Category: Mistakes and errors

Negligent nurses? Or dodgy digital? – device design can unintentionally mask errors

Magicians often fool their audience into ‘looking over there’ (literally or metaphorically), getting them to pay attention to the wrong thing so that they’re not focusing on what the magician is doing and can enjoy the trick without seeing how it was done. Computers, phones and medical devices let you interact with them using a human-friendly interface (such as a ‘graphical user interface’) which make them easier to use, but which can also hide the underlying computing processes from view. Normally that’s exactly what you want but if there’s a problem, and one that you’d really need to know about, how well does the device make that clear? Sometimes the design of the device itself can mask important information, sometimes the way in which devices are used can mask it too. Here is a case where nurses were blamed but it was later found that the medical devices involved, blood glucose meters, had (unintentionally) tripped everyone up. A useful workaround seemed to be working well, but caused problems later on.

At the end you can find more links between magic and computer science, and human-computer interaction.

Negligent nurses? Or dodgy digital?

by Harold Thimbleby, Swansea University and Paul Curzon, Queen Mary University of London

It’s easy to get excited about new technology and assume it must make things better. It’s rarely that easy. Medical technology is a case in point, as one group of nurses found out. It was all about one simple device and wearable ID bracelets. Nurses were taken to court, blamed for what went wrong.

The nurses taken to court worked in a stroke unit and were charged with wilfully neglecting their patients. Around 70 others were also disciplined though not sent to court.

There were problems with many nurses’ record-keeping. A few were selected to be charged by the police on the rather arbitrary basis that they had more odd records than the others.

Critical Tests

Blood glucose meter image by Katherine Ab from Pixabay

The case came about because of a single complaint. As the hospital, and then police, investigated, they found more and more oddities, with lots of nurses suddenly implicated. They all seemed to have fabricated their records. Repeatedly, their paper records did not tally with the computer logs. Therefore, the nurses must have been making up the patient records.

The gadget at the centre of the story was a portable glucometer. Glucometers allow the blood-glucose (aka blood sugar) levels of patients to be tested. This matters. If blood-sugar problems are not caught quickly, seriously ill patients could die.

Whenever they did a test, the nurses recorded it in the patient’s paper record. The glucometer system also had a better, supposedly infallible, way to do this. The nurse scanned their ID badge using the glucometer, telling it who they were. They then scanned the patient’s barcode bracelet, and took the patient’s blood-sugar reading. They finally wrote down what the glucometer said in the paper records, and the glucometer automatically added the reading to that patient’s electronic record.

Over and over again, the nurses were claiming in the notes of patients that they had taken readings, when the computer logs showed no reading had been taken. As machines don’t lie, the nurses must all be liars. They had just pretended to take these vital tests. It was a clear case of lazy nurses colluding to have an easy life!

What really happened?

In court, witnesses gave evidence. A new story unfolded. The glucometers were not as simple as they seemed. No-one involved actually understood them, how the system really worked, or what had actually happened.

In reality the nurses were looking after their patients … despite the devices.

The real story starts with those barcode bracelets that the patients wore. Sometimes the reader couldn’t read the barcode. You’ve probably seen this happen in supermarkets. Every so often the reader can’t tell what is being scanned. The nurses needed to sort it out as they had lots of ill patients to look after. Luckily, there was a quick and easy solution. They could just scan their own ID twice. The system accepted this ‘double tapping’. The first scan was their correct staff ID. The second scan was of their staff card ID instead of the patient ID. That made the glucometer happy so they could use it, but of course they weren’t using a valid patient ID.

Self service till

Supermarket till from I See Modern Britain on Flickr.

As they wrote the test result in the patient’s paper record no harm was done. When checked, over 200 nurses sometimes used double tapping to take readings. It was a well-known (at least by nurses), and commonly used, work-around for a problem with the barcode system.

The system was also much more complicated than that anyway. It involved a complex computing network, and a lot of complex software, not just a glucometer. Records often didn’t make it to the computer database for a variety of reasons. The network went down, manually entered details contained mistakes, the database sometimes crashed, and the way the glucometers had been programmed meant they had no way to check that the data they sent to the database actually got there. Results didn’t go straight to the patient record anyway. It happened when the glucometer was docked (for recharging), but they were constantly in use so might not be docked for days. Indeed, a fifth of the entries in the database had an error flag indicating something had gone wrong. In reality, you just couldn’t rely on the electronic record. It was the nurses’ old fashioned paper records that really were the ones you could trust.

The police had got it the wrong way round! They thought the computers were reliable and the nurses untrustworthy, but the nurses were doing a good job and the computers were somehow failing to record the patient information. Worse, they were failing to record that they were failing to record things correctly! … So nobody realised.

Disappearing readings

What happened to all the readings with invalid patient IDs? There was no place to file them so the system silently dropped them into a separate electronic bin of unknowns. They could then be manually assigned, but no way had been set up to do that.

During the trial the defence luckily noticed an odd discrepancy in the computer logs. It was really spiky in an unexplained way. On some days hardly any readings seemed to be taken, for example. One odd trough corresponded to a day the manufacturer said they had visited the hospital. They were asked to explain what they had done…

The hospital had asked them to get the data ready to give to the police. The manufacturer’s engineer who visited therefore ‘tidied up’ the database, deleting all the incomplete records…including all the ones the nurses had supposedly fabricated! The police had no idea this had been done.

Suddenly, no evidence

When this was revealed in court, the judge ruled that all the prosecution’s evidence was unusable. The prosecution said, therefore, they had no evidence at all to present. In this situation, the trial ‘collapses’: the nurses were completely innocent, and the trial immediately stopped.

The trial had already blighted the careers of lots of good nurses though. In fact, some of the other nurses pleaded guilty as they had no memory of what had actually happened but had been confronted with the ‘fact’ that they must have been negligent as “the computers could not lie”. Some were jailed. In the UK, you can be given a much shorter jail sentence, or maybe none at all, if you plead guilty. It can make sense to plead guilty even if you know you aren’t — you only need to think the court will find you guilty. Which isn’t the same thing.

Silver bullets?

Governments see digitalisation as a silver bullet to save money and improve care. It can do that if you get it right. But digital is much harder to get right than most people realise. In the story here, not getting the digital right — and not understanding it — caused serious problems for lots of nurses.

It takes skill and deep understanding to design digital things to work in a way that really makes things better. It’s hard for hospitals to understand the complexities in what they are buying. Ultimately, it’s nurses and doctors who make it work. They have to.

They shouldn’t be automatically blamed when things go wrong because digital technology is hard to design well.

This article was originally published on the CS4FN website and a copy can be found in Issue 25 of the CS4FN magazine, below.

Related Magazine …

Magic Book

There are a number of surprising parallels between magic and computer science and so we have a number of free magic booklets (The Magic of Computer Science 1, 2 and 3 among others) to tell you all about it. The booklets show you some magic and talk about the links with computing and computational thinking. From the way a magician presents a trick (and the way in which people interact with devices) to self-working tricks which behave just like an algorithm. For the keenest apprentices of magic we also have a new book ⬇️, Conjuring with Computation, which you can buy from bookshops or as an e-book. Here are a couple of free bonus chapters.

EPSRC supports this blog through research grant EP/W033615/1.

Mary Clem: getting it right

by Paul Curzon, Queen Mary University of London

Mary Clem was a pioneer of dependable computing long before the first computers existed. She was a computer herself, but became more like a programmer.

A tick on a target of red concentric zeros
Image by Paul Curzon

Back before there were computers there were human computers: people who did the calculations that machines now do. Victorian inventor, Charles Babbage, worked as one. It was the inspiration for him to try to build a steam-powered computer. Often, however, it was women who worked as human computers especially in the first half of the 20th century. One was Mary Clem in the 1930s. She worked for Iowa State University’s statistical lab. Despite having no mathematical training and finding maths difficult at school, she found the work fascinating and rose to become the Chief Statistical Clerk. Along the way she devised a simple way to make sure her team didn’t make mistakes.

The start of stats

Big Data, the idea of processing lots of data to turn that data into useful information, is all the rage now, but its origins lie at the start of the 20th century, driven by human computers using early calculating machines. The 1920s marked the birth of statistics as a practical mathematical science. A key idea was that of calculating whether there were correlations between different data sets such as rainfall and crop growth, or holding agricultural fairs and improved farm output. Correlation is the the first step to working out what causes what. it allows scientists to make progress in working out how the world works, and that can then be turned into improved profits by business, or into positive change by governments. It became big business between the wars, with lots of work for statistical labs.

Calculations and cards

Originally, in and before the 19th century, human computers did all the calculations by hand. Then simple calculating machines were invented, so could be used by the human computers to do the basic calculations needed. In 1890 Herman Hollerith invented his Tabulator machine (his company later became computing powerhouse, IBM). The Tabulator machine was originally just a counting machine created for the US census, though later versions could do arithmetic too. The human computers started to use them in their work. The tabulator worked using punch cards, cards that held data in patterns of holes punched in to them. A card representing a person in the census might have a hole punched in one place if they were male, and in a different place if they were female. Then you could count the total number of any property of a person by counting the appropriate holes.

Mary was being more than a computer,
and becoming more like a programmer

Mary’s job ultimately didn’t just involve doing calculations but also involved preparing punch cards for input into the machines (so representing data as different holes on a card). She also had to develop the formulae needed for doing calculations about different tasks. Essentially she was creating simple algorithms for the human computers using the machines to follow, including preparing their input. Her work was therefore moving closer to that of a computer operator and then programmer’s job.

Zero check

She was also responsible for checking calculations to make sure mistakes were not being made in the calculations. If the calculations were wrong the results were worse than useless. Human computers could easily make mistakes in calculations, but even with machines doing calculations it was also possible for the formulae to be wrong or mistakes to be made preparing the punch cards. Today we call this kind of checking of the correctness of programs verification and validation. Since accuracy mattered, this part of he job also mattered. Even today professional programming teams spend far more time checking their code and testing it than writing it.

Mary took the role of checking for mistakes very seriously, and like any modern computational thinker, started to work out better ways of doing it that was more likely to catch mistakes. She was a pioneer in the area of dependable computing. What she came up with was what she called the Zero Check. She realised that the best way to check for mistakes was to do more calculations. For the calculations she was responsible for, she noticed that it was possible to devise an extra calculation, whereby if the other answers (the ones actually needed) have been correctly calculated then the answer to this new calculation is 0. This meant, instead of checking lots of individual calculations with different answers (which is slow and in itself error prone), she could just do this extra calculation. Then, if the answer was not zero she had found a mistake.

A trivial version of this general idea when you are doing a single calculation is to just do it a second time, but in a different way. Rather than checking manually if answers are the same, though, if you have a computer it can subtract the two answers. If there are no mistakes, the answer to this extra check calculation should be 0. All you have to do is to look for zero answers to the extra subtractions. If you are checking lots of answers then, spotting zeros amongst non-zeros is easier for a human than looking for two numbers being the same.

Defensive Programming

This idea of doing extra calculations to help detect errors is a part of defensive programming. Programmers add in extra checking code or “assertions” to their programs to check that values calculated at different points in the program meet expected properties automatically. If they don’t then the program itself can do something about it (issue a warning, or apply a recovery procedure, for example).

A similar idea is also used now to catch errors whenever data is sent over networks. An extra calculation is done on the 1s and 0s being sent and the answer is added on to the end of the message. When the data is received a similar calculation is performed with the answer indicating if the data has been corrupted in transmission. 

A pioneering human computer

Mary Clem was a pioneer as a human computer, realising there could be more to the job than just doing computations. She realised that what mattered was that those computations were correct. Charles Babbages answer to the problem was to try to build a computing machine. Mary’s was to think about how to validate the computation done (whether by a human or a machine).

More on …

Related Magazines …

EPSRC supports this blog through research grant EP/W033615/1. 

Recognising (and addressing) bias in facial recognition tech #BlackHistoryMonth

The five shades used for skin tone emojis

By Jo Brodie and Paul Curzon, Queen Mary University of London

A unit containing four sockets, 2 USB and 2 for a microphone and speakers.
Happy, though surprised, sockets

Some people have a neurological condition called face blindness (also known as ‘prosopagnosia’) which means that they are unable to recognise people, even those they know well – this can include their own face in the mirror! They only know who someone is once they start to speak but until then they can’t be sure who it is. They can certainly detect faces though, but they might struggle to classify them in terms of gender or ethnicity. In general though, most people actually have an exceptionally good ability to detect and recognise faces, so good in fact that we even detect faces when they’re not actually there – this is called pareidolia – perhaps you see a surprised face in this picture of USB sockets below.

How about computers? There is a lot of hype about face recognition technology as a simple solution to help police forces prevent crime, spot terrorists and catch criminals. What could be bad about being able to pick out wanted people automatically from CCTV images, so quickly catch them?

What if facial recognition technology isn’t as good at recognising faces as it has sometimes been claimed to be, though? If the technology is being used in the criminal justice system, and gets the identification wrong, this can cause serious problems for people (see Robert Williams’ story in “Facing up to the problems of recognising faces“).

“An audit of commercial facial-analysis tools
found that dark-skinned faces are misclassified
at a much higher rate than are faces from any
other group. Four years on, the study is shaping
research, regulation and commercial practices.”

The unseen Black faces of AI algorithms
(19 October 2022) Nature

In 2018 Joy Buolamwini and Timnit Gebru shared the results of research they’d done, testing three different commercial facial recognition systems. They found that these systems were much more likely to wrongly classify darker-skinned female faces compared to lighter- or darker-skinned male faces. In other words, the systems were not reliable. (Read more about their research in “The gender shades audit“).

“The findings raise questions about
how today’s neural networks, which …
(look for) patterns in huge data sets,
are trained and evaluated.”

Study finds gender and skin-type bias
in commercial artificial-intelligence systems
(11 February 2018) MIT News

Their work has shown that face recognition systems do have biases and so are not currently at all fit for purpose. There is some good news though. The three companies whose products they studied made changes to improve their facial recognition systems and several US cities have already banned the use of this tech in criminal investigations. More cities are calling for it too and in Europe, the EU are moving closer to banning the use of live face recognition technology in public places. Others, however, are still rolling it out. It is important not just to believe the hype about new technology and make sure we do understand their limitations and risks.

More on

Further reading

More technical articles

• Joy Buolamwini and Timnit Gebru (2018) Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification, Proceedings of Machine Learning Research 81:1-15. [EXTERNAL]
• The unseen Black faces of AI algorithms (19 October 2022) Nature News & Views [EXTERNAL]

See more in ‘Celebrating Diversity in Computing‘

We have free posters to download and some information about the different people who’ve helped make modern computing what it is today.

Screenshot showing the vibrant blue posters on the left and the muted sepia-toned posters on the right

Or click here: Celebrating diversity in computing

EPSRC supports this blog through research grant EP/W033615/1.

The red sock of doom – trying to catch mistakes before they happen

Washing machine mistake

A red sock in with your white clothes wash – guess what happened next? What can you do to prevent it from happening again? Why should a computer scientist care? It turns out that red socks have something to teach us about medical gadgets.

How can we stop red socks from ever turning our clothes pink again? We need a strategy. Here are some possibilities.

  • Don’t wear red socks.
  • Take a ‘how to wash your clothes’ course.
  • Never make mistakes.
  • Get used to pink clothes.

Let’s look at them in turn – will they work?

Don’t wear red socks: That might help but it’s not much use if you like red socks or if you need them to match your outfit. And how would it help when you wear purple, blue or green socks? Perhaps your clothes will just turn green instead.

Take a ‘how to wash your clothes’ course: Training might help: you’d certainly learn that a red sock and white clothes shouldn’t be mixed, you probably did know that anyway, though. It won’t stop you making a similar mistake again.

Never make misteaks: Just never leave a red sock in your white wash. If only! Unfortunately everyone makes mistakes – that’s why we have erasers on pencils and a delete key on computers – this idea just won’t work.

Get used to pink clothes: Maybe, but it’s not ideal. It might not be so great turning up to school in a pink shirt.

What if the problem’s more serious?

We can probably live with pink clothes, but what happens if a similar mistake is made at a hospital? Not socks, but medicines. We know everyone makes mistakes so how do we stop those mistakes from harming patients? Special machines are used in hospitals to pump medicine directly into a patient’s arm, for example, and a nurse needs to tell it how much medicine to give – if the dose is wrong the patient won’t get better, and might even get worse.

What have we learned from our red sock strategies? We can’t stop giving patients medicine and we don’t want to get used to mistakes so our first and fourth strategies won’t work. We can give nurses more training but everyone makes mistakes even when trained, so the third suggestion isn’t good enough either and it doesn’t stop someone else making the same mistake.

We need to stop thinking of mistakes as a problem that people make and instead as a problem that systems thinking can solve. That way we can find solutions that work for everyone. One possibility is to check whether changes to the device might make mistakes less likely in the first place.

Errors? Or arrows?

Most medical machines are controlled with a panel with numbered keys (a number keypad) like on mobile phones, or up and down arrows (an arrow keypad) like you sometimes get on alarm clocks. CHI+MED researchers have been asking questions like: which way is best for entering numbers quickly, but also which is best for entering numbers accurately? They’ve been running experiments where people use different keypads, are timed and their mistakes are recorded. The researchers also track where people are looking while they use the keypads. Another approach has been to create mathematical descriptions of the different keypads and then mathematically explore how bad different errors might be.

It turns out that if you can see the numbers on a keypad in front of you it’s very easy to type them in quickly, though not always correctly! You need to check the display to see if you have actually put in the right ones. Worse, mistakes that are made are often massive – ten times too much or more. The arrow keypads are a little slower to use but because people are already looking at the display (to see what numbers are appearing) they can help nurses be more accurate, not only are fewer mistakes made but those that are made tend to be smaller.

Smart machines help users

A medical device that actively helps users avoid mistakes helps everyone using it (and the patients it’s being used on!). Changing the interface to reduce errors isn’t the only solution though. Modern machines have ‘intelligent drug libraries’ that contain information about the medicines and what sort of doses are likely and safe. Someone might still mistakenly tell the machine to give too high a dose but now it can catch the error and ask the nurse to double-check. That’s like having a washing machine that can spot bright socks in a white wash and that refuses to switch on till it has been removed.

Building machines with a better ability to catch errors (remember, we all make mistakes) and helping users to recover from them easily is much more reliable than trying to get rid of all possible errors by training people. It’s not about avoiding red socks, or errors, but about putting better systems in place to make sure that we find them before we press that big ‘Start’ button.

This story was originally published here and is an article from CS4FN, a free computer science magazine from Queen Mary University of London which is sent to subscribing UK schools. To find out more please visit our About page.

Further reading / watching
You can find a copy of this article on pages 4 and 5 in issue 17 (Machines Making Medicine Safer) of CS4FN 17.

From 50s in this Paddington 2 clip you can see a ‘real world’ example of a red sock getting into the laundry.