HealthTech Needs Data Science

Posted 2017-07-07T Posted by Tom

HealthTech presents a huge opportunity, but how can businesses leverage these advantages, and how can Data Science help?

The healthcare industry is huge and in turn, the size of the Health Tech industry is significantly larger than in other fields. In the USA alone, the healthcare market is more than 17% of GDP. A recent article published by TechCrunch highlights the vulnerabilities in the industry based on its consistent and rapid expansion: “the healthcare market represents $3 trillion, almost 20 percent, of the U.S. economy. This market also is plagued by a level of gross inefficiency and under-performance largely unseen in any other industries in our post-internet world”. Furthermore, highlights that the potential of untapped data in healthtech is substantial, saying:

“According to one estimate, clinical data from a single individual will generate 0.4 terabytes of information per lifetime, genomics data around 6 terabytes and additional data, 1,100 terabytes. By 2020, the amount of health-related data gathered in total will double every 73 days.”
With the costs of drug developments being higher than ever and populations that continue to live longer, this industry is primed for revolutionary developments in tech. In order to minimise the growing costs of developing and manufacturing new drugs to meet the continually increasing demands of an older and more complex population, Big Data is set to evolve and effectively secure the industry.

To date, the health industry is relatively untapped by tech businesses. As reported in Forbes recently, “a new crop of tech entrepreneurs is trying to change that. Their products range from apps and social networks to robots and complex simulators. But they all share a common goal: to leverage new technology to fix an old industry.” There are huge areas within the health industry that sit as voids in the tech world and data minds are becoming quicker at identifying the untapped potential.

High Profile HealthTech In The UK

Techworld explains the ways in which other high profile figures are asserting the need to address the voids in HealthTech, explaining that London Mayor, Sadiq Khan, is backing HealthTech with the ‘digital’ accelerator scheme, which is currently helping 32 companies design technology solutions for the NHS. Other examples, as reported by Forbes  include “Dr. Mohammad Al Ubaydli received care from many different providers for a rare genetic condition, he realised that no one had a complete picture of his medical treatment. There was little he could do to change this because he didn’t have easy access to all of his medical records. That’s why he built Patients Know Best a platform that keeps medical records in the cloud and in the control of patients”.

Harnessing and understanding big data are crucial for companies looking to operate in this space. What is more, the size of this field is so extensive that companies must be prepared for the huge undertaking. explains that “big data sets in medicine include genomics, transcriptomics and proteomics … Genomic data sets alone have already shown their value … But a single molecular data set will not contain sufficient information to tell the whole story of an individual’s medical fate. Integration of different types of molecular data might tell more, but that remains a computational challenge”.

The role of data in healthcare is imperative and all healthcare organisations should have a Data Science strategy in place.  NEJM Catalyst explains that in health care, “individual pieces of data can have life-or-death importance, but many organisations fail to aggregate data effectively to gain insights into wider care processes. Without a data science strategy, health care organisations can’t draw on increasing volumes of data and medical knowledge in an organised strategic way, and individual clinicians can’t use that knowledge to improve the safety, quality, and efficiency of the care they provide”.

The UK-based AI division of Google, Deepmind, is set to analysis one million eyes in order to provide a host of algorithm data to researchers. This tech advancement is set to allow clinicians the opportunity to detect and diagnose eye and sight conditions at early stages, predominantly the condition of namely diabetic retinopathy and wet age-related macular degeneration. The programme will work in connection with the Moorfields Eye Hospital, where the leader of ophthalmology research center, Peng Tee Khaw explained;

"These scans are incredibly detailed, more detailed than any other scan of the body we do: we can see at the cellular level,".
DeepMind at the NHS have worked together previously but this exciting new scheme is set to introduce the AI to the partnership.

HeathTech Needs a Data Science Strategy

Just as in other industries, the efficient data strategy will recognise the quality of data and will analyse and store it successfully. When the strategy is at its optimum level, the healthcare industry will benefit from precision medicine and learning health systems. This means that treatment will be more easily tailor to individual patient needs and tech will be able to predict likely outcomes for a variety of healthcare concerns. Healthcare strategies will benefit most from robust strategy, and as a starting point leadership should be considering:

  1. Data storage that serves the whole organisation
  2. Integration of data across sources
  3. Structured and stable security
  4. Data processing experts - Data Scientists - who can make use and direct analytics and data
  5. Feedback: the use of data should be continually evaluated so that it is consistently used and reused to its maximum effect
Not only does the healthcare industry demand technological progression in order to keep up with patient needs, but in order for the field to benefit from greater knowledge, improved methods and better survival rates, the inclusion of Data Science and AI is essential. As TechCrunch has highlighted

“The potential benefits of these new technologies go beyond identifying risk and screening. Advances in machine learning can help accelerate cancer drug development and therapy selections, enabling doctors to match patients with clinical trials, and improving their abilities to provide custom treatment plans for cancer patients (Herceptin, one of the earliest examples, remains one of the best)”.
Tim O'Reilly furthers this by detailing how imperative data science is to the healthcare industry in his book, "How data science is transforming healthcare”:

“Data science is not optional in health care reform; it is the linchpin of the whole process. All of the examples we’ve seen, ranging from cancer treatment to detecting hotspots where additional intervention will make hospital admission unnecessary, depend on using data effectively: taking advantage of new data sources and new analytics techniques, in addition to the data the medical profession has had all along.”
The health industry represents a huge opportunity for tech businesses and entrepreneurs, with a huge financial rewards and many inefficiencies to tackle. Key to success in this field is in using data science to organise, access and understand the vast big-data reserves. Securing an effective a data science strategy in healthcare cements the ability for the industry to deliver better standards of care and treatment, promotes efficiency and supports a population that continues to grow and age. Data will form the foundations of our future healthcare system and enables the industry to improve operational capabilities, minimise costs and increase access.

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