Speeding up time-to-clinic

One thing I'm struck by, the more I work on medical projects, is how long everything takes.  Now, partly this is a a reflection on tempo of academic research, and partly it's that these projects are challenging.  But, frustratingly, in many cases there isn't a good reason - could often happen much faster, if we put our minds to it.

That begs the question of how we can more rapidly translate medical research into the clinic, where it can actually help someone.

One major limiting factor is the time it takes to get the resources to get a new project off the ground. If you need grant funding, the proposal itself can take months to put together, especially if you need to generate pilot data.  Then the process of your submitted proposal being assessed will take months more, with the associated chance of rejection in most cases.  And by the time a funded project gets started, hires the relevant people etc, over a year will have passed since you were first in principle ready to get started.   There are now funding streams which try to speed things up, but more are needed.

This couples to another problem, that medical research is often very expensive.  It's hard for funders to dole out a research grant quickly if it's for millions of pounds.  They have a responsibility to the public/their donors/etc to ensure the money is used wisely.  It is much easier for a funder to fund compact projects costing £100k than it is to dole out multi-million pound grants.  Cheaper ways to do the research would help accelerate this process.

Then there's the research project itself.  This can take a lot of time for all sorts of reasons.  And I'm afraid that academics are often very bad at focusing on getting stuff done promptly.  This is a mistake if one is concerned with maximising the value-per-unit-time one creates.  At every stage, delays creep in because people are busy etc.  If it takes a month to arrange a telecon to discuss something, that's 8% of a year wasted.  Such delays add up.  And I think academia can lack urgency, which in medical research is another mistake.  If one aspires to develop something that can save 100 lives a year, a delay of 12 months has killed 100 people...

And even once all of the above is dealt with, we may well not even have begun the process of translation.  There needs to be evidence that the research outcomes will work in the clinic (as opposed to a research lab), there needs to be a way of turning the research into a product that can then be deployed (e.g. commercialisation).  Perhaps this even needs to be built into the research project from the word go, so the definition of a successful project is that something new has been deployed to the clinic.  If all you've done is draw some conclusions and write a paper, maybe who cares?

Every single one of these steps is slow, ponderous, and flaky.  What exactly a better system looks like isn't (yet) clear, but  I'm pretty sure we want something a lot closer to an exponential organisation.

So, what should we do about all this?

Well, even a simple consideration of process optimisation would help.  For example:

1.  Remove all unnecessary steps.  If there is some funding already in place (e.g. for pilot studies), the whole grant-application step can sometimes be avoided.  If infrastructure is already in place (e.g. for data acquisition, sample collection), there is no need to have to build it.

2.  Parallelise as many steps as possible.  If we have a hundred candidate biomarkers for a disease, why not set up a project that can systematically test them all at the same time.  And for clinical trials, multi-arm, multi-stage trials end up being hugely efficient in terms of treatments tested per unit time.

3.  Make each remaining step as fast as possible.

How hard can it be...?

Translational Medicine with Data Science

I've been thinking a lot recently about how to use all the medical data that are being generated to actually help patients.  We can (and do) measure a lot of medically-relevant things about a given patient, but that doesn't automatically lead to an improvement in care or outcome.

So, the question is:  What are the challenges to using data science to improve medical treatment?

Small Data

Many medical data sets end up  being small to the point of being underpowered.  Reasons for this include the cost of collecting samples and making the measurements, and practical considerations of recruiting patients to the study.  This misses a trick, both because they can be underpowered, and also because of the 'unreasonable effectiveness of data' (the observation that larger training sets can lead to significant improvement in predictive ability).  This is even more of an issue with many new data types, many of which are very high-dimensional.

The Right Patient Cohort?

Does the cohort of your training data match the cohort of people who you'll actually want to help, clinically?  The answer to this is often 'no', and this may seriously affect the generalisation of your model. Is it possible to use transfer learning to help offset this effect?

NHS-Quality Data?

Data from research labs is not enough!  The quality can be too low, and will typically not be certified to NHS standards.  Trust in data quality is vital, but also your clever data science method will not be
allowed into clinical practice if the data aren't NHS-certified.

Changes in Measurement Technology

We are now in an era where medical measurement technologies are developing very rapidly.  For example, if you have a method for cancer diagnosis that uses next-generation sequencing technology, will it still work with whatever platform is being used in 5 years time?  We need to learn how to future-proof, as much as possible.

Do You Trust Your Models?

If one is using a data science method to help treat someone medically, the price for the method failing could be very high (possibly even death). Therefore, do you *really* trust your model?  Is the parameter fitting robust?  It the model robust to poor data (outliers etc).

Do You Trust Your Code?

Similarly, do you trust your code?  Really?  Because a bug in your code in this context could kill someone.  There are other disciplines that have expertise in this kind of thing (e.g. control systems for
aircraft), but this is a huge challenge for medical data science.

Data Integration

An open methodological question:  how best to combine multiple sources of information, in e.g. statistically principled ways.  Medicine is now routinely producing multiple data types for many patients, so we need reliable (automated) ways to get the most out of these.

Covariate Drift

Our goalposts will move over time.  For example, if we're screening for the early detection of a disease, the more successful we are, the more the at-risk cohort will change over time.  Can our models adapt to account for this?

Ongoing Data Aggregation

As we use a medical data science system, it will aggregate more data (i.e. old test examples can subsequently be added to the training set). Can we use these data to improve e.g. the predictive ability of our models?

Are You Solving The Right Problems?

Are we solving problems that will actually improve clinical practice. It's all very well to develop a statistical model that can predict which cancer patients will die sooner, but if one has no treatments
available which can change this, there is no value to the prediction.


There are a lot of challenges that we've barely begun to address, when it comes to the idea of getting data science methods into clinical practice.  But at the same time, there are so many potential benefits
that it is well worth the effort.  There's much to be done...