Jan 26 2010
This concept may bring new meaning to the phrase “Doc in a box” (used to refer to small walk-in clinics). Increasingly computers are infiltrating the practice of medicine – but to what extent have or will computers replace the cognitive work of trained health professionals? This is a concept I have been following with interest, but at the moment there is probably much less in the way of computer-assisted medicine than the the public imagines.
A news story today reminded me of the baby-steps that are being taken in the direction of AI (artificial intelligence) medicine. A new study shows that a computer program is as good or better at making a specific measurement (wear of the meniscus) on MRIs of the knee – but only when they are mild to moderately damaged, severe damage still requires a human eye.
This kind of thing is definitely the low-hanging-fruit for medical AI systems – interpreting digital images. It does not require making a diagnosis, weighing choices, or interpreting human input. It is simply using pattern recognition to measure one feature of an image.
But even with this application, computers have a hard time competing with humans. People are very good at pattern recognition – something we still do far better than the most advanced computer. My own experience with this in medicine is in doing nerve conduction studies which measures the electrical response of an impulse sent through nerves to measure their function. The output is a simple waveform, and the computer program is tasked with suggesting where to mark the beginning, peak, and end of the curve. Sounds simple enough, an yet I almost always have to tweak the computer’s markings, and if there is any noise in the waveform the computer can be way off, sometimes even marking a bit of noise that is not even the nerve response.
What this program does well is making all the calculations – and for some types of studies even very complex calculations. But of course, that is a computer’s sweet spot. Pattern recognition – not so much.
What I find interesting is thinking about what computers do well vs what people do well, and therefore where and how best to integrate computer systems into the practice of medicine.
Simply handling large volumes of information (so-called medical informatics) is primarily the current role of computers in medicine. The best systems do this well, but in actuality they are only as good as the implementation – meaning the way in which a hospital or practice customizes, uses, maintains, and supports the application. For accessing and documenting medical records, lab results, viewing digital images, etc. computers are awesome and I would never go back.
But even here, the state-of-the-art of electronic medical record systems and their implementation is about a decade behind where it potentially could be. The reason for this, I think, is that implementation is very difficult, and requires high-level communication between medical experts and computer experts. Computer experts do not know what physicians and nurses really need, in terms of information and workflow. And medical experts do not necessarily know what computer systems can do for them, or how to communicate their needs in ways that translate to the software developers.
The process is slowly inching forward, mainly due to the efforts of computer savvy medical experts who can bridge the gap. But it is frustratingly slow.
What about reading digital images, like in the current news story? That is probably one of the next areas ripe for development. As I said, I don’t think computers will replace humans anytime soon for such readings, but they can provide an excellent assist. What computers do well (and humans do not do well) is provide consistent thorough attention. Every radiologists nightmare is having their attention waver for just a moment, and missing that small detail that has significant implications for the care of a patient. Perfect vigilance is hard. In fact all of medicine is plagued by the occasional lapse of vigilance and attention to detail, leading to medical mistakes. We have increasingly put systems into place to minimize such mistakes – but all we can do is minimize them, not eliminate them.
However, computer assistance can provide the vigilance and systematic attention to detail that humans find so challenging. I can envision the development of sophisticated algorithms, for example, that can systematically read X-rays and make precise measurements. The results can then be presented to a human radiologist, who can filter out the noise and false positives, and provide the context and pattern recognition that humans are good at. This way we get the best of both worlds – human and machine.
This type of approach is not new – such systems are called expert systems, as they provide an assist to experts but do not try to replace them. There are many areas of medicine where this can be helpful. For example: flagging abnormal lab results and bringing them to the attention of the ordering physician; checking for drug-drug interactions or allergies whenever a new drug is prescribed (assuming that the patient’s medication list is up to date); suggesting possible diagnoses from a list of signs and symptoms (maybe suggesting something the physician did not think of); or reminding physicians of standards of care (imagine this in a Hal-like computer voice – “your patient is over 55 and has hypertension and diabetes; would you like to prescribe an anti-platelet agent for vascular prophylaxis?”)
In short, there is tremendous potential for computers to function as assistants in the practice of medicine – reducing error, improving adherence to standards of care, and giving physicians access to the information they need when they need it. I do not think we are fully exploiting this potential, however, and while we are considering ways to improve the practice of medicine to reduce errors and improve cost effectiveness, this is an area that seems worthy of investment.
But we are nowhere near computers actually practicing medicine. For the foreseeable future the practice of medicine will be a partnership between person and machine – each doing what they do best.
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