Looking back if I was to summarise my first 3 months at BMJ, confusion would be the best word to describe it. Moving from one company to another can be a daunting task, a plethora of questions goes through one’s mind. There are the big questions such as; What are my colleagues going to be like? Will I be appreciated? How many holidays do I get in a year? To the trivial questions such as; When do I get paid? Where’s the nearest toilet? What’s the quickest route to the office? While I was finding the answers to these questions and learning more about the technology behind the BMJ’s many products it was a challenging time for the tech department where we saw many departures. Two colleagues left within my first week, followed by another two leaving before the new year. However one thing that reassured me was that everyone was leaving after a few years of employment here, which was a positive sign.
I had previously been working at a startup called Noetic Marketing Technologies, who focus on the revolutionising the entire hotel and guest experience. They provide automated processes to the guests, predict purchasing behaviour, match the right promotion to the right guest via detailed analytics and complex data workflows. Transitioning from a small office to a large company took some adjusting, as previously if I needed something from someone we were all in the same physical space. Whereas in BMJ you find that a person can be on a different floor or completely different department. Noetic only had 10 employees in the UK office and around 20 in their Sri Lanka office. Since there wasn’t that many people to begin with, it meant one person was wearing a lot of hats. For example, the Business Analyst doubled as a Devops Engineer and most HR matters such as Payroll or Pensions were handled by one of the co-founders. So the comparison was not only surprising but a great relief because not only do BMJ have appointed departments but someone focused solely on Learning & Development or Pensions.
The design and implementation of accessible features has long been a bugbear for designers and developers alike. That’s not surprising – the Web Accessibility Initiative (WAI) committee have done a great job of obfuscating the task of delivering inclusive designs.
Take a look at the WCAG 2.0 website. Wade through it. Attempt to make sense of it. It’s labyrinthine! Don’t have to take my word for it either. As an A List Apart article put it:
“the fundamentals of WCAG 2 are nearly impossible for a working standards-compliant developer to understand”.
My name is Fares, and I somehow became a recognised health tech entrepreneur.
About two years ago, I had to undergo surgery and was prescribed lots of different medicines. Antibiotics, painkillers, you name it. It was a fairly painful experience keeping track of the medicines I was taking, and my parents were always worried about me because they live abroad. And that’s where my journey started.
In the summer of 2016, I applied to Kings20, the King’s College London Accelerator with my venture The Medic App. The app was a medication reminder designed for carers to help them schedule track medication reminders for their loved ones. This solved the two problems I knew I had: my parents wouldn’t be worried about me because they could see me taking my medicines, and I would not forget to take my medicines again. Continue reading Learning To Entrepreneur – The Hard Way→
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. Continue reading WhatsApp in the NHS – Framing the problem→
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.
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:
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.
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.
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.
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.
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.