Category Archives: Miscellaneous

The Amazing Growth Of Citizen Medicine

by Dr. Adrian Raudaschl

There is a feeling that researchers, patients and healthcare providers are growing increasingly unhappy with the state of scientific and medical research  (10, 11).

Patient groups like Alzheimer’s Society go as far as to use member donations to fund their own research and leverage internal expertise to help speed up the development of new treatments 1. This is a twist on the conventions of medical science, and arises out of frustration of the lack of attention and funding for certain medical conditions like dementia  (12).

Combine this trend with a decrease in new drug discoveries, the rising costs of medication, a decreasing cost of scientific equipment/services, open access to scientific literature and I get the feeling a revolution in how patients and organisations engage with healthcare is coming.

Frustrations of patient relations to pharma & difficulties in drug discovery

Pharmaceutical companies have not been getting best press these days 9. It feels as if public perception of the industry has been souring for a while 8. An infamous example of this was last year’s outrage over Mylan increasing the price of the EpiPen by over 500 percent – much to the opposition of the company’s own employees, regulators, patients, politicians and the press 2. Of course they are not the only ones, and according to to Credit Suisse, list prices for prescription drugs across the drug industry rose 9.8% in 2016 which played a critical role in drug companies growth last year.

Developing a new drug today costs more than $2.8 Billion.

In this situation, pharmaceutical companies may need to overprice their very few successful drugs to compensate for the R&D failures of their portfolios. What we are left with is a marketplace of expensive medications, and stagnating medical innovation.

New rise of citizen science

Science is not just for scientists these days. Through the bulk of scientific activity takes place in commercial enterprises, government laboratories and universities there have always been people who have done their own scientific research. People who haven’t been employed by an institution or a firm. You could argue that Charles Darwin was such a person 13.

Nervous about possible pollution from a nearby road? Set up an Arduino powered nitrous oxide sensor. Want accurate feedback about glucose levels related to diet? Hack a glucose monitor to turn it into a continuous monitoring device and share your data online.

Technology can make scientists of us all. Data churned out by consumer gadgets equipped with satellite navigation, cameras, biometrics and other sensors have great potential to drive a boom in citizen science. Initiatives such as the EU Open Science policy aim to increase our access to personal data even further, so in future patients may even be able to access their medical test results and contextualise them on a timeline. In medicine, organisations like Findacure and Raremark aim to consolidate medical data from multiple patients to help inform treatment strategies and research.

Looking more to the fringes however, some people are taking this concept further and leveraging open access scientific research and cheaper equipment to start their own medical projects.

Just as hobbyists in the 1980s found new uses for home computers, so amateur biohackers are now experimenting with the tools of biotechnology such as the London Biohackspace. Though it’s a far cry from a professional biotech lab, it sends a clear message – motivated people can learn to bioengineer, experiment and manipulate biological entities without a university degree or expensive equipment.

This motivation is driven by the changes described above of the increasing difficulties many people have in accessing cutting edge medicine. This is interesting, because it’s not hard for me to imagine a group of motivated individuals, armed with knowledge and equipment to start taking on more ambitious projects in the world of healthcare.

Citizen Medicine

How does citizen medicine manifest itself? An example is the response to the price hike of EpiPens last year. One group (Four Thieves Vinegar) released instructions and videos on how anyone could take a cheap off-the-shelf needle injector made for diabetics, and combine it with a syringe that can be preloaded with a $1 dose of epinephrine. They called it the EpiPencil, and it costs $35 to construct – a fraction of Mylan’s $600 brand name EpiPen.

The group says their mission is not about medicating necessarily, but about “empowering people, in sharing information” and enabling people “to talk about alternatives to expensive medication regimens” 6. Some believe the EpiPencil effort contributed to Mylan releasing a cheaper generic version of their pen soon after, as well as a few companies launching their own cheaper versions.

This is a good case study of how market pricing can motivate private citizens to protest in unconventional ways.

It may seem unusual to us, but remember that pharmaceutical piracy is not uncommon in countries where medications are unaffordable by the majority of the population 7.

Four Thieves Vinegar and other groups like it are also working on reverse engineering medications such as Pyrimethamine (AIDS, malaria and cancer) and Mifepristone (abortion) 6.


If people are starting to experiment with pharmaceutical synthesis there is always a high risk of contamination, sub-potency, super-potency or improper dosing with anything synthesised. It should come as no surprise that regulatory bodies have expressed disapproval over medical projects such as described above, and with good reason – people’s lives may be at risk.

Though groups likes Four Thieves Vinegar supports FDA safety reviews and clinical trial tests for new drugs, their position is that they are simply providing knowledge, and it’s up to individuals to do with that information what they wish.

The Future

We are seeing the emergence of a new subgroup of individuals who are taking citizen science further and are leveraging passion, open access to medical knowledge and equipment to take control of their problems in research and healthcare.

When a parent (Terry) discovered her children had been born with a rare genetic disease called pseudoxanthoma elasticum she said “We [as parents] look at things differently. We look at what matters to us, and not some biological pathway that absolutely is important but isn’t going to give us the answers we need right away.” 11. Terry and her husband set out and borrowed a lab bench at Harvard University and set about tracking down the gene responsible for their children’s connective-tissue disease. With no science background it took them a couple of years, but remarkably, they did find the gene.

Though I don’t approve or advocate the human use of unofficially synthesised medications or medical devices, the knowledge and skills these patient/public communities have obtained to achieve these goals has great potential for good in the world.

In the same way that the first home computers and web services were developed by enthusiasts and hackers, I wonder if we will see a similar trend in medicine with a new generation of regulated biotech startups, public laboratories and pharmaceutical companies. The world clearly does not have a shortage of health problems, and some fresh perspective in an industry with few established players might be in everyone’s interest.



  1. Alzheimers Society – Current Projects. Accessed 18/06/17
  2. Outcry Over EpiPen Prices Hasn’t Made Them Lower. The New York Times. Accessed 18/06/17
  3. Diagnosing the decline in pharmaceutical R&D efficiency. Nature Reviews Drug Discovery 11, 191-200 (March 2012) | doi:10.1038/nrd3681.
  4. Jacob Glanville. Accessed 18/06/17
  5. Sharon Terry. Accessed 18/06/17
  6. Was the EpiPen Hack Ethical?. Accessed 19/06/17
  7. USTR: 97% of Counterfeit Drugs in US Shipped From Four Countries . Accessed 25/06/17
  8. The public’s view of pharma just keeps getting worse. Accessed 25/06/17
  9. Pharma’s Reputation Continues to Suffer — What Can Be Done To Fix It?. Accessed 25/06/17
  10. Young, talented and fed-up: scientists tell their stories. Nature. Accessed 09/07/17
  11. Patients Increasingly Influence The Direction Of Medical Research. NPR. Accessed 09/07/17
  12. Ensuring the future of dementia research. Alzheimers Society. Accessed 09/07/17
  13. Darwin Online. Accessed 09/07/17



Sharon Terry with a background in theology, whose children were diagnosed with pseudoxanthoma elasticum (PXE) in 1994, became a researcher and data-sharing advocate. Her name is now on more than 140 scientific papers. With her husband, she discovered the ABCC6 gene that was responsible for her children’s illness 5.


Jacob Glanville – a ex-Pfizer scientist who left his job to pursue the creation of a ‘universal flu vaccine’ 4. Jacob developed his knowledge of sequencing, protein engineering, immunology, and algorithm development to create a vaccine from his lab in Guatemala. Though the focus is currently to develop a vaccine for pigs, Jacob hopes to use his research and profits to develop a human vaccine in future.

NHS data feeding frenzy is in progress

A data feeding frenzy is happening in the NHS right now as  Artificial intelligence (AI)  technology companies scramble for access to NHS data.

Driven by the  wide ranging potential for AI to improve healthcare – from checking laboratory results, to bed management –  Artificial intelligence (AI) in the medical space has skyrocketed to the 3rd most active sector in the AI startup space.

But developing AI and machine learning products is not the same as creating more traditional technology products, you cannot just create one by writing some code. This is known as the “cold start” challenge because AI algorithms have to be trained up on masses of data before they can produce any useful insights, and like most things they are only as good as the quality of the data fed into them.

Therefore, if you want to create a high quality AI product for the healthcare market you need lots of high quality medical data. In this context the holy grail of data is the non-public patient data sets held in the UK’s NHS, as it is the largest single data source of its kind anywhere in the world. This is the reason for the data feeding frenzy, as technology companies desperate to get access to this valuable data repository are being very proactive in looking for ways to partner with the NHS.

On the face of it this is a good thing. Partnerships using NHS data to enable machine learning have led to fantastic clinical outcomes. A good example of this is the collaboration between Google’s Deep Mind and Moorfields Eye Hospital.  

But I am worried. Once an AI product has been trained up on NHS data, the company that developed it can sell this product in the wider market and make a profit. There’s nothing wrong with that per se, but I am not aware of any instances of these longer term profits being shared with the NHS, without which the product may never have been developed.

So instead of short term partnerships focused solely on delivering  clinical value, how can the NHS create longer term revenue from the intellectual property (IP) created by partnerships with technology companies?

In my opinion the best way to leverage this opportunity is to continue the partnerships, but ensure they are  underpinned by a licensing model that agrees up front the percentage of the product’s lifetime revenue the NHS should be granted as a fair reflection of its data contribution.

This sort of model is achievable but it does require a shift in the way the NHS deals with these companies which needs to be supported by an enhancement of staff skills,  or the risk is that down the line the NHS has to pay to use the new AI products it helped create.


Esther O’Sullivan is Head of Digital Strategy for BMJ. She is a specialist in impact of digital transformation on Healthcare and Academic publishing and an expert in understanding where opportunities from these transformations can be strategically applied.


What makes machine learning in healthcare so powerful?

A revolution in healthcare is coming, and it is going to fundamentally change the way we practice and think about medicine.

Ask yourself — what if before you even saw a physician your medical history, blood test results, presenting symptoms, medications over the last year and thousands of other data points had been processed to give a list of potential diagnoses and a recommended course of treatment? Your visit to the doctor could be more personalised, faster, easier, accurate and more focused on your needs.

That is just one of the promises of machine learning (ML) – a subset of artificial intelligence which no longer exists in the realm of science fiction, but is with us right now.

You don’t have to look far for examples – Google Deepmind, IBM Watson and Babylon Health are just a few of the commercial players in the field at the moment, not to mention the countless others leveraging machine learning for business and research (more examples at the end).

So how do they work?


With ML, you give the machine the data and ask it to learn the rules. For example, you could say – “I’m going to show you a bunch of people who had heart attacks, and a bunch who didn’t. Now learn how to tell them apart.”

Machine learning works by taking a dataset of examples labeled with correct predictions. Using this, it “learns” the relationships between the data and the predicted output. Once the machine has reviewed a million patients, you can show it information about a patient it’s never seen before and let it predict whether they may be at imminent risk of a heart attack.

Impact on Healthcare

One unsettling aspect about machine learning from a physician’s perspective is that you can’t actually see the logic the computer has used to reach a conclusion. That can be a challenging mental obstacle, but not an unfamiliar one in medical research. For example – think about the discovery of steroids for immunosuppression. It begins with a very pragmatic observation of, “Oh, this thing works,” and then we tend to backfill our understanding of why it works. That will be the model for a lot of ML applications.

I mentioned before that machine learning is with us right now. So why is it not already widely being used? There are currently two main blockers .

First, the way we store patient data is fragmented, difficult to extract and difficult to contextualise. For example, if you want to know when someone died it can be difficult to piece together if they went home or to another hospital that is not part of your health system. It is possible to overcome, but it takes time and potentially needs new processes.

The bigger obstacle however, is finding a way to take our prediction algorithms and use them safely and responsibly in real world applications that don’t endanger patient lives.

Though the technology for applying machine learning to things like diagnostics exists, it is still really a toy example of what the technology can achieve. The most sophisticated ML algorithm can’t look at a sick child and decide whether they need emergency intubation or whether they can be discharged with conservative therapy. Healthcare professionals synthesise huge amounts of information within milliseconds, often just by “eyeballing” a patient.

The Human Body X-ray Anatomy People Human Skeleton

Even in more direct applications like radiology or pathology where ML can be put to use to interpret CT’s and peripheral blood smears, replacing physicians with software is a long way off. Collecting labeled data for just a single application is extremely time-consuming and expensive. Moreover, datasets require a universal standards to be defined, but gold standards in medicine are often ambiguous.

I believe the goal of machine learning in healthcare will be to help with patient management and logistics. Examples like bed management, heating management and theatre utilisation can help free up time for healthcare professionals to do the most important part of their job – communicate with patients.

We don’t need an AI to replace physician’s – rather we need an AI that serves as a clinical aid part of the clinician’s toolbox for treating patients. The opportunity for machine learning in the medical field lies in logistical patient management, predicting readmissions, triaging patients, auto-populating order sets and any process that could require the interpretation of large trustworthy datasets to provide diagnostic assistance.

The biggest thing I want to emphasise is that it will be clinicians, NOT engineers who are going to push forward the innovations in this field. Right now the limitation is data.

The algorithms used in machine learning are all publicly available, work very well, and can be applied with a moderate programming background. Tensorflow, Theano and Touch are just a few of the technologies in use (learn more). What engineers lack is access to large, high-quality datasets to use them with, and that’s where healthcare professionals can step in.

Written by Dr Adrian Raudaschl

Interesting Recent Research

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs – JAMA Network, 2016


Dr Adrian Raudaschl is a medical doctor turned product manager. While working in the NHS (National Health Service) he created apps and games to help patients learn more about medical conditions. This demonstrated to Adrian the power great tools have to help people in times when they need it most. Currently he works with children, parents and healthcare professionals to create exciting medical apps and games, which both educate and delight users. Dr Raudaschl is a firm believer in the accessibility of medical information for everyone.

If you search then you will find (Part 2)

How clever is search today?

There was a time when search just provided search results but today searches provide you with answers and even options to help you make a decision. This however, is not a new concept and even as far back as 1996 AskJeeves saw the potential of a digital assistant.

Here’s a few examples of what Google knows about me, my environment and even what it knows about the future: Continue reading If you search then you will find (Part 2)

BMJ switched to Google Apps around a year ago and the results are just amazing.

BMJ switched to Google Apps around a year ago and the results are just amazing.

We left the environment of shared drives, Microsoft Office 2007 and Lotus Notes where ‘file in use’ was a common greeting in the morning followed by “Your mail file has exceeded the size threshold. You should delete messages and compact your mail file” . Continue reading BMJ switched to Google Apps around a year ago and the results are just amazing.