When you want to find your way around a city, you might memorise key streets or more likely use a simplified map as a guide as you travel. But when you know a city, you navigate by being able to recall how you get from A to B. In fact you may have difficulty drawing a map — certainly to scale — but your memory is made up of lots of instances of what lies around a particular corner. Much of what you learn about diseases is the map in this analogy. By contrast, what the experienced clinician knows are lots of instances of what lies round particular corners. Those instances have a name: they are called patients.

You cannot change or reform undergraduate medical education in a significant way without changing the way doctors work and behave.

Q: What’s at stake when scientists fib?

A: Science is the last institution where being honest is a quintessential part of what you’re doing. You can do banking and cheat, and you’ll make more money, and that money will still buy you the fast cars and the yachts. If you cheat in science, you’re not making more facts, you’re producing nonfacts, and that is not science. Science still has this chance of giving a lead to democratic societies because scientific values overlap strongly with democratic values.

Interview with Harry Collins about his book: Gravity’s Kiss: The Detection of Gravitational Waves Harry Collins MIT Press, 2017. 414 pp.

That’s a question I just got at our most recent all-hands meeting. I’ve been reminding people that it’s Day 1 for a couple of decades. I work in an Amazon building named Day 1, and when I moved buildings, I took the name with me. I spend time thinking about this topic.

“Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always Day 1.”

Resist Proxies: As companies get larger and more complex, there’s a tendency to manage to proxies. This comes in many shapes and sizes, and it’s dangerous, subtle, and very Day 2. A common example is process as proxy. Good process serves you so you can serve customers. But if you’re not watchful, the process can become the thing. This can happen very easily in large organizations. The process becomes the proxy for the result you want. You stop looking at outcomes and just make sure you’re doing the process right. Gulp. It’s not that rare to hear a junior leader defend a bad outcome with something like, “Well, we followed the process.”

(Do you know what they know they want?)

Good inventors and designers deeply understand their customer. They spend tremendous energy developing that intuition. They study and understand many anecdotes rather than only the averages you’ll find on surveys. They live with the design. I’m not against beta testing or surveys. But you, the product or service owner, must understand the customer, have a vision, and love the offering. A remarkable customer experience starts with heart, intuition, curiosity, play, guts, taste. You won’t find any of it in a survey.

Jeff Bezos here. I dislike process, and in education or research, whatever promise it offers, is offset by its tendency to lead to institutional denigration of those who keep their eyes on reality.

Via Benedict’s Newsletter

I keep coming back to a few central insights that have — in the best sense of the word — disturbed my world view. These are from a wonderful article in a journal I had never heard of, written by Frank Davidoff. (But I do not buy the term ‘revolution’)

Competence, in contrast, is like “dark matter” in astronomy: although it makes up most of the universe of working knowledge, we understand relatively little about it. What does it really consist of? Which of its components are most important? How do people acquire it? What’s the best way to measure it? And how can you tell when they have enough of it?

Most importantly, it is increasingly clear that competence is acquired primarily through experiential learning – a four-element cycle (or spiral) in which learners move from direct personal involvement in experiences, to reflection on those experiences, integration of their observations with sense-making concepts and mental models, and finally back to more experiences. Formal training for all high-performance (applied) professions, for example, music, architecture, theater, and athletics, is grounded in the unique requirements of experiential learning: case-based coaching, rather than lectures by content experts; hands-on, practicum experiences (including simulations, if necessary) in addition to written end-objectives; repeated experiences and outcome evaluations over time rather than initial, one- shot exercises; and, ultimately, acquisition of the advanced skills of “reflection-in-action,” which is required for high-level performance and “reflection-on-action,” which is required for continued self-evaluation and self- instruction (Schon, 1987).

Mens Sana Monographs:2008 | Volume:6 | Issue:1 | Page:29–40
Focus on Performance: The 21 st Century Revolution in Medical Education

Bruce Alberts talks a lot of sense about science education and education in general. And of course he produced a book that ‘educated’ a whole generation (or more) of people like me. But in this recent Science piece he is taking on some of the big questions, questions that have been asked before, but for which few have managed to follow through on. As ever, the emphases are mine.

In previous commentaries on this page, I have argued that “less is more” in science education, and that learning how to think like a scientist—with an insistence on using evidence and logic for decision-making—should become the central goal of all science educators. I have also pointed out that, because introductory science courses taught at universities define what is meant by “science education,” college science faculty are the rate-limiting factor for dramatically improving science education at lower levels.

For example, there is a long-standing belief that every introductory college biology course must “cover” a staggering amount of knowledge. There is no time to focus on a much more important goal—insisting that every student understand exactly how scientific knowledge is generated. Science is not a belief system; it is, instead, a very special way of learning about the true nature of the observable world.

His phrase, “college science faculty are the rate-limiting factor for dramatically improving science education at lower levels”, could equally apply to medicine and medical teachers. It is not hyperbole to say these are some of the central problems of our time. And it is not just science education that is the issue.

Universities are idea factories. Current corporatization approaches emphasize the factory rather than the ideas.

Ralf Buckley in Nature. I would say— for the short term at least — unless somebody finds a way to create new ‘dissenting academies’ things in UK higher ed will get worse.

“Core surgical training in the UK has been dubbed “core service training” because many trainees believe it does not provide enough surgical experience. At the southern tip of Africa, I felt I was being taught to operate, not to just watch and hold retractors. My commitment and progression were judged on hard work and merit, not on how many courses I had attended.”

Here.

Given your past views on measuring quality in universities, what do you think of the teaching excellence framework, which the government would like to use to measure teaching quality?

The government needs to think more about the evidence we have showing that measuring performance, and in particular ranking performance, creates strong incentives – but all too often the wrong incentives.

What is the biggest threat facing higher education today?

Too much emphasis on comparative achievement, not enough on the pleasure of learning or the importance of doing at least some things really well.

Amen. Nora O’Neill interviewed in the THE

There was an interesting paper published in Nature recently on the topic of automated skin cancer diagnosis. Readers of my online work will know it is a topic close to my heart.

Here is the text of a guest editorial I wrote for Acta about the paper. Acta is a ‘legacy’ journal that made the leap to full OA under Anders Vahlquist’s supervision a few years back — it is therefore my favourite skin journal. This month’s edition, is the first without a paper copy, existing just online. The link to the edited paper and references is here. I think this is the first paper in their first online only edition :-). Software is indeed eating the world.

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When I was a medical student close to graduation, Sam Shuster then Professor of Dermatology in Newcastle, drew my attention to a paper that had just been published in Nature. The paper, from the laboratory of Robert Weinberg, described how DNA from human cancers could transform cells in culture (1). I tried reading the paper, but made little headway because the experimental methods were alien to me. Sam did better, because he could distinguish the underlying melody from the supporting orchestration. He told me that whilst there were often good papers in Nature, perhaps only once every ten years or so would you read a paper that would change both a field and the professional careers of many scientists. He was right. The paper by Weinberg was one of perhaps fewer than a dozen that defined an approach to the biology of human cancer that still resonate forty years later.

Revolutionary papers in science have one of two characteristics. They are either conceptual, offering a theory that is generative of future discovery — think DNA, and Watson and Crick. Or they are methodological, allowing what was once impossible to become almost trivial — think DNA sequencing or CRISPR technology. Revolutions in medicine are slightly different, however. Yes, of course, scientific advance changes medical practice, but to fully understand clinical medicine we need to add a third category of revolution. This third category comes from papers that change the everyday lives of what doctors do and how they work. Examples would include fibreoptic instrumentation and modern imaging technology. To date, dermatology has escaped such revolutions, but a paper recently published in Nature suggests that our time may have come (2).

The core clinical skill of the dermatologist is categorising morphological states in a way that informs prognosis with, or without, a therapeutic intervention. Dermatologists are rightly proud of these perceptual skills, although we have little insight as to how this expertise is encoded in the human brain. Nor should we be smug about our abilities as, although the domains are different, the ability to classify objects in the natural world is shared by many animals, and often appears effortless. Formal systems of education may be human specific, but the cortical machinery that allows such learning, is widespread in nature.

There have been two broad approaches to try and imitate these skills in silica. Either particular properties (shape, colour, texture etc.) are first explicitly identified and, much as we might add variables in a linear regression equation, the information used to try and discriminate between lesions in an explicit way. Think of the many papers using rule based strategies such as the ABCD system (3). This is obviously not the way the human brain works: a moment’s reflection about how fast an expert can diagnose skin cancers and how limited we are in being able to handle formal mathematics, tells us that human perceptual skills do not work like this.

There is an alternative approach, one to some extent that almost seems like magic. The underlying metaphor is as follows. When a young child learns to distinguish between cats and dogs, we know the language of explicit rules is not used: children cannot handle multidimensional mathematical space or complicated symbolic logic. But feedback, in terms of what the child thinks, allows the child to build up his or her own model of the two categories (cats versus dogs). With time, and with positive and negative feedback, the accuracy of the perceptual skills increase — but without any formal rules that the child could write down or share. And of course, since it is a human being we are talking about, we know all of this process takes place within and between neurons.

Computing scientists started to model the way that they believed collections of neurons worked over 4 decades ago. In particular, it became clear that groups of in silica neurons could order the world based on positive and negative feedback. The magic is that we do not have to explicitly program their behaviour, rather they just learn, but — since this is not magic after all — we have got much better at building such self-learning machines. (I am skipping any detailed explanation of such ‘deep learning’ strategies, here). What gives this field its current immediacy is a combination of increases in computing power, previously unimaginable large data sets (for training), advances in how to encode such ‘deep learning’, and wide potential applicability — from email spam filtering, terrorist identification, online recommendation systems, to self-driving cars. And medical imaging along the way.

In the Nature paper by Thrun and colleagues (2) such ‘deep learning’ approaches were used to train computers based on over 100,000 medical images of skin cancer or mimics of skin cancer. The inputs were therefore ‘pixels’ and the diagnostic category (only). If this last sentence does not shock you, you are either an expert in machine learning, or you are not paying attention. The ‘machine’ was then tested on a new sample of images and — since modesty is not a characteristic of a young science — the performance of the ‘machine’ compared with over twenty board certified dermatologists. If we use standard receiver operating curves (ROC) to assess performance the machine equalled if not out-performed the humans.

There are of course some caveats. The dermatologists were only looking at single photographic images, not the patients (4); the images are possibly not representative of the real world; and some of us would like to know more about the exact comparisons used. However, I would argue that there are also many reasons for imagining that the paper may underestimate the power of this approach: it is striking that the machine was learning from images that were relatively unstandardised and perhaps noisy in many ways. And if 100,000 seems large, it is still only a fraction of the digital images that are acquired daily in clinical practice.

It is no surprise that the authors mention the possibilities of their approach when coupled with the most ubiquitous computing device on this planet — the mobile phone. Thinking about the impact this will have on dermatology and dermatologists would require a different sort of paper from the present one but, as Marc Andreessen once said (4), ‘software is eating the world’. Dermatology will survive, but dermatologists may be on the menu.

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Full paper with references on Acta is  here.