Living, and dying, by algorithms
by Karl D. Stephan | May 28, 2018
The National Health Service (NHS) in England is one of the oldest government health-care systems in the world, founded in 1948 when the Labor Party was in power. Despite consuming some 30 percent of the public service budget, by many accounts it is underfunded, especially when it comes to capital equipment such as IT systems.
This may be a factor in a scandal involving a wayward algorithm that prevented some half-million Englishwomen from receiving mammograms for the last nine years. Estimates vary as to how serious a problem this is, but it's likely that at least a few women have lost their lives due to breast cancer that was caught too late as a result of this computer error.
A report carried in the IEEE's "Risk Factor" blog describes how in 2009, an algorithm designed to schedule older women for breast cancer screening was set up incorrectly. As a result, over the next nine years almost 500,000 women aged 68 to 71 were not allowed to have mammograms that they otherwise would have been scheduled for. When the error was caught, the news media had a field day with headlines like "Condemned to Death . . . by an NHS Computer." Depending on who's making the statistical estimate, the consequences are either tragic or possibly beneficial.
The NHS's own Health Minister had his statisticians run the numbers, and they came up with a range of 135 to 270 women who may have died as a result of this error. But others claim that as many as 800 women may be better off because of not having to go through surgical and other procedures based on the false positives that inevitably result from a large number of mammograms.
While the actual consequences of this problem are ambivalent, it raises a larger issue: what should we do when computer-generated algorithms that affect the fates of thousands go awry?
As a practical matter, computer algorithms are part of the fabric of modern industrial society now. If you want to borrow money, the bank uses algorithms to decide whether you're a good credit risk. If you look for something online, sophisticated algorithms take note of it and decide what other kinds of ads you see. And if you're in England or another country where health care is allocated by a central computerized authority, a computer is going to tell you when you can get certain kinds of preventive health care and if you're ill, it may even tell you when you can get treated—if at all.
From a utilitarian engineering perspective, computer algorithms are the ideal solution for large-scale resource-allocation problems. Health care these days is very complicated. Each person has a unique combination of health history, genetic makeup, and needs, and the arsenal of treatments is constantly changing too. If you are working in an environment of centralized fixed resources (as NHS is), then you will naturally turn to computers as a way of implementing policies that can be shown mathematically to treat everyone equally. Unless they don't, of course, as happened with the older women who were left out of mammogram screenings by the badly programmed algorithm.
There's an old saying, "To err is human, but to screw up royally requires a computer." The NHS flap is a good example of how one mistake can affect thousands or millions when multiplied by the power of a large system.
The US, with its much more hodgepodge mixture of private, commercial, and government health care systems, is still not immune from such errors, but because the federal government doesn't run the whole show, its mistakes are somewhat limited in extent. There are also numerous outside agents keeping tabs on things, so that an egregious error by, say Medicare, comparable to what happened with the NHS algorithm in England, would probably be caught by private insurers before it got too far. Just as a power grid with a number of small distributed generating stations is more robust than one that relies exclusively on one giant power plant, the US health care system, even with all its flaws, is less likely to be felled by a single coding mistake.
Instead, we have widely distributed minor errors that cause more inconvenience than tragedy. But precisely because the system is so kludged together, it doesn't take much to cause a problem.
Here's a simple example: my wife is scheduled the day I am writing this for a routine well-person exam that requires her general practitioner (GP) to write a referral for it. Dutifully organized person that she is, several weeks ago she went by her doctor's office and asked them to do the referral so she could schedule the appointment, and the staff at the office said they'd take care of it. Yesterday (the day before the procedure), she got a call from the office that was going to do the procedure, saying they hadn't gotten the referral yet and if they didn't get it they were going to cancel the procedure or make us pay cash for it.
So ensued a half-hour or so of near panic, during which time we ran down to her doctor's office and discovered that the lady who was supposed to send the referral out had quit the previous day. And that was one of the things she left undone.
When the GP's office staff figured out what had happened, they were very nice about it—they faxed the referral to the proper office, handed us a copy which we carried over by hand to the office needing it, and everything is fine now. But until all medical offices are staffed by robots and all paperwork is untouched by human hands, people will always be involved in medical care, and people sometimes make mistakes.
Personally, I much prefer a system in which I can drive over to the office where the mistake was made and talk to the people responsible. If we had something like the NHS here, the mistake might have been made in Crystal City, Virginia by an anonymous person whom it would take the FBI to discover, and my wife would have been out of luck.
Karl D. Stephan is a professor of electrical engineering at Texas State University in San Marcos, Texas. This article has been republished, with permission, from his blog Engineering Ethics, which is a MercatorNet partner site. His ebook Ethical and Otherwise: Engineering In the Headlines is available in Kindle format and also in the iTunes store.