Big Data is not an uncommon subject nowadays, just as AI, Machine Learning or Blockchain have been for the last few years. And although these concepts are well-discussed in consumer goods or FinTech, the impacts they can have in the healthtech landscape are often overlooked. We don’t want that, do we? With COVID-19 sweeping across continents, it is time for us to pay more attention to the role of data in health and why collecting such information will allow us to have better and more personalized healthcare. Cheaper, faster, more accurate, that is how we want our treatments to be!
What exactly is Big Data?
‘Normal’ data is a collection of facts (numbers, words, measurements, observations, etc.) that has been translated into a form that computers can process. Data is everywhere: your age, your name, the dress you looked up yesterday, and certainly your test results from the clinic. Whichever industry interests you, data has already revolutionized it. The way that computers interpret data is different than how we as humans do. Data in computing and business refers to machine-readable information as opposed to human-readable information. That is structured data versus unstructured data. Big data is ‘a little’ different. It comprises more extensive, more complex data sets that traditional data processing software just can’t manage. Similarly, big data in health refers to consumer, patient, physical, and clinical data that is too vast or complex to be understood by traditional data processing. Instead, big data is often interpreted and analyzed by machine learning algorithms and data scientists. With digital transformation finally arriving in healthcare, the demand for big data to fuel data-driven decisions, minimize human errors and increase personalized therapeutics has grown larger and larger. Naturally, Big data in healthcare became a thing.
To visualize how big ‘big data’ in health is, here are some numbers: if all the digital healthcare data is stored on a stack of tablet computers, the height of the tower, by the year 2020, would cross 82,000 miles (or 131,966 km) – scaling from 5,500 miles (or 8,851 km) in 2013. For the record, the tallest tower right now does not even reach 1km in height.
Why is it such a big deal and why is everyone talking about it?
As mentioned before, big data is the premise for data-driven decision-making while minimizing human errors and most importantly, treating patients better and more efficiently. Data tracking and analysis in healthcare will change the world for the better. Not only that, with the massive amount of information at hand, data scientists can forecast upcoming epidemics and combat existing diseases while saving a significant amount of money. Not to mention, in terms of healthcare, investments are pouring into Big Data.
For instance, a McKinsey report in 2011 indicates a high financial impact of big data applications in the healthcare domain in the US: the sector could create more than $300 billion in value every year, whereas two-thirds of that would be in the form of reducing expenditure. Similarly impressive numbers are provided by IBM: within the Executive Report of IBM Global Business Services (Korster and Seider 2010), the authors describe the healthcare system as highly inefficient: approximately US$ 2.5 trillion is wasted annually and efficiency can be improved by 35%.
But there is more to it.
Let’s look at some significant sources of Big Data in Healthcare:
- Electronic health record (EHR): test results, clinical observations, diagnoses, current health problems, medications taken by the patient, the procedures he/she underwent
- Personal health record (PHR): Allergies and adverse drug reactions, chronic diseases, family history, also illnesses and hospitalizations.
- Medical Images: the usual concepts of CT, MRI or X-ray that we always talk about. It just proves that Big Data has been around for longer than we are aware of.
And just how popular is it? A study done by Statista in 2018 showed 44% of US adult respondents had accessed their EHR, while 18% had not accessed them, but they did have one. Only 6% of respondents opted out of having an EHR.
Many startups saw this window of opportunity and did not disappoint. Among our portfolio companies, OncoChain creates an extensive database with real-world data about oncological patients to generate useful analytics that can guide and support research. They provide a web-based oncology EHR that also acts as a software platform that connects the patient with care providers.
Roadblocks and Obstacles
However, there is always a catch. Unlike other industries, healthcare is tightly secured with rules and regulations, which have fenced off innovations for change. The broad scope and diversity of big data applications showed just how valuable and promising this technology could improve overall healthcare delivery. To realize such potential seamless access to various health data sets is required, but it is minimal and cumbersome in reality.
How can we address this issue? Research in 2019 pointed out that several technical requirements such as semantic enrichment of data, data integration and sharing, data privacy and security, as well as data quality have to be addressed. In terms of market adoption, the big data revolution in the healthcare domain is in a very early stage with the most potential for value creation and business development unclaimed and unexplored. Current roadblocks are the healthcare system’s established system incentives, which hinder collaboration and, thus, data sharing and exchange. Like thousands of healthtech startups out there, OncoChain collaborates with large organizations to share data safely and securely. They are the unspoken heroes who will turn the wheel for humankind.
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