Friday, 26 October 2012

Fixing modern healthcare?

(caveat: this post post will be fairly UK-centric, because that's the healthcare system with which I'm familiar!)

Modern healthcare is the victim of its own success.  We've been so successful over the last few decades at developing amazing new medical treatments that healthcare systems can no longer afford to give everyone every single treatment that might help their ailments.  

Now, at some level this is a glass-is-half-empty view of the situation.  Just because we can't afford to use every single viable treatment in every single case doesn't mean we're in a bad way.  Quite the contrary.  We live in an age where people are routinely cured of diseases that were fatal a few short decades ago, and modern treatments improve the lives of millions who would previously simply have to suffer their ongoing conditions.

However, this is no reason to not be ambitious.  We also live in an age of huge innovation, where information technologies such as computers and the internet are radically changing and improving many areas of life.  Why should healthcare be any different?

Here in the UK, the NHS has within it a body called the National Institute for Clinical Excellence (NICE).  Part of NICE's job is to assess the cost effectiveness of different medical treatments.  Because the NHS has finite resources to be shared across the UK population, this is very important - if money is wasted in one area, it's to the detriment of people elsewhere.  Getting the best overall medical return per unit cost is therefore key.  But this begs an interesting question:

What if certain medical costs dropped to zero?

There would be an immediate knock-on effect that the saved money could be used elsewhere in the NHS to treat people (in an insurance-based healthcare system, I suppose what should happen is that insurance costs come down, so more people can afford it).  There's also a more subtle consequence, depending on what the cost in question is - for example, if the cost to test for a given disease drops to zero, we can test everyone for it, as often as we like.  If the disease in question is cancer, say, early detection will also hugely improve survival rates and make the required treatments a lot cheaper (you need a lot less chemo- and radiotherapy if your tumour is small and unmetastasised to begin with).

That would be great, but why on Earth might we expect the cost of anything to drop to zero?  Well, by analogy to what's happened in other industries that have been radically changed by information technologies. The minimum costs to effectively market and distribute books, music and video have dropped essentially to zero - this is why people can self-publish books, release albums without needing a record label, or run entire TV channels over YouTube.  Scientific journals can now be run entirely online, eliminating the need to spend money on marketing or distributing hard copies, leading to new journals that can be run at greatly reduced costs.  And modern search engines let us, for free, efficiently access a reasonable approximation of the sum total of human knowledge.

In medicine, there are a couple of obvious trends that are likely to radically reduce certain costs.  The first is genome sequencing.  The first time we sequenced a human genome, it cost several billion dollars and took a decade and a half.  Currently, it costs about $1000 and takes maybe a week.  Not bad progress considering the (more or less) complete first human genome was only announced 12 years ago!  Post-genomic medicine is taking a little time to arrive, but arriving it is.  And who knows how huge the impact will be when we can routinely sequence the DNA of not only every person, but every pathogen that they have.  Immediately.  For minimal cost.

The second trend is machine learning.  Medical diagnostics and prognostics can be very expensive.  But what if we can use all the easily available information that we have on a given patient (from their history, previous medical notes, and cheap-to-administer measurements) to spot diseases early and to identify which treatments are likely to be most effective?  There may be a whole range of diseases for which we can test at essentially no cost, if we marshal the information appropriately and build effective algorithms to analyse the data.  And many diseases have a range of possible treatments available - what if we can use the information we know about a given patient to accurately predict which treatment will work best for them.   This is personalised medicine, but it could also be very cost-effective medicine.

Of course, this is all pretty speculative.  But maybe it's something on which we should be focusing.  Anything medical that has negligible cost is something from which everyone in the world can benefit, as well as freeing up resources to help elsewhere.  And this is a pretty worthwhile goal.


  1. my first thought is that tests always have risks, whether that's a risk of a test itself eg a blood test could cause a painful bruise, or the risk of getting a false positive result and requiring further tests that are not free/have more serious risks eg the argument against doing PSA tests on everyone because you then have to do lots of prostate biopsies on normal people, which is quite an invasive test... also how about the bayesian thing of pre-test probability affecting how you interpret? eg if you suspect a blood clot in a patient you can do a d-dimer test to stratify the probability. if a patient has low pre-test probability and the result is negative you can exclude a clot, but if the patient has high pre-test probability and a negative test you can't exclude a clot and need to do better tests... I'm not sure if these points apply to your argument as your tests are not "physical" but "informational", what do you think?

  2. Yes absolutely - I think they still apply. Happily, algorithms don't usually leave bruises :-), but things like false positives will be every bit as much an issue as for any other test.

    My speculation is that in the same way physical tests have utility in spite of this, it may also be the case that we can find "informational" (machine learning) approaches that are useful in specific contexts. I'm kind of imagining this as another set of potential tools that can be used in healthcare. They just need identifying and properly testing (in clinical trials) to determine which ones are worth using, in the same way as one would for any "physical" tool.

    The great potential benefit is that algorithms can be very cheap (even free) to run, so in some cases we may be able to develop medically useful tools that cost essentially nothing to use. For example, one might imagine an algorithm that for a given disease uses pre-existing information about the patient to predict how likely different drugs are to be effective in treating them. The treating physician can then use this additional information to give the patient more effective treatment.

    This is certainly a speculative post on my part :-), but I do think there's something to it!