There have been tremendous advances in the quantity of data we regularly generate and collect in all areas of our lives, as well as applications to ascertain and analyze it. This crossroads is referred to as “Big Data” and it is aiding companies in a wide range of industries to become more effective and streamlined.

Healthcare is no exception.

Big Data in healthcare helps to foresee outbreaks, find cures for diseases, enhance quality of life, and escape avoidable deaths. These patient benefits are in addition to improving earnings and reducing unnecessary operating costs.

The collective goal of caretakers is to understand the entire patient, and flagging signs of serious illness early in life so treatment is more effective and less expensive in the long run. With our global population growing and people living longer than ever before, treatment delivery is quickly evolving, and data is driving the direction of these decisions.

Here is a look into how Big Data is playing a significant role within healthcare.

Blockchain is a tool that allows the trafficking of data and services and ensures financial exchanges happen within a secure setting. Because of this high level of security and variability, it provides valuable opportunities within healthcare. Here are some examples of how blockchain can be integrated.

Data Security within Clinical Trials

The blockchain technology allows users to prove the authenticity of any document registered in the system. Because blockchain records immutable data, it allows for the storage of clinical trials’ results in a protected fashion, literally making data unable to be modified and ensuring its efficacy.

Blockchain technology is currently one of the most pivotal and disruptive in the world. Multiple industries are implementing the blockchain technology to innovate the way they function, including the healthcare industry.

Blockchain is decentralized digital accounting that keeps a permanent, unalterable record of transactions between users. In short, a blockchain is a time-stamped series of fixed record of data that is managed by a cluster of computers not owned by any single entity. Each of these blocks of data (i.e. block) are secured and bound to each other using cryptographic principles (i.e. chain).

In our current healthcare climate, a patient can book an appointment with a physician, inquire about medical guidance, access medical records, and have a prescription filled…but never actually leave their front step. If that isn’t incredible enough — robots perform surgeries and 3D printers can print human organs; the healthcare industry transforms year after year, opening the door and earning a front row seat in the development of digital health.

Let’s explore primary drivers that are responsible for moving digital health along within the medical market.

The transformation to value-based medical care, artificial intelligence (AI) and machine learning, and the rise of private insurance models is predicted to have the most substantial adjustments to how the healthcare industry functions, according to Forbes.

Forbes has published predictions on the evolution to the healthcare industry for the last 10 years.

Here are Forbes’ top eight predictions for the healthcare landscape in 2019:

Healthcare organizations have their plates full, juggling the latest and greatest offerings and cutting-edge tools, all while figuring out budgets to support the advanced tech tools and procuring staff with the capacity to oversee them. One of these trending tools  on every administrator’s mind is preparing for and implementing Artificial Intelligence (AI).

Read any article or watch a news clip about the subject, and there are common themes that AI is projected to bring to the table — you’ll hear words like:

• Analyze
• Predictive actions
• Preventative care
• Feedback

The bottom line is that AI is seen as a benefit to the patient, first and foremost. With continuous connectivity and the capacity to learn, AI would have the ability to improve clinical efficiency by collecting, aggregating, and analyzing patient data —the results of this learning can then be used to improve patient adherence, engagement, and to proactively advise on next steps. AI in a clinical setting would prove its effectiveness as it begins to transform the healthcare system into one more focused on preventative care as opposed to reactionary care.