All posts by Esther O'Sullivan

WhatsApp in the NHS – Framing the problem

By  Joel Schamroth and Lucinda Scharff

With 1960s technology the status quo for communication in hospitals, it is no surprise that the NHS has a WhatsApp problem. The recent article by O’Sullivan and colleagues (1) published by the BMJ further emphasises the point. Instant messenger use is widespread and deeply ingrained in the workings of the modern NHS.

 Our own UK wide data supports that of our Irish colleagues. Gathering data from over 60 trusts we found that 91.9% of doctors surveyed reported using some form of external instant messaging app at work. More importantly 83.3% had sent or received an instant message containing patient identifiable data (PID).

 Headlines about ‘rampant use of WhatsApp’ will garner clicks and attention, but this needs further examination. Discussing ‘clinical information’ is a broad term, which must be unpacked if we are to understand how WhatsApp is being used, when this is inappropriate and how we provide clinicians with solutions.

 In many cases, WhatsApp simply replaces the informal verbal communications within teams. It’s no secret that hospitals have become increasingly busy (2), with teams often spread across multiple wards. Decisions must be made at a faster rate, with colleagues who are not physically present. The minutes saved by sending a quick message to a colleague asking whether the CT has been done yet are invaluable, when one considers that the alternative requires finding a landline, sending the bleep and interrupting their workflow. These communications would never have been documented in the patient record, and whilst they relate to a particular patient, do not need to contain PID. However the anonymisation involved – initials, bed numbers, used by doctors on instant messaging services means there is room for error.

 WhatsApp becomes significantly problematic when PID is in play. Here, WhatsApp encroaches on the existing formal communication systems where record keeping and a paper trail are deemed essential. The same motivation for WhatsApp use, speed, is again often critical. There are numerous anecdotal incidences of patients being transferred for urgent Primary Percutaneous Coronary Intervention based on WhatsApp images of ECGs, because doing so is faster and more immediately accessible compared to faxing or emailing. The lack of a paper trail and transparency around this decision is a common example of how off-licence use of instant messaging apps can be highly problematic.

So how can we solve this? The intensity and complexity of modern healthcare demands communication solutions that offer the speed and utility that doctors have come to expect from instant messengers such as WhatsApp.

As Dr Matt Morgan says in his article WhatsApp Doc?

“Restricting their (Message Apps) use is fine but there must be a viable alternative provided”.

However this mandates a tool that is designed specifically for this purpose, with the appropriate safety measures to ensure patient data remains secure. In other industries, platforms like Slack provide seamless communication between teams which can be adapted and tailored to the needs of the organisation using it. The frontline staff of the NHS are long overdue a similar solution.

Together with a team of junior doctors and software developers we have created a mobile instant-messaging app called Forward. Forward is designed to offer all the speed and convenience of Whatsapp but in a way that is secure and did not put patient data at risk. We believe Forward has the potential to be an important tool to support the instant communication needed in today’s busy clinical environment. If Forward and the other start-ups working to solve this problem do not provide the right solution, clinicians will keep using Whatsapp and this issue will persist.”

Conflicts of Interest. Authors Joel Schamroth and Lucinda Scharff are junior doctors who also work for Forward, a mobile application offering compliant, secure instant messaging for clinical teams.

 

How tech can combat NHS prescription fraud

by Stephen Bourke

Analysis published last week by the NHS estimates that £1.25bn of fraud is being committed each year by patients, staff and contractors. That’s around 1% of the NHS budget.

Patients who falsely claim exemption from the NHS prescription charge, alone, are costing the taxpayer at least £200 million a year.

These kinds of unnecessary financial burdens on an already overstretched NHS have been described as “despicable” by Sue Frith, the chief executive of the NHS Counter Fraud Agency.

A flawed system
When dispensing free NHS medications, pharmacists largely rely on people’s honesty. If exempt from paying prescription fees, patients present an exemption certificate, sign the back of the prescription form to say that they are exempt of any charge, or both.

Because the NHS Business Services Authority only runs checks after a free prescription has been issued, additional administrative costs are incurred chasing up fraud cases.

The roll out of the electronic prescription service across England will remove some of the vulnerabilities inherent in paper-based prescriptions – pharmacists will have to record data such as whether a prescription charge was levied, type of prescription exemption claimed by a patient, and whether evidence of exemption was seen by the pharmacist.

However, the Royal Pharmaceutical Society says asking pharmacists to police prescriptions could harm patient trust. But this need not be the case.

The tech solution

Prescription fraud saps vital resources away from front-line services and will ultimately cost lives. But what is more frustrating is that the tools to combat NHS fraud, particularly prescription fraud, already exist.

(Click on image to enlarge it)

Echo users, for example, can request their medications for free through our app. Before we process a prescription order, users are automatically asked whether they’re exempt from prescription fees. If so, they click on the appropriate exemption category and then are requested to photograph and upload evidence of their exemption. We use a machine-assisted checking algorithm to parse data, such as expiry dates, and have patent-pending optical character recognition that facilitates fast and accurate checking.

The collection of such evidence has been integrated into the Echo user journey, as standard, thereby reducing the chances of exemption fraud and reducing related costs for the NHS. This process also reduces the administrative burden of the pharmacist, removing the need for them to collect such data.

Most importantly, Echo completely removes the need for those awkward conversations between pharmacists and patients about providing evidence for prescription exemptions. No pharmacist wants to ask their patients to prove how much they earn or whether they are on tax credits. Echo, therefore, can stop the erosion of trust that such conversations cause.

Using this kind of data is not just about reducing fraud, it also has the power to greatly improve the patient experience and give them access to the most cost-effective health services. For example, Echo can track how often a patient orders a particular prescription and advise them directly through the app if it’s cheaper for them to buy a prescription pre-payment certificate (which is usually most cost-effective if patients order four or more prescriptions every three months or if they order 13 or more prescription each year).

There is no reason why such technologies cannot be integrated into NHS services more widely to help reduce the costs of prescription fraud, funnel this money back to the front-line services, and improve patient outcomes across the country.

Stephen Bourke, CXO and cofounder of Echo – Stephen successfully launched the pioneering digital health business LloydsPharmacy Online Doctor in Ireland and Australia. Prior to that he worked as a Head of Strategy for BT. He holds a masters in marketing from the Vlerick Business School and a bachelor’s degree in economics and philosophy from University College Dublin.

 

 

Quantum Computing And Health Care

By Adrian Raudaschl

Over the last two decades, advancements in medicine and biomedical research have been vastly improved thanks to the continuous increases in computer processing.

As we begin to enter an age of personalised healthcare, dependent on genomics, individual physiology and pharmacokinetics the need to take huge amounts of data and process it in a format for clinical use will become more urgent. Quantum computing may be our best tool for achieving this.

A single bit can be both a ’1’ and a ’0’ at the same time – also known as a quantum bit or ‘qubit’.

The computer you are using today processes information using a series of sequential 1’s and 0’s or ‘bits’. A quantum computer (QC) takes advantage of an usual observation in quantum physics which means that a single bit can be both a ’1’ and a ’0’ at the same time – also known as a quantum bit or ‘qubit’. Thats a bit crazy right? How can something be two states at the same time? Well it gets stranger – when a qubit is observed it will resolve to either a ‘1’ or a ‘0’, which means you only ever see a single state (almost as if the universe knows we are watching it).

If all this is going over your head don’t worry, quantum physics is hard to digest even for the brightest minds, and much of what is happening now is still research.

I highly recommended watching this video to get an idea of how this superposition concept works with electrons:

We can essentially trick the universe into creating a super powerful computer

What is important to understand here is that by taking advantage of this ‘superposition’ principle we can essentially trick the universe into creating a super powerful computer capable of processing multiple pieces of data all at the same time! That basically means we can process allot of more in a fraction of the time and start finding ways of applying huge amounts of data in real-time.

For those still with us I found the following video by Kurzgesagt to be a delightful of helping to get my head around QC’s:

You can even learn the quantum computing basics by trying quantum computing on IBM Cloud.

Medical Applications of Quantum Computers

Radiotherapy

Radiation therapy is the most widely-used form of treatment for cancers. Radiation beams are used to destroy cancerous cells or at least stop them multiplying.

Devising a radiation plan is to minimise damage to surrounding healthy tissue and body parts is a very complicated optimisation problem with thousands of variables. To arrive at the optimal radiation plan requires many simulations until an optimal solution is determined. With a quantum computer, the horizon of possibilities that can be considered between each simulation is much broader. This allows us to run multiple simulations simultaneously and develop an optimal plan faster 1, 2.

Drug Research

Molecular comparison is an important process in early-phase drug design and discovery. Today, companies can run hundreds of millions of comparisons on classical computers; however, they are limited only to molecules up to a certain size that a classical computer can actually compute. As quantum computers become more readily available, it will be possible to compare molecules that are much larger, which opens the door for more pharmaceutical advancements and cures for a range of diseases.

This has the potential to save years of development time and billions of dollars required to bring a drug to market 3.

Drug Interactions

Quantum computing allows us to model complex molecular interactions at an atomic level. This will be particularly important for medical research and drug discovery. Soon, we’ll be able to model all 20,000+ proteins encoded in the human genome and start to simulate their interactions with models of existing drugs or new drugs that haven’t been invented yet 4, 5.

Diagnostics

Artificial Intelligence

There is a growing trend of applying machine learning to aid with patient diagnostics. Much of machine learning is about “pattern recognition.” Algorithms crunch large datasets of patient information to find signals in the noise, and the goal is to leverage comparisons made to help identify a diagnosis.

With quantum computing, we’ll be able to do this processing orders of magnitude more effectively than with classical computing. Quantum computing will allow doctors to compare much, much more data in parallel, simultaneously, and all permutations of that data, to discover the best patterns that describe it.

This will lead to fundamentally more powerful forms of AI much more quickly than we expect. 4, 5

Disease Screening

Using a method known as the bio-barcode assay, we can now detect disease-specific “biomarkers,” in our blood using gold nanoparticles, which are visible using MRI technology and have unique quantum properties that allow them to attach to disease-fighting cells. These gold nanoparticles are completely safe for human use. This method is also cheaper, more flexible, and more accurate than conventional alternatives.

Mikhail Lukin, a physics professor at Harvard, is also working on manipulating nanoscale particles of diamond for similar purposes. He hopes to eventually use diamond particles, to take images of human cells from the inside and detect disease without exposing patients to radiation. It seems he has already managed to do this to detect neural activity 6, 7.

Imaging

Quantum sensors can also improve MRI machine’s by allowing for ultra-precise measurements. Quantum-based MRI could be used to look at single molecules or groups of molecules instead of the entire body, giving clinicians a far more accurate picture.

)ther quantum-based techniques are also being developed to treat diseases. For example, gold nanoparticles can be “programmed” to build up only in tumour cells, allowing for precise imaging as well as laser destruction of the tumour, without harming healthy cells.

Healthcare Data

People want to protect their health data for obvious reasons, so it’s important to consider all the ways that it can be hacked.

ID Quantique is a company using the strange quirks of quantum phenomena to protect our data in an ultra-secure fashion. Using quantum entanglement in one of the most practical applications of the phenomenon to date, quantum cryptography prevents data from being viewed by anyone other than the intended recipient.

Innovations built on the principles of quantum mechanics hold the potential to affect health care on nearly every level, from diagnosis and treatment to data storage and transmission 7.

Genomic Medicine

Techniques such as laser microscopy that is built on the principles of quantum mechanics 8. And using quantum computers, we can more quickly sequence DNA and solve other Big Data problems in health care. This opens up the possibility of personalized medicine based on individuals’ unique genetic makeup.

Protein Folding

Proteins are the basic building blocks of life. Malfunction of a given protein is frequently due to its being wrongly folded.

While the chemical composition of proteins is quite well known, their physical structure is much less well understood. Obtaining more detailed knowledge of the way proteins are folded can help lead to the development of new therapies and medicines.  

A quantum computer will in theory be able to simultaneously test a huge number of possible protein fold structures and identify the most promising ones 1.

Closing Thoughts

Quantum mechanics is a field of science which is inherently astounding, and computing capabilities offered by it is just the tip of the iceberg.

We need to keep a close eye on quantum technology and its application for health care. We’re on the cusp of some thrilling advancements, and we should all educate ourselves on how quantum technology will transform health care in the not-so-distant future.

I hope this has been a useful introduction…

“If you think you understand quantum mechanics, you don’t understand quantum mechanics.” Richard Feynman

About the Author

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.

 

Bibliography

  1. https://hackernoon.com/quantum-computing-explained-a114999299ca
  2. Possible Medical and Biomedical Uses of Quantum Computing . NeuroQuantology. September 2011 | Vol 9 | Issue 3 | Page 596‐600. https://www.neuroquantology.com/index.php/journal/article/view/412/440

References

  1. QUANTUM COMPUTING SET TO REVOLUTIONISE THE HEALTH SECTOR. Atelier. Accessed 14/10/17. https://atelier.bnpparibas/en/health/article/quantum-computing-set-revolutionise-health-sector
  2. D-Wave. Accessed 14/10/17. https://www.dwavesys.com/quantum-computing/applications
  3. BIOGEN, 1QBIT AND ACCENTURE:PIONEERING QUANTUM COMPUTING IN R&D. Accenture. Accessed 14/10/17. https://www.accenture.com/us-en/success-biogen-quantum-computing-advance-drug-discovery
  4. Massive Disruption Is Coming With Quantum Computing. singularityhub.com. Accessed 14/10/17. https://singularityhub.com/2016/10/10/massive-disruption-quantum-computing/
  5. How IBM Universal Quantum Computing Impacts HIT Infrastructure. HIT Infrastructure. Accessed 14/10/17. https://hitinfrastructure.com/news/how-ibm-universal-quantum-computing-impacts-hit-infrastructure
  6. Researchers trace neural activity by using quantum sensors. Phys.org. Accessed 14/10/17. https://phys.org/news/2016-12-neural-quantum-sensors.html
  7. 4 Ways That Quantum Technology Could Transform Health Care. Fast Company. Accessed 14/10/17. https://www.fastcompany.com/3016530/4-ways-that-quantum-technology-could-transform-health-care
  8. Quantum physics sheds light on cells. ABC Science. Accessed 14/10/17. http://www.abc.net.au/science/articles/2013/02/04/3681478.htm

 

 

Never mind the Blockchain, we need to fix the basics

by Stephen Bourke.

My wife and I recently had a baby daughter and, from a care perspective, the experience was outstanding. From our first nervous appointment, to the paramedics who rushed us to the delivery room, I’ve rarely seen passion or professionalism like it.

I’ve also rarely seen quite as much paperwork. Here is about 10% of what we have received so far:

It’s 2017 and our daughter’s arrival has been tracked and documented through the medium of pen and paper. At one point I swear I saw our midwife use a rubber. We’re inundated by talk of how robots will replace doctors, blockchain will transform health records and how we are on the verge of a technological revolution in healthcare. And it’s certainly an exciting time to work in our sector, but it feels a little premature to discuss artificial intelligence when the NHS remains world’s largest purchaser of fax machines.

Tackling the dull, hard problems

Our experience will come as no surprise to anyone working in healthcare, where the second-class stamp is still a critical communication tool.

It’s also not meant as a dig. The NHS is hugely complex and shifting this stuff online is hard. No one wants to user a pager, or cart around large folders of hand-written notes. We do so because they are the often the only tools at hand and we need to get on with caring for patients.

The problem lies with the fact that fixing the basics is not only a colossal task but, let’s be honest, often relatively dull and complex. Compared with nanomedicine, CRISPR and brain implants, optimising nurse shift patterns or digitising appointment booking can seem a little lacklustre. But it’s practical, front-line innovation like electronic appointment booking that delivers real, cashable benefits to the NHS today, and it merits at least as much attention as the sexy stuff.

Tech-start ups to the rescue

The good news is that several tech start-ups are quietly working on these challenges.

When it comes to childbirth, eRedbook has digitised the entire pathway. From test results and observations to appointment booking and immunisations, it’s an elegant solution that eliminates paper, improves communication and empowers parents.

When it comes to secondary care access, drDoctor is blazing a trail. Their platform enables patients to book hospital appointments, access consultant letters, and complete pre-screening questionnaires. A simple patient portal improves access and communication, reducing non-attendance and paperwork for everyone involved. Currently live in five hospitals, drDoctor claims to save £1.2m per Trust. Extrapolate these savings across all Trusts, and we’ve got an extra £240m+ to invest in innovation.

The team behind GDm-Health go one step further, removing the need for (some) face-to-face appointments altogether. Gestational diabetes is a serious condition and needs close monitoring. Typically, this involves bi-weekly visits to the hospital for blood glucose tests and dose adjustments. With GDm-Health, patients can monitor their levels at home and relay information back to clinicians who then make dynamic adjustments based on longitudinal data. Critically, it’s not the consumer technology but the interoperability that is important here—their experienced team know how to adapt to existing care pathways and systems, which means the benefits are quickly realised.

 

Data is useless unless it can be easily interpreted. Converging Data takes standard health-level 7 (HL7) data—the international standard for exchange, integration, sharing, and retrieval of electronic health information—and converts it into meaningful, visual dashboards that can be used to monitor patient flow, identify bottlenecks, and figure out what is actually happening within a hospital. This enables the smarter allocation of staff and essential resources, improving efficiency.

Efficient healthcare is built on a foundation of clear, concise, and quick communication. Consumer smartphone instant-messaging apps are ubiquitous, but data security, consent and compliance issues cause headaches for information governance officers. Enter medCrowd: a fully-compliant platform that enables real-time communication whilst keeping confidential data secure. Instant messaging is often far more far more efficient than email, and creates a stronger, more immediate connection between patient and clinicians.

40% of medication isn’t taken as directed and, despite significant progress in rolling out electronic prescriptions, medication management is still painful from a patient’s perspective. This is what my co-founder and I set-out to fix with Echo. We both take medication for chronic conditions and feel that the bi-monthly rigmarole of ordering prescriptions is needlessly complicated. Through Echo, patients simply tell us who their GP is and what repeat medication they need. We sort out the prescription and deliver medication for free. Smart reminders tell patients when to take their meds and when they are about to run out, promoting medication adherence. Working with 90% of NHS England GPs means that we are on the coal-face of interoperability, but mercifully get support and guidance from NHS Digital.

These six firms represent just a small sample of start-ups tackling immediate system problems with practical solutions. Unless our industry tackles the dull, hard problems—consent, procurement, interoperability, digitalisation, information governance and culture—bleeding edge technology will be stuck in permanent pilot mode. The complexity of these challenges can be intimidating, but we need to prioritise fixing them in order to pave the way for the future.

Stephen Bourke, CXO and cofounder of Echo – Stephen successfully launched the pioneering digital health business LloydsPharmacy Online Doctor in Ireland and Australia. Prior to that he worked as a Head of Strategy for BT. He holds a masters in marketing from the Vlerick Business School and a bachelor’s degree in economics and philosophy from University College Dublin.

 

 

 

 

 

 

 

 

 

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.

Safety

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.

 

References

  1. Alzheimers Society – Current Projects. https://www.alzheimers.org.uk/info/20053/research_projects/562/current_projects. Accessed 18/06/17
  2. Outcry Over EpiPen Prices Hasn’t Made Them Lower. The New York Times. https://www.nytimes.com/2017/06/04/business/angry-about-epipen-prices-executive-dont-care-much.html?_r=0. 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. http://www.nature.com/nrd/journal/v11/n3/full/nrd3681.html
  4. Jacob Glanville. http://www.distributedbio.com/. Accessed 18/06/17
  5. Sharon Terry. http://www.geneticalliance.org/about/staff/sterry. Accessed 18/06/17
  6. Was the EpiPen Hack Ethical?. https://ww2.kqed.org/futureofyou/2017/01/23/was-the-epipen-hack-ethical/. Accessed 19/06/17
  7. USTR: 97% of Counterfeit Drugs in US Shipped From Four Countries . http://www.raps.org/regulatoryDetail.aspx?id=24853. Accessed 25/06/17
  8. The public’s view of pharma just keeps getting worse. https://www.statnews.com/pharmalot/2016/08/30/gallup-poll-drug-firms-negative/. Accessed 25/06/17
  9. Pharma’s Reputation Continues to Suffer — What Can Be Done To Fix It?. https://www.forbes.com/sites/johnlamattina/2013/01/18/pharmas-reputation-continues-to-suffer-what-can-be-done-to-fix-it/#2159e4292aa5. Accessed 25/06/17
  10. Young, talented and fed-up: scientists tell their stories. Nature. http://www.nature.com/news/young-talented-and-fed-up-scientists-tell-their-stories-1.20872. Accessed 09/07/17
  11. Patients Increasingly Influence The Direction Of Medical Research. NPR. http://www.npr.org/sections/health-shots/2016/11/28/502904826/patients-increasingly-influence-the-direction-of-medical-research. Accessed 09/07/17
  12. Ensuring the future of dementia research. Alzheimers Society. https://www.alzheimers.org.uk/info/20052/research_strategy/433/ensuring_the_future_of_dementia_research. Accessed 09/07/17
  13. Darwin Online. http://darwin-online.org.uk/biography.html. Accessed 09/07/17

 

Footnotes

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.

Using mother nature to inspire the next generation of medical implants and devices

by Dr. Gavin Hazell

Medical devices are ubiquitous in modern medicine. Devices range from simple catheters to artificial cardiac devices and complex materials that can replace our own joints. Contemporary surgical procedures have revolutionised our approach to joint replacement with 160, 000 total hip and knee replacement procedures performed each year in England and Wales. Medical implants have seen a rapid expansion in use which has been facilitated by technological advances and reduced manufacturing costs. Today, these devices profoundly impact patient quality of life and disease outcome.

However, all of these devices suffer from a major weakness. They are susceptible to bacterial colonisation, which leads to a medical device associated infection. Once bacteria adhere to the surface of an implant they grow and proliferate until a dense bacterial film resides on the surface, known as a biofilm. The presence of such a bacterial layer leads to the failure of the medical device and puts the patient at risk of sepsis and death.

Biofilms on the surface of implantable materials (such as a titanium hip replacement) are difficult to treat as they are generally recalcitrant to conventional antibiotic therapy. It is therefore necessary for the surgeon to remove the device, thoroughly clean the infected area and implant a replacement. This comes at a significant financial cost to the NHS as well as being a very traumatic and invasive experience for the patient.

Another significant problem with these kinds of infections is the presence of antibiotic resistant bacteria. If the infection is composed of bacteria such as methicillin-resistant Staphylococcus aureus (MRSA, or other antibiotic resistant strains), this makes it even harder to treat as conventional antibiotics cannot be used.

What is required for the next generation of medical implants/devices are materials with surfaces that are lethal to adherent bacterial cells. If surfaces that kill bacteria upon contact could be used in medical devices, then the risk of infections associated with these materials would be significantly reduced. This would negate the need for revision surgery, lower the financial burden for the healthcare provider and significantly improve patient experience.

Recently it has been shown that the surface of the wing of the cicada fly is composed of periodic arrays of nanopillars. These are tiny pillars that are around 200 nm in height and only visible with a very powerful microscope (human hair is around 100, 000 nm in width). When bacteria hit such surfaces, their cellular membrane stretches across these nanopillars and is placed under mechanical strain. If the membrane is soft enough, it ruptures and the bacteria die (see figure below).


In our laboratory, we seek such inspiration from nature to modify the surface of medical implants/devices and render them bactericidal. We generate surfaces composed of tiny nanospikes and/or nanocones that are able to mechanically kill bacteria. Killing bacteria through mechanical means ensures that they cannot evolve resistance and it is possible to kill bacteria that are already resistant to antibiotic strains.

We generate nanopatterns on a vast array of materials. We are currently focused on forming nanocones on polymers for use in catheters, blood storage bags and contact lenses. Black silicon is used as a new material for biosensor electrodes. Here we can pattern extremely sharp spikes that are able to puncture bacterial contaminants when the electrode is working. Finally, a large interest is in the patterning of titanium dioxide for use in prosthetic joint replacement surgery. It is possible to form nanospikes on these surfaces that can also puncture adherent bacteria. Below is an image gallery of all the materials we are working with along with some microscope images of dead, punctured bacteria on the surfaces. A note on intended clinical application is also included. (Please click on image to expand) 

Dr Gavin Hazell is a research scientist working in the biomaterials engineering group at the University of Bristol. He is an expert in materials science and his research interests lie in finding new ways to improve healthcare through the generation of novel, smart materials.

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.

Creating patient convenient technology

By Dr Adrian Raudaschl and Esther O’Sullivan

Technology has the potential to reduce the burden on health services by empowering patients to support themselves better. Apps which record clinical data and then prompt users to undertake activities like exercise, remind patients to take their medication, or provide patient education, already exist, but the uptake is low. Continue reading Creating patient convenient technology

Guerrilla Prototyping

How BMJ gets prototypes delivered.

In common with many organisations BMJ is keen to test new concepts, but when it comes down delivering the goods we have very little capacity available as we cannot justify using a whole sprint team when there is always masses of revenue generating work to deliver. Additionally prototyping is viewed by some as a large investment for low returns. Continue reading Guerrilla Prototyping