prerequisites for Organisational Learning

We have, as human beings, an innate gift for learning – it is built into our DNA and, whilst most active in our early years of life, never really leaves us. Individual learning is the most powerful lever of change in human societies, because people love to learn and change as a result. Teams and organisations are made up of people and yet team and organisational learning does not happen by chance as it does for individuals – team learning is an unnatural and deliberate act.

There are three prerequisites needed within organisations in order to promote learning from error and system failure. It is strangely rare to find them all reliably present in healthcare organisations.

  • A Learning Environment
  • A Team Based Learning Infrastructure
  • A Compelling Vision Delivered Through Leadership

    • I will expand on these three prerequisites, but first I want to explore why they are found rarely in our hospitals and healthcare organisations.

    Two Key Barriers to Organisational Learning in Hospitals

    Hospitals are busy places, this is a universal truth – not unique to the NHS. The work processes of nurses and doctors in hospitals rarely run smoothly – they are by their nature characterised by frequent interruptions, unexpected deviations and minor crises. In order to get the job done a large part of the work involves having to create on-the-hoof workarounds and solutions to problems – giving rise to the familiar sense of almost continuous ‘fire fighting’.
    20130120-132457.jpg
    We are actually incredibly successful at doing this, much of our individual innate learning capacity is consumed developing coping strategies for the chaotic environment we find ourselves in. The problem with this ‘first order problem solving’ for ‘low level failure’ is that the learning it generates is of value only to the individual nurse or doctor – they are simply adapting to the flawed environment they find themselves in – just to get the job done. In doing so they are condemning themselves and and their successors to having to learn the same lessons in perpetuity – this grinds you down and drives talent away from ‘the front line’. How do we break the cycle of low level failure that requires constant first order problem solving making every day work flow inefficient and time consuming? The first step is to recognise the problem and then acknowledge that low level failure, whilst common place, is neither inevitable nor acceptable. The next step is to then deliberately and collectively make the time to move first order problem solving into second order problem solving (of which more later).

    The second key barrier to organisational learning in hospitals is a deeper, more cultural one. This is to do with interpersonal attitudes and responses to error. The shameful truth is that the overwhelmingly pervasive culture is a blaming one that inhibits speaking up with questions, concerns and challenges that might otherwise have caught and corrected human error. Moreover there is a culture in medicine that does not encourage admissions of error. Both ourselves and others have high expectations of success in medicine – when we don’t meet those expectations we are as blaming of ourselves as we might expect others to be. What is interesting is that the direction of blame isn’t just top down – in fact top down blame only really materialises when the failures mount up to catastrophic levels. The vast majority of, and undoubtedly more corrosive, blame is that of our colleagues and peers. What is clear is that whilst blame remains the primary response to failure opportunities for learning will be lost and the quality of the lessons learnt will be poor. Overcoming this barrier is a true challenge of leadership at all levels of an organisation as it requires a change in culture – a clear and sustained statement and restatement of values, unwavering adherence to behaviours that follow from those values, even in the face of challenges from within and without the organisation.

    Leading Learning for Patient Safety

    So where should we start with creating a learning culture in our organisations? The answer has to be with leadership, because without leadership on this issue nothing else can follow. The type of leadership and skills required to lead learning, however, are not what are typically viewed as traditional leadership skills. The leadership model for leading learning differs from the traditional leadership model in several important ways:

  • Whilst a ‘burning platform’ undoubtedly exists, the future state can only be guessed at (in an educated way)
  • This makes it hard to articulate
  • The flaws in the current state are hard to spot – there is a deep seated culture of acceptance of low level failure
  • The way forward is not a clear plan with deadlines and critical paths but a process of experimentation, a gradual reduction of uncertainty and regular revision of interim goals and ultimate vision
  • The leadership task is primarily one of engagement and reduction of fear not a promotion of employee effort
  • The task will never be finished

    • If you have read my previous blogs you might guess that I believe these ‘New Model Leaders’ need to come from the rank and file of doctors, nurses and other healthcare professionals that don’t often put themselves forward for such a role.

    Second Order Problem Solving and A Team Based Learning Infrastructure

    Second order problem solving is about creating long term fixes for recurrent problems, it is about analysing root causes and putting in place solutions with ‘traction’, it is often about changing behaviours in ourselves that have consequences for others. There are several reasons why we don’t stop and take the time and effort required to convert first order to second order problem solving. First of all – it does take both time and effort – neither of which we have much left of after a day / week / month / years of fire fighting. Secondly the problems we need to solve are quite often not even perceived as problems, we have been compensating for so long it has just become part of the job – this is where our new model leader has to be insightful. Thirdly second order problem solving requires some quite specific skills such as root cause analysis, process mapping, and change modelling that are not commonly found in healthcare teams. Fourthly – we are quite proud of our first order problem solving, being a coper and thriver in a stressful front line job is associated with significant kudos, particularly in the hospital environment. Finally it does require us to meet as teams for a significant time on a regular basis – which we are astonishingly bad at doing – and when we do for those team meetings to be led in a way that promotes speaking up, learning from others, admissions of failure and a willingness to innovate (and therefore risk failure). This final requirement leads on to the the final pre-requisite for organisational learning – an environment of psychological safety – A Learning Environment

    Blame Free Culture Vs Accountability – A Balance that Creates ‘Psychological Safety’

    Our new model leaders have their work cut out – not only do they have to create time (in an already overloaded time table) to bring together teams (who are singularly reluctant to gather) to discuss both low level and high level failure (failures that may not even be recognised as such) and defend these notions against pressures to use the time ‘more productively’; but also resist the temptation and pressures from above, inside and out to apportion blame for every failure that comes to light. The prize is great if they achieve it – a learning environment in an organisation that continually improves both itself and the people that move through it, one that delivers both on the economic and quality front. A true value adding organisation.

    But – it can’t all be so idyllic surely? People do also make mistakes borne out of stupidity, brazen over confidence, ignorance, stubbornness, laziness, jealousy and – yes – even malice. There is a level of human behaviour for which we all need to be held account. There is also a performance imperative, we all have to be helped to raise our game. Where is the place for accountability in a blame free culture? The diagram below will perhaps help you decide…

    20130120-213610.jpg
    This is the essential difference between ‘blame free’ and ‘psychologically safe’ for the latter comes not just from creating an environment where people feel able to speak up and admit failure but also feel assured that when boundaries are truly crossed that individuals will be held to account. This is the real test of leadership – knowing and communicating expectations and boundaries as well.

    Blameworthy Acts – the Boundaries of a Blame Free Culture

    Where do you draw the boundaries? There are no text books, there are no rules – there is intuition and there are inspirational leaders we can follow. Here is my starter for ten of blameworthy acts:

  • Reckless behaviour
  • Disruptive behaviour
  • Working significantly outside your capability
  • Disrespectful behaviour
  • Knowingly violating standards
  • Failure to learn over time
  • Failure to work as a team
  • Covering up

    • No doubt there are more. Clear boundaries around a learning zone create an environment in which organisations can thrive and patients can feel and be safe.

    I have to acknowledge the source of the ideas for this article. Amy C Edmonson – a truly inspirational teacher at HBS who not only articulates this message with conviction but backs it up with the irrefutable results of research both in healthcare and other settings.

    Medicine is an Information Industry

    27. August 2012 · Write a comment · Categories: Health Informatics · Tags: , , , ,

    In a book chapter I wrote on the subject of information management in critical care, I concluded that one of the most important challenges for this generation of doctors is the transfer of clinical information management from paper to electronic systems. So far we have failed that challenge, the vast majority of clinical information is still being recorded and managed (rather poorly) on paper. Those parts that are managed electronically are, in general, still cumbersome, bespoke systems that serve functions other than the delivery of clinical care far better than the needs of doctors, nurses or even patients. As a result a lot of these systems are at best grudgingly tolerated, often despised and sometimes even avoided altogether. The majority of doctors, with the exception of the minority enthusiasts, have withdrawn from the conversation on development of information management systems (or even been left out altogether) because it has been seen as a technological challenge rather than a clinical one. This is wrong and has to change because the way we manage clinical information is a crucial enabler for radical change in health care delivery. If doctors fail in this challenge we will find ourselves marginalised and obsolete in an ‘innovatively disrupted’ health economy.

    Early Adopters

    There is, of course, some history here which partly explains our current situation. Electronic clinical information systems have been in existence for over twenty years. The early years of the development of these systems was dominated by the technological challenges. The sheer volume and complexity of information that is collected in the course of delivering clinical care was a challenge when the cost of electronic storage was high and networking infrastructure not well developed. Taming the complexity of the information – codifying it and structuring it so that it could ‘fit’ in a conventional database – was not only difficult but also met with resistance of professionals as it constrained practice and the PC / workstation became a barrier between doctor and patient. Despite these challenges there are examples of hospitals and hospital systems that showed the world how it could be done (Burton Hospital being a notable example in the NHS) and also how it could go wrong.

    The Lost Decade

    If the nineties was the pioneering decade for clinical information systems then the first decade of this century can only be characterised as the ‘lost decade’ – whilst the Internet flourished and the age of distributed, personalised, world-in-your-pocket computing dawned – hospital IT systems remained desk-bound, cumbersome, inflexible, centralised systems. The need for information sharing was misinterpreted as a need to provide a single solution for all. A strategy that has cost billions, failed to deliver and diverted funding and more importantly the engagement of the medical profession (it was often doctors with IT skills that where the pioneers of the early adoption period) away from user and patient centred solutions.

    A Tablet Ushers in a New Era of Medicine

    Technology is no longer the problem – storage is cheap and abundant, networks are reliable and fast and devices are powerful, intuitive and mobile. Data management has transformed as well. XML allied to sophisticated search algorithms means less taming of information is required, the structure of the ‘database’ need not trouble the user any longer. Cloud technology means that information can be kept absolutely secure whilst not compromising the freedom of permitted users. The technology really has come of age and has surpassed the specification required to deliver clinical information management that truly serves the needs of patients, doctors and managers. Mobile devices like the iPad can give doctors both tools for information gathering and the tools to access it when it is needed without the technology getting in the way of the transaction with the patient.

    Paper, Paper Everywhere!

    But we are still using paper – tons of it. Medical records are stuffed with cardboard folders bursting with, mostly useless, pieces of paper. The information is locked away, unstructured and inaccessible – every request for information (and there are lots) is a mountainous struggle, consuming hundreds of man hours to extract it. The functions of the paper medical record as care coordinator, communicator, clinical process manager, monitor and legal witness are all conflated and result in an extreme precautionary approach to the retention of information which completely subsumes the probably more important function as informant almost as important (and often more informative) as the patient themselves.

    It’s the Information Stupid

    It’s time for the conversation to move from the technology to the information. We must focus on the type of information we gather, how we gather it, what we need and when we need it in order to deliver safe effective care. So much duplication and iteration and re-iteration of clinical information has evolved as a defence against the in-accessibility to information. Most patients I have met are astonished at the number of times they are asked the same questions over and over again even within the same clinical episode – they see the duplication and fragmentation that we as professionals miss.

    The care we give our patients is complicated and messy – partly because our patients are complicated and inflict on us huge variance in presentation, severity, comorbidity and response to treatment. That is the nature of medicine and what makes it so all consumingly interesting. But we make life exponentially more difficult for ourselves by imposing our own variance in practice and reliability on this already unpredictable background. Doing it differently every time, sometimes even changing our mind half way through results in variance on variance which is the definition of chaos. Chaotic medicine results in unpredictable, usually poor, outcome and huge waste – and is bad medicine.

    There is an answer to the information problem which also solves the chaos problem and results in not just better care but dramatically better care. Healthcare organisations that adopt this solution are not only better than their peers they are exponentially better. The solution is the key to delivering reliable care and it is the Clinical Process Model. This will be the subject of my next blog.

    Are we building hospitals or Healthcare Delivery Systems?

    27. August 2012 · Write a comment · Categories: Healthcare Delivery · Tags: , , , , , , ,

    It is interesting to reflect – now that the PFI bonanza has come to an end and we all have to hunker down and work out how to pay for it for the next 30 years – on what we have spent all the money on and consider whether what we have thrown up around the land is actually what we need.

    This paper by the think tank Reform The Hospital is Dead Long Live The Hospital is an eloquent exposition of Clayton Christensen’s ‘Innovator’s Prescription’ within an NHS context. The essential conclusion of both of these is that Hospitals need to move from being ‘A place where sick people go’ to becoming ‘An organisation that keeps people well’. This re-framing of purpose prompts the question – what does a hospital that keeps people well look like? I suspect it is not a large building with lots of beds in it (or clinic rooms for that matter).

    Interestingly the specialty of Intensive Care Medicine underwent a similar re-framing of purpose over ten years ago as a result of the comprehensive critical care program in response to a lack of intensive care beds. The outcome of this process was the introduction of critical care outreach teams (or medical emergency response teams) linked to a system of population surveillance (MEWS track and trigger) and an expansion of lower acuity beds (high dependency). There were almost no additional intensive care beds commissioned or provided. The result has been intensive care units have been able absorb ten years of demand growth, almost eliminate the need for inter hospital transfer for capacity reasons, reduce futile care, contain costs and improve outcome.

    How do we replicate this operating model at the scale of the hospital within a health economy (as opposed to an intensive care unit in a hospital)? The essential elements are:
    1) Knowing the population you are caring for – a disease registry
    2) Knowing how they are – a simple method of measuring disease status
    3) A response team that averts crisis when a trigger threshold is reached – a specialist community team
    4) An escalation pathway that includes rapid access to specialist input – specialty hubs
    5) Lower acuity beds for step up or step down care – intermediate care beds
    6) Alternate pathways for those that acute care is inappropriate – end of life services
    7) Acute beds for those that genuinely need it – closely linked to an intensive care unit!

    This distributed model of care does still need buildings – but what it needs more is intelligent information and communication systems used by a workforce that understands the need to keep patients other than those in genuine need away from hospital. It also needs an operating system that measures its impact, analyses unexpected pathway deviance and learns from system failure.

    Eliminating the huge waste in the system of inappropriate and futile hospital care (both inpatient and outpatient) will not only deliver cost savings it will improve quality of care and outcomes and create the capacity we need for the growth in demand we know is coming.

    The hospital is no longer a building it is a healthcare delivery system. We should be investing in the infrastructure that makes it possible – And that is not bricks and mortar…