Digital Palette (Part 2): Digital Transformation in Health & Biotech

Now more than ever, as the world we know is forever changed by COVID-19, we need to explore and embrace the endless new possibilities enabled by digital transformation. Especially where it matters most: Health!

In a previous article, NovAzure introduced the meaning of Digital transformation and the digital palette:


© NovAzure Ltd 2020  Illustration by: Jana Kattan @janamation_

Now we will explore the diverse applications of these elements in Healthtech and Biotech. 

1.    Cloud and Platforms


Telemedicine: The last few years have seen a rise in telemedicine. There are a number of platforms on which you can call a doctor and make a video consultation. Of course this does not work for any disease, but many consultations could work via video, especially for prescription renewal and psychiatry, nutrition and such. In the UK, the most famous example is Babylon Health, which partnered with the NHS (National Health Service) in order to offer video consultations. They also provide access to clinical records, a search engine for health information, a health check using questionnaires to get personalised insights on how to get healthier and a conversational bot to answer your questions using deep neural networks and artificial intelligence. In France, telemedicine has been refunded under certain conditions by social security since September.

Another use of telemedicine is advice between doctors: a General Practitioner can call a specialist and show him his patient via video to ask for advice. During the call the GP can execute the examinations the specialist needs to give his opinion. This can also be done between a nurse and a doctor, as nurses are able to carry out many of the examinations that are necessary for the doctor to give a diagnostic. 

Telemedicine cabins exist as well, in which patients can use different tools to calculate their own measurements such as blood pressure, heart rate etc. The doctor will guide the patient through the different measurements and give his diagnostic accordingly.

When there is health data, there is a need for security, meaning video consultations need to take place on special platforms with high security norms. For the doctors, it is also easier to use platforms dedicated to telemedicine with which they can write their prescription and share it with the patient easily. These platforms need to be built with the help of health professionals, however a lot of them also use big data, artificial intelligence and such.

2.    Social Media and Collaboration


Social media has shaped patient’s lives in many ways. It has allowed patients who are suffering from rare diseases to find each other, share advice and create patient advocacy groups. Social media and the internet have also transformed the patient-doctor relationship: now the patient has access to information, and does not accept a form of medicine in which he is given treatment without detailed information. Unfortunately, social media is notorious for spreading ‘fake news’, making it difficult to differentiate reliable sources from false ones. This is the same for the medical field, as conspiracy theories and false information are shared. This makes it hard for the medical staff to treat untrusting patients, especially if they have a hard time communicating in a clear, yet precise and detailed way. Social media is also known to polarise opinions, which contributes to the creation of an opposition between “traditional medicine” and “complementary medicine”. In France, certain medical staff have created an online movement to oppose what they called “fake medicine”, which had a great influence in the ending of the reimbursement of homeopathy.

This being said, social media has also helped doctors communicate with each other, and it further helps researchers from all over the world come together to discuss their research.

The movement of DIY biology or biohacking also rose through social media. It is a movement of people from every background doing experiments with low-tech labs, finding cheap equipment wherever they can and learning independently through books, internet and collaboration. By always looking for cheap equipment, some of them are developing techniques that remote hospitals with low budgets could use. Others work to share their love for science to the general public, expanding public knowledge around health and biotech. This movement is still quite young and it is difficult to say how much of an impact it could have, but it is definitely worth keeping an eye on.

3.    Wearable Technology

Wearable technology has many uses in healthcare, especially for chronic diseases such as chronic heart failure. A lot of patients need to have constant monitoring which can be done through Wearable technology. For example, start-ups such as Chronolife have created t-shirts that integrate many different sensors. Chronolife’s t-shirt takes 10 different measures, from heart rate and breathing capacity to posture and number of steps. This data is then sent to the patient’s phone and to the cloud to be analysed through predictive algorithms that alert the patient and their doctor if a danger is detected.

These kind of t-shirts can also be of use for professional players and athletes, care dependent persons, or for managing sleep apnea or epilepsy.

Many wearable monitors can also track sleep patterns to help manage insomnia or other sleep conditions.

4.    Big Data Analysis (Analytics)

Big Data, in essence, makes data exploitation easier. In biotech and healthcare, it has made research faster and cleared the way for new discoveries. Drug discovery, for example, relies mainly on finding interactions between compounds and proteins or genes, which can be done much quicker through Big Data. It has paved the way for personalised medicine, and has made possible the exploitation of genetic data.

Moreover, it has helped the trend for ‘open’ research and innovation, where researchers work together to make giant databases. This enables other researchers to perform analyses that are more relevant and use a sample size that could never have been achieved by a single researcher or a single research team.

Some examples of these Databases are NCBI (a gene database) or Uniprot (a protein Database). In the Human Genome Sequencing Project, multiple research teams sequenced different parts of a human genome and shared it on the internet, making the first complete human genome available to all. Epidemiological Data can be gathered all around the world and then exploited to figure out trends and risk factors.

DNA as storage: The digital transformation has tremendously impacted health and biotechnology and, in return, biotechnologies could help digital innovation. Today’s issue with big data is storage, as our capacity to generate and share data is growing faster than our capacity to store it. A possible solution would be storing data in DNA[1], which stores a great amount of information in an extremely compacted way. The code of DNA is made up of four letters, A, T, C and G, which could enable us to store information like binary code.

So why are we not using it already? Although DNA costs went down from $10K per megabase in 2001 to less than $1 per megabase in 2019[2], DNA synthesis (writing and creating DNA with any combination of A T C and G that you chose) is still very expensive. However, companies such as start-up DNA script are working towards making this affordable and faster.

Many have already seen the opportunity in this process:

  • Harvard scientists have shown that DNA as storage is possible by storing the animation of the galloping horse in a cell [3]:

  • A start up named Catalog emerged from MIT in Boston and is working towards commercialisation of the technology. According to them, the commercial system won’t be ready until 2021 [4] [5].
  • At the University of Washington, an engineer collaborating with Microsoft is trying to automate the process in order to make it scalable [5].

Drug Repositioning: In the recent years, drug development has slowed down and has become more costly. It seems logical: the more drugs are discovered, the harder it is to find new ones and the more innovative you have to be. The process to get a new drug to market is difficult and long: it takes 13-15 years, costs between $2 billion and $3 billion on average [6], and only 1 in 5,000 drugs that enter preclinical testing makes it to market [7].

However, the need for new drugs is still strong, and pharmaceutical companies are looking for faster and more cost effective ways to find them. In this context, one of the solutions that has emerged is drug repositioning.

The principle behind drug repositioning is simple: the same molecule can have effects on different diseases. Therefore, a drug approved for a certain condition could be used to treat another. Not only does this cut down the cost of drug development, but those drugs can also skip the first clinical trials if the safety has already been proven. Moreover, sometimes a drug has passed safety trials but not proved its efficiency for the disease it was tested for. If so, it will not reach the market but could be repurposed if efficiency is present for another condition. Drug repositioning can also include a new combination of drugs, however, all the trials would have to be done again, but the research costs would be cut.

Drug repositioning can be up to 60% cheaper and, depending on the context, 30 to 60% faster than drug development from scratch [8]. It was estimated that the global market for drug repurposing was nearly $24.4 billion in 2015 and could reach $31.3 billion in 2020.

The idea is not entirely new, one of the most well-known examples is the drug currently known as Viagra, which was first developed in 1989 as Sildenafil, a chest pain medication but was approved in 1998 in the US to treat erectile dysfunction. Another example occurred in the1980s: Azidothymidine failed as a chemotherapy but emerged as a VIH medication.

These two repositioning happened by chance: a side effect was reported and the company realised this side effect could be used as medication. Efficient drug repositioning needs digital transformation.

Big data and computational approaches are essential to drug repositioning. There are many different methods but all of them include screening through a large number of compounds to find chemical, biological or structural similarity or complementarity. The methods involve screening compounds from databases of patents, clinical trials, etc. For example, the US National Institute of Health has created a Database of drugs that have passed safety trials. At a small level, it can be done without big data but it will be a long and painful process for scientists. As a result, some companies like intomics in Denmark offer bioinformatic services for drug repositioning.

This field is quite new, and we will probably find new ways of identifying reposition-able drugs through advances in big data and biology, but it opens up a new area of possibilities beneficial both for pharmaceutical companies who will save time and money, and patients who will get new, and sometimes vital, drugs faster.       

5.    Artificial Intelligence (AI)

Personalised and predictive medicine: Personalised medicine is considered by many to be the medicine of the future. It has long been observed that the “one size fits all” approach to drugs and medication is not the optimal one but, until recently, it was the only one logistically manageable. Depending on our DNA, our hormones and many other factors, we will react differently to treatment. Women and men might react differently to some medication, which has been disregarded by years of drugs being tested mainly on men until 1990. But to tailor the treatment to each person, we need two things: first we need to understand what the correlations between drug efficiency and the many factors are. Those factors include gender, DNA, hormones and size and weight to name a few. In order to discover these correlations, we need large epidemiological data, and Big Data to find out the statistics.

Once these correlations have been found, we need to tailor the treatment to the patient. This is where artificial intelligence comes in. If a doctor had to tailor every treatment himself taking into account dozens of data, it would be very long and costly, and would decelerate the whole health system. However, if artificial intelligence can do the maths in a few minutes with all the patient data, then it becomes easy.
Of course, administration mode would also need to be altered. For this problem, robots or 3D printing could be of assistance.

Predictive medicine goes a step further: it is the idea that, with all of your Data (DNA, physical activity, weight, height…) the kind of disease you are at risk of could be calculated. This would mean a great deal for disease prevention, as we have seen already

for cancer prevention. If you have certain risk factors, including some genes like BRCA for breast cancer, you will need to get tested more often. However, predictive medicine also sparks huge ethical and philosophical debates: how much should we know of our health risk? Can we really choose not to know, despite the fact that when a family member runs a test their results might apply to us as well? If we have a 97% risk of a deadly and incurable illness, is it better not to know? Can these results be exploited for the worse by insurances and employers? Stress being an extremely detrimental health factor, can this knowledge do more harm than good?

Whilst in some countries like France, DNA tests can only be done by a medical professional and needs to be ordered by one, in many others such as the US and the UK, they can be bought easily, with private societies offering results about risks of diverse diseases, with sometimes no way to verify how the process works or how private the data is.

Predictive medicine is a new area that works through big data and artificial intelligence, and will probably rapidly progress at the same rate as the development in these areas and in biology. This being said, it is hard to say if regulation will progress as fast as science, or how much it will be needed.

6.    Robots and Drones

Robots have many uses in Healthcare. Surgical robots can assist surgeons with precision, and nanobots, equipped with cameras, can make surgeries less invasive because there is no need to make a big entrance cut to access the inside of the body.

Companion robots also have many uses. It has long been established that loneliness is a great threat to health, especially amongst the elderly. To prevent this problem, many start-ups have developed companion robots that can keep the elderly company, help them play games for their memory, video call their family easily and even detect falls and call for help. Some robots have been developed for children in hospital to keep them company and to entertain them. Kompaï robotics has also developed a robotic cat that purrs and reacts to the touch. It can give comfort to those who need it but could not care for an animal, for example in nursing homes.

Robots can also be used in pharmacies to deliver a precise amount of medicine according to the prescription, and to pack them by the time or day it should be taken, so that patients who have to take a lot of medicine do not get confused.

Drones can be used to transport medicine. For example Zipline uses drones to transport medicine and medical equipment to remote hospitals in developing countries.

7.    3D Printing

3D printing can work with many materials. In healthcare, bio-ink is used: a pipette is guided by a computer to layer living cells [10]. This can help create skin for burns victims [11], miniature organs for research, patient-specific organ replica for surgery practise and, in the future, even organs for transplant. Scientists at the University of Mexico are working on 3D printed ligaments, and some at University of Minnesota 3D have printed photoreceptors which could pave the way for bionic eyes to cure blindness [12].

3D printing can also help make custom-made hearing aids or prosthetic limbs cheaper and faster to create. Sterile surgical equipment can now be 3D-printed, cutting down costs.

In pharmacology, 3D-printed pills could host multiple drugs with different release times, which would make it easier for people who take a lot of medication not to forget any and lessen the risk of drug interaction [13].

8.  Virtual Reality

Virtual reality can be used to help calm patients’ stress. Patients see and hear calming music and images, which can be helpful for anxiety, chronic stress, or before and after surgeries.

It can also be an immersive teaching method for healthcare students and for patients who want to understand their disease, as it allows them to journey through the human body. For example Cognitant, a start-up based in Oxford, develops immersive and interactive 3D Health information videos for tablets, smartphones or virtual reality headsets. 

By Gaia Jouanna (Apr 2020)

Interested to know more or share your thoughts? Or keen to understand how NovAzure can help you transform digitally? Please contact us at and we will be delighted to hear from you.
















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