It is very disheartening for me to listen to the political discussions surrounding health care. I can’t help thinking that the proposed changes amount to rearranging the furniture on the deck of the Titanic.
This is not to say that there aren’t important policy decisions at stake. It is important that everyone has health coverage, as a matter of efficiency and just compassion. However, the health care debate is often framed as an attempt to reduce health care costs. This is where the rearranging the deck furniture metaphor is apt.
There are some minor efficiencies to be gained in how we pay for health care, but that is not going to touch the real driver of rising health care costs – technology. Of course there are multiple factors, but the main one is the fact that we have the technology to deliver more care and more expensive care, and to keep people alive longer. This is combined with a culture that demands expensive care. We want MRI scans for every ailment, and the most aggressive procedures to keep our loved ones going for as long as possible.
More care and higher tech care costs more money.
Fixing those issues, however, is going to require tough decisions and a change in culture. Meanwhile we do need to pick the low hanging fruit, even if it is the smaller portion of rising health care costs. The low hanging fruit consists of the win-wins – changes that lead to better outcomes, cheaper care, and everyone is happy.
Medical Decision-Making
One area that everyone agrees needs to improve is medical decision-making. This consists of making the best diagnosis as early as possible, ordering just those tests that are necessary, and prescribing the optimal treatment. Of course, all physicians already aspire to this ideal. The problem is, it’s really difficult. Medicine is complex and getting more complicated as our knowledge and options increase.
The traditional method for dealing with the complexity of medicine is training, and this will always be necessary. But training and education have their limits, and it seems that we are pushing up against those limits. Further, as you engage in error reduction you get diminishing returns. It takes more and more effort to make smaller and smaller incremental improvements.
Because of our technology, errors and delays are getting more costly.
While I do think we need to optimize medical education and training, it is also clear we need other methods to optimize care and reduce error. Atul Gawande wrote about this in his excellent book, The Checklist Manifesto. He advises using systems, like checklists, to reduce error and improve quality of care. A checklist is essentially an external aid that professionals (not just doctors) can use to reduce error. Checklists work.
There is another external aid that has the potential to have an even more profound effect on error reduction and care optimization than checklists, but has yet to come into its own – expert computer systems. These are software algorithms that can, for example, take a list of signs and symptoms and suggest possible diagnoses.
The latest issue of Scientific American has an excellent article on these systems, which is worth a read. I will expand upon their main points here.
Essentially physicians engage in two types of thinking when it comes to diagnosis and treatment – intuitive and analytical. Intuitive thinking is experience based and involves a great deal of pattern recognition. The advantage of intuitive thinking is that it is fast and can incorporate a great deal of information. The disadvantage is that it is based upon the quirky (not necessarily representative) experience of the physician, and is subject to a host of cognitive biases.
Every heuristic I have discussed here and elsewhere comes into play when thinking about patients. This is why physicians (and again, any professional) needs to be aware of critical thinking, biases, heuristics, logical fallacies, and the fallacy of memory and perceptions and take them into consideration when making medical decisions.
Analytical thinking is slower and more deliberate, but has the advantage of accounting for specific bits of information in a rigorous statistical manner. Analytical thinking in medicine is critical, but it is very difficult. It involves remembering or having access to a mountain of statistical data and knowing how to properly crunch the numbers.
The ideal clinician blends intuitive thinking and analytical thinking to take advantage of the best of both.
Expert computer systems essentially are a tool for analytical thinking, which is something that computers do much better than humans. Humans are better at intuitive thinking – recognizing a disease by the subtle way a patient looks and moves, for example. Intuitive thinking is also critical for interpreting a patient’s symptoms. Patients don’t complain to their doctors that they have appendicular ataxia. They report what they experience, and that has to be translated into medial phenomena. Further, there is a personality and cultural layer involved. How significant is a symptom, how new is it, etc. ? Because patients are people, we won’t be removing the human element of health care anytime soon.
But computers potentially kick ass at that analytical evaluation. They can sift through thousands of factors, and know what their statistical influence is on possible diagnoses. They will know what tests are necessary, and what treatments are likely to have the best outcomes. This information can then be filtered through the intuitive and personalized evaluation of the physician.
To give you an example of how powerful the analytical approach can be, when coupled with large amounts of data, play with the Akinator. With a surprisingly few questions, some of which may seem arbitrary, this computer algorithm can guess just about any character you are thinking of. It essentially takes an analytical approach to 20 questions, and is better than any human.
Now imagine playing this game except for diseases.
There are applications already in existence, DXplain, VisualDx and Isabel. They work, and studies have shown they improve diagnostic accuracy for rare or obscure diseases. For everyday common diseases, there is less room for improvement. However, even in those situations an expert system may optimize treatment, by, for example, recommending the optimal medication.
Right now we essentially are having an infrastructure issue. These systems need to get better and more comprehensive, but mainly they need to be available at every point of patient care. Further, use of expert systems needs to be incorporated more into medical training.
I would like to see such systems everywhere and routinely used. We should already be at this point, in fact. They we can explore further ways to exploit these systems to optimize care and reduce health care costs.
Again – we do need to improve how we pay for health care to optimize access and reduce waste and inefficiency. But this is small potatoes compared to optimizing the practice of medicine itself. Expert systems, when fully realized and integrated into medical practice, have a far greater potential for improving outcomes and reducing costs.
This is an infrastructure investment worth making.
