Being awarded a medical degree in the UK confers certain privileges in terms of clinical practice. I think most people would therefore believe that what students are required to know —and what universities to some extent are required to teach— would be fairly clearly laid out somewhere. This is not the case however, certainly not in comparison with gaining a driving license or learning to fly a plane. There are high level descriptions, but exposure differs widely between different medical schools. At the postgraduate level, things are probably clearer. Other countries do things differently. I tend to think that measures of process —as in most of education—have a limited role. They are too easy to game, for one thing; and what you have to do to ensure learning takes place, is hard to capture on spreadsheets. What you can do however, is to check the outcomes: you can test that students are capable of what you want them to know and, with effort (and scale), you can do this robustly. You just have to remember that whilst you want them to pass the exam,  education is about more than passing the exam.

One problem we still therefore have to grapple with is knowing what you want students to learn. One popular approach is to limit teaching (and testing) to a limited range of conditions. The arguments for this approach are obvious: there is an awful lot of medical knowledge out there, and we have to guide students as to what we think is important. This approach leads to the ‘we expect students to know the ’10 commonest presentations’ in speciality X. I go along with this in my own discipline. If you look at different dermatology textbooks you quickly see that although there is some core material, the differences are enormous, and in general the level of detail in many textbooks unrealistic in terms of course structures. Recommending a book, without lots of annotation, seems inappropriate. So, we provide very detailed guidance on what material students are expected to master (for example see skincancer909 for skin cancer, and there is similar material for ‘rashes’ on the university teaching pages (firewalled)).

There are however problems with this approach, and they relate to how a confident diagnosis is achieved. If you diagnose a scaly red rash as psoriasis, you are doing two things. First, you are saying the physical signs match those you see in psoriasis, but second, you also saying that the signs match those seen in psoriasis more than they match those seen in other conditions. I am not trying to represent this formally, but the decision is a function of the likelihood of psoriasis / not-psoriasis. In the schema below, I have represented the ‘core knowledge’ as circle 1. But to diagnose these conditions, requires you to have to have knowledge of the other (non-core) conditions in circle 2. Circle 2 will usually be larger than circle 1. Then there is circle 3, representing those conditions that are either much rarer or much less important. Which of these do you mention?

Of course, the content of the circles is not just a measure of frequency, but has to include a weighting for severity and conditions ‘not to be missed’. The ability to diagnose a lesion as a basal cell carcinomas confidently, means knowing that a particular lesions is not a squamous cell carcinoma or a melanoma or a range of other tumours. You can only diagnosis a BCC confidently when you know that the lesion is not something else. As circle 1 becomes small in comparison with circle 2, diagnostic confidence drops. It is for these reasons that the classic ‘compare and contrast’ questions, and the ability to run through a differential diagnosis, matters more for learners than experts.

I do not have a solution, except that categorisation tasks (which is probably the key skill we want students to acquire) are much more error prone if the light you possess is so weak that most of the search space remains in darkness.

Lots has been written about how modest long term funding of those worked with their own hands at the bench underpinned the revolution that occurred in biology in the mid twentieth century. Think of Sakmann, Brenner, Watson and Crick (broad definition of a bench), and Hubel and Wiesel. I also think it is true of much clinical science (but not those dreaded mega trials ….). Sadly, much modern science is done by mini-chief executives who shift paper. It is not entirely their fault either. I once worked in a large science factory, and it was only then  that I understood what the term alienation really meant, or what Marx was really on about (‘Intellectual capitalism’). David Hubel wrote a piece not so long ago setting out what we needed to do to shift back to what we know worked very well. Most of the masters who benefit from the status quo will of course not rush to embrace it. Funding young people to follow their own path—rather than acting as serfs for others till their late thirties or even early forties (average age of first main NIH grant)— will not be universally popular. (Nor is it suitable for all of science).

Fred Sangers’s death suggests to me that we should measure the funding a scientist receives relative to how much Sanger received. It might bring a breath of realism into what we do. So, his modest funding, provides the base of 1. If you receive 1.5 FSU if means you receive annually 50% more than he received.  You can average it out over the years. The higher your FSU score, the more people who give you money, should wonder about what they are paying for. Better still, they might wonder if they can find another Sanger instead. Of course, we have to think about what you do with it, but before sorting that issue out (it’s trivial honest!), applicants have to answer why their need for funding is greater than that which Fred Sanger required. Are these extra people and experiments that good? Perhaps even great universities should start boasting about how low their average FSU scores are.

[here is  Sydney Brenner in Science on Sanger]

A Fred Sanger would not survive today’s world of science. With continuous reporting and appraisals, some committee would note that he published little of import between insulin in 1952 and his first paper on RNA sequencing in 1967 with another long gap until DNA sequencing in 1977. He would be labeled as unproductive, and his modest personal support would be denied. We no longer have a culture that allows individuals to embark on long-term—and what would be considered today extremely risky—projects.

And the old definition of a genius: somebody who had at least two great ideas.

Our nation’s lack of research in medical education contrasts starkly with the large and essential commitment to biomedical research funded by industry, philanthropic organizations, and the public.

In a NEJM article, ‘ Innovation in Medical Education, by Asch and Weinstein.

“When I was asked to give the keynote to graduates of the class of ’14, it was an honour that nevertheless filled me with terror and a bit of wistfulness.”…”The wistfulness comes from wondering whether I would want to be a member of the class of ’14 if I had the opportunity. These are trying times for young doctors.”

Brian Goldman

“We have to deliver value, and we’ve got to measure that value, and really adjusting the resources so we’re doing that well is a mission for you, the business officers of the colleges and universities. You’re the ones charged with fiscal management, and that has huge impact on every aspect of the student’s experience. On the quality of instruction, the availability of financial aid, the physical plant, the support systems. All of those are trade-offs that the financial model drives. My key message today is that that model will be under challenge. And so, instead of tuning it to find 3 percent here or 4 percent there, which has been the story in the past, there will be dramatic changes. … The role of the business officer won’t be just finding that last little tuning, or getting the reports done. It will be to get in the center of the strategy, working with the educational leaders, the effectiveness measures, and figuring out how those goals and the financial numbers come together.”

It’s tough agreeing with Bill Gates.

A short while back PNAS published a meta-analysis of studies by Freeman et al comparing traditional lectures with those that include more ‘active’ activities. In an accompanying news and views, Carl Wieman defined those active methods as follows: ‘In active learning methods, students are spending a significant fraction of the class time on activities that require them to be actively processing and applying information in a variety of ways, such as answering questions using electronic clickers, completing worksheet exercises, and discussing and solving problems with fellow students.‘ The magnitude of the effect was large, with some measures showing an effect size of 0.47. In a letter just published, Hora argues that it is hard to define exactly what traditional lectures are, and that there may be much heterogeneity in this group. In his words ‘the jury is still out on lecturing (his argument is more nuanced that this, so read his words).
I have sympathies on all sides. Lectures are not a natural kind, and the delivery, format and style will, I suspect, interact with content and the target group. To some extent, the question, ‘ Do lectures work?’ is a bit like asking ‘Do drugs work?’ Having said all of the that, the weight of evidence seems  clearly be in favour of more active methods. There are however other things to think about.
Changing how you do things in a traditional course is not like swapping one pill for another. Nor are studies based on single courses necessarily a good guide to what happens when you implement widespread change. Most importantly, much as though I think you can improve learning using more active methods in lectures, we need to look hard at why we rely on lectures to such an extent, and how we can phase many or most of them out (at least in medicine). We also need to work out when we should use them. Drugs have both benefits and side effects too (or at least unintended actions); some of the latter are occasionally useful. The art, is in matching the type of drug, to the type of patient, at the right time.

‘If there is more truth in the hallways than in meetings, you have a problem’

Ed Catmull,  Creativity Inc

I like Gerd Gigerenzer’s writings (see for instance The Empire of Chance and Simple Heuristics that make us smart) and I am sure it is not his fault that the same stories keep coming round again and again. This story on the BBC web site treads over old ground but of course the lessons remain the same (even if the book is different). Doctors don’t like working using Bayes’ theorem in clinic — at least not if we have to use algebra, rather than real numbers (as Gigerenzer makes clear). And I still think we do a poor job of teaching medical students statistics. But something niggles me about his line of argument, and in part it it is not a million miles away from some of Gigerenzer’s other work on heuristics and ‘quick and dirty’ computation.
One view of expertise is that doctors somehow work from ‘basic principles’ and then work out what to do. This used to be the dominant view of medical expertise: we had to understand the physiology, so that we had a live model in our brain of what was happening to the patient. This may well be true in some instances, but more often it seems to me that the burden of knowledge to do this is so great, that we just follow simple shortcuts or heuristics— or we read it off look-up charts. I actually think this is sensible. We don’t need to fret about the molecules, just as I don’t need to worry about machine code or C+ when I write this blog. What Gigerenzer is drawing attention to is the absence of the relevant cognitive prostheses that takes care of the number crunching for us. Of course if the prosthesis existed, we would play with it, and actually become more at ease with the algebra.