Big Data in Healthcare: the Ultimate Guide on its Application and Benefits
The overview of Big Data in healthcare and the way it transforms the industry
Big Data is one of the hottest trends in the healthcare industry as it brings multiple benefits to both the medical specialists and the patients. However, the subject of Big Data in healthcare is quite broad and involves many aspects. Thus, we prepared a detailed guide that explains how exactly Big Data can be used within healthcare and what challenges the companies might face when adopting it.
Big Data explained: what is it and where does it come from?
Big Data is a whole field of study that focuses on the collection, processing, and analysis of the data sets that are so big they require specific tools such as Machine Learning, for example.
In healthcare, Big Data includes all the data that is collected from numerous sources:
Electronic health records,
Existing research studies,
Wearables (i.e. Apple Watch, MiBand),
And this is not the full list of sources where healthcare data comes from. One more thing to know about Big Data is that it is based on the three Vs:
Volume: Big Data comes in incredibly large data sets.
Velocity: this implies the high speed of the data generation.
Variety: Big Data is comprised of the most different data formats such as audio, images, text, etc. All this data needs to be structured and unified for further processing.
Before we discuss the benefits of using Big Data in the healthcare industry, we also need to mention the most common tools used for its processing and management. Without a doubt, that would be Artificial Intelligence and its subsets like computer vision, Internet of Things or Machine Learning. The biggest advantage of AI is its capability to quickly process massive data sets while minimizing the possibility of an error. As well, AI can identify hidden or non-obvious dependencies and patterns that are often missed by human employees. This is why the companies that wish to study their Big Data need to deploy machine learning and similar tools.
Now that we are clear on the definition of Big Data and the tools that are needed for its management, let’s move on to the benefits that Big Data brings to healthcare.
When making a diagnosis, doctors sometimes do guesswork as they cannot always be 100% sure in the accuracy of the diagnosis. Now, thanks to the Big Data and the availability of tools for its mining and processing, medical specialists can make more accurate diagnoses based on the past patient’s records
For example, if a doctor examines a patient, the analysis of the past patient’s records can show a liability to a certain disease or the possibility of the disease’s occurrence. In this way, the doctor’s decision will be backed by data and it will reduce the possibility of an error such as the wrong diagnosis. Another example is computer vision technology and its ability to pinpoint any problem areas on the patient’s X-ray image (or similar), thus providing additional information to the doctor.
Predictive analytics allows medical specialists to build accurate forecasts regarding the patient’s future state of health or the possibility of a disease. In this way, medical specialists can take preventative measures to avoid serious consequences and to timely treat the patient’s condition. It is especially important for detecting such conditions like cancer when even the slightest delay can lead to serious consequences.
As an example, Google Health and Imperial College London conducted research and designed an AI-powered model that 6X times outperformed radiologists in mammograms reading. This, in turn, proved that the implementation of the latest technology combined with the use of Big Data can greatly optimize the work of the medical specialists and become the number one tool in combatting the diseases.
Another good example of a predictive approach is a recent collaboration between Apple and Stanford University. The two parties work together on determining whether Apple Watch can efficiently detect atrial fibrillation among the users of the device. In case the research proves successful, Apple Watch will be capable of timely identifying the user’s condition and notifying the user about it.
The capability of Machine Learning tools to search through Big Data and extract the necessary insights within mere seconds is a highly valuable asset, especially if we talk about finding new treatments and conducting medical research. The biggest issue that medical specialists face while during research is the necessity to go through the overwhelming amounts of data either manually or with the help of legacy tools. As well, do not forget about testing the newly discovered treatments and holding multiple experiments before the product can actually be released in the market.
With AI and Big Data, the process speeds up significantly. Not only can the ML-powered tool quickly find the necessary information but it can also suggest the most suitable and cost-saving option. And that means the costs for healthcare treatment can drop down, making the medical services more available.
Minimization of errors and negative effects
In relation to the point mentioned above, the deployment of Big Data in medical research also helps minimize errors and eliminate or minimize the negative effects of the treatments or medications. Because the ML tool searches through the data, it can easily predict whether a certain medication or treatment would cause a negative effect among the patients and it can suggest a more suitable alternative.
In this way, Big Data can contribute to making healthcare services safer for the patients which is one of the key advantages that medical specialists strive for.
Expanded functionality of wearables
Companies need data in order to use it for the patients’ and medical specialists’ benefit and wearables are one of the best sources for data mining. First, wearables do not interfere with the user’s activity and second, the data that they collect is extremely accurate as it comes directly from the user. Therefore, the deployment of wearables as a primary data source is becoming a very emerging niche for the companies to fill in.
One of the best examples is the above mentioned Apple Watch that might be used for heart condition monitoring. And considering the rising demand for smart and healthcare-focused wearables, we might soon see other devices picking up the initiative.
A few challenges to consider
Even though Big Data is highly recommended for use, companies need to know about a few challenges related to it.
We already mentioned that healthcare data comes in many formats and sizes. Hence, when a company decides to collect the data and process it, the following questions must be answered:
How will you pull all the needed data from all the sources together?
What kind of data format will you choose to unify the data?
How exactly will you store the data?
How will you cleanse the data and ensure it does not contain errors?
Make sure that your company has all the resources needed for resolving these issues. Otherwise, you will have a hard time collecting and processing your data and, most likely, it will not bring you much value.
Compliance with regulations
Another important thing to take care of when working with Big Data is following the principal healthcare regulations such as HIPAA and PHI (Protected Health Information). This means companies should watch how they store and transmit the data, control the access and user roles, how data is authenticated and similar issues.
The good news is that the biggest software providers such as Amazon AWS offer their services that are already compliant with HIPAA and PHI. However, not all the companies will be using such services so compliance with regulations becomes an additional task to resolve.
Big Data is an incredibly valuable asset for the healthcare industry but it should be treated in the right way in order to maximize its benefits. The use of Big Data opens a whole lot of new opportunities for the software development companies to fill in the niche and offer an efficient solution to maximize the Big Data benefits and contribute to the healthcare industry transformation.
Irina LinnikView all articles by this author.