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Mr John Loder Interview

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Dr Tom Foley

Background

John Loder is a senior programme manager at Nesta in the Health and Ageing team within the Innovation Lab. He works on the Centre for Social Action Innovation Fund that aims to use the skills and enthusiasm of citizens to solve the problems generated by long term conditions and an ageing population.

John has a particular interest in the potential of data and information to improve healthcare. He co-authored Doctor Know, a Nesta report looking at new ways of creating and using knowledge in healthcare.   Before joining Nesta, John led the health team at the Young Foundation. His main focus was on innovation in healthcare, including working directly with healthcare innovators, helping health institutions to create an environment for innovation.

Nesta is an independent innovation charity with a mission to help people and organisations bring great ideas to life.  Originally a public body The National Endowment for Science, Technology and the Arts (NESTA) was funded by an endowment from the UK National Lottery in 1998 before becoming an independent charity in 2012.  Nesta is dedicated to supporting ideas that can help improve lives, with activities ranging from early stage investment to in-depth research and practical programmes. (www.nesta.org.uk)

Interview Synopsis

Current Work

One of Nesta’s current projects is “Big PD”.  This focuses on Parkinson’s Disease and the collection of health data through the use of smart phones and wearable technologies.  The aim is to leverage this to improve the self-care and treatment of Parkinson’s Disease.  This project is led by uMotif (www.umotif.com).  Hopefully a similar project focussing on dementia will be developed later in the year, with plans to expand further into mental health as well. 

Knowledge

The pattern of knowledge in medicine is interesting and in some senses very peculiar.  There is very rich detailed knowledge about population average response rates to treatment which is derived from Randomised Control Trials (RCTs).  These are the most rigorously derived knowledge used in day to day practice.  However, we are also very interested in individual variation and how to manage it.  This individualised aspect of medicine is handled by a completely artisan craftsmanship-like approach.  At present medicine is often trial and error, where patients try one drug followed by another until an effective one is found.

Often when thinking about the LHS, people think first about business analytics and business intelligence data and what we can learn from that, but there is likely greater potential around patient generated data.  This is about the personalisation of medicine and when trying to learn at that level, there is a need for very rich data sources.  It is not obvious that the data collected in the course of treatment is currently rich enough to yield statistically firm conclusions.

Future

The potential of the LHS begins with a change in the order of magnitude of the data available.  It is not just what we do with the data that we already have, but also what new data we will be able to record.  The challenge is to determine how best to learn from this and derive meaningful information from the data that can aid decision making.  How to implement this into practice will likely be explored over the next 10-15 years of medical research.

There is a lot to learn.  The personalisation of medicine appears to be the way to improve quality and outcomes.  There is a need to examine what can be learned from wearable technologies and other available devices, how they record and collect data and then to develop analytical tools to be used on these data sources.    For example the Parkinson’s Disease project focusses on accelerometer data to measure when medication is beginning to wear off.  If this accelerometer data is combined with recordings of drug administration times and derive a personalised algorithm, it could be a powerful tool to give advanced notice to patients about when their medication is wearing off.  For a Parkinson’s patient this may be very important and could prevent significant symptoms like rigidity and mobility problems.  

Another more radical approach would be to use this sort of technique to re-explore mental health.  To collect data about symptoms from the diagnostic and statistical manual of mental disorders (DSM), but rather than use the diagnosis from DSM, use the data collected regarding symptoms to re-cluster symptoms and essentially rewrite the DSM. 

Another interesting application could be in the field of physiotherapy.  There is not a lot of evidence to match individual patients with particular exercises that are most likely to work for them.  This could be explored using wearable technologies or Microsoft Kinect.  This could help with performance of the exercises as well as tracking progress on the exercises and providing feedback to the patient and clinician, while generating secondary knowledge. 

It is likely that patients will become more interested in and will begin to coordinate their own data.  This will likely be for self-care purposes rather than to become research subjects.   This will be aided by developments in technology.  For example, asking someone to fill in a medication diary each evening is much less effective than giving them medication reminders, as the patient will see some benefit from this.

This type of application would have to be done in a way that engages the consumer.  There are many examples of health apps that people download but rarely use.  Those with chronic or serious conditions have displayed a much higher use rate of applications designed for them than the general health apps. 

There will likely be a convergence of data held within and out with the healthcare system.  This will impact on the doctor/patient relationship.  How the doctor uses this data will need to be explored.   In some instances doctors are very keen engage with this data.   In general practice for example they may only see a patient for 10 minutes once every 6 months.  In this time, it is difficult to collect all of the relevant information and patients may have forgotten important information.  In this case doctors might be excited to get their hands on useful data to aid the consultation if it was presented to them in a suitable format.  However, some others view the potential for all this information less optimistically and feel that it would be ignored.  Ultimately, it will depend on the usefulness of the data, the degree of statistical rigour that can be derived from it and the existing state of knowledge.  This will be useful for areas of medicine that currently lack significant evidence and where the doctor is under informed.  When these factors are pointing in the same direction we will see useful clinical insights produced.  Hopefully researchers will begin to wake up to the potential from this and in 5-10 years’ time we will be at a stage where this data will be used more effectively.   This is just starting, but it is starting very quickly.

 



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