Big Data has filled an essential role in healthcare. It’s immediately apparent applications as a means of storing and recalling patient medical history pales in comparison to what else that data can be used for. Big Data, from an enterprise perspective, deals with the collection of multiple streams of information from various sources. These data packets are collected and processed (usually in real-time) to offer insights based on that data. However, patient medical data is confidential, or at least it should be. As The Guardian informs us, the total collected worth of the British NHS’s client data is in the billions. It raises the questions as to whether utilizing Big Data is more of a curse than a blessing.
Who Has Your Data?
Big Data is a double-edged sword for the healthcare industry. Not only is patient data a sensitive issue, but recent leaks in other sectors have raised doubts in the safety of user data overall. According to Forbes, in July 2019, the British Parliament agreed with Amazon to hand over NHS healthcare information to make searching for symptoms easier on non-specialists. This caused a massive uproar, as users had no say in what happened with their personal data. While it’s not a leak, it’s even worse since it abuses the trust of the citizen. While Big Data does bring with it a lot of things to be concerned about, it also offers a remarkably innovative way to help patients.
Introducing AI and Machine Learning
Artificial intelligence and machine learning are branches of modern technology that focus on using intelligent systems to solve problems. The application of AI to the medical field has shown moderate success. As The Financial Times notes, UCHealth, the company responsible for running several hospitals within the state of Colorado, depends upon computer surveillance to help doctors fight sepsis. There have been failures such as AI agents overpromising and failing to deliver in the past. However, as more healthcare companies start adopting AI and machine learning, there is an even broader scope for Big Data.
Using Big Data for Medicinal Sequencing
One of the emerging technologies related to Big Data in the field of healthcare is its use to point pharmaceutical developers in the right direction. In the past, sequencing data to develop a potential cure could take almost half a decade. Even then, there was no guarantee that the possible treatment would succeed or be safe in human trials. Big Data has allowed pharmaceutical companies to streamline their process, cutting down the time for the approval to less than three years.
These technologies usually depend upon a large volume of data – the kind that a healthcare provider can quickly generate from its Big Data stores. Using connected devices, IoT sensors, and smart feedback mechanisms, a healthcare provider can collect user data, which can then be forwarded to the AI or ML agent after being scrubbed. By introducing ML and AI into the equation, it reduces the chances that a human doctor would misdiagnose a patient. Artificial intelligence processing would then use the raw client data without needing to know who the patient is. This anonymity helps to protect the user’s information while still exposing the necessary data to the machine.
Big Data’s Place in Healthcare
Changing a paradigm of an entire industry takes a lot of time. It’s not as simple as getting an Avant Permanent Cosmetics agent to microblade away the bad parts of the system. Changing how medical practitioners see data and use technology requires getting them on board with embracing how that technology works. Big Data can be a handy tool in the right hands. However, just like other data-based industries, the security and anonymity of patients must come first. Big Data alongside AI and ML has the opportunity to revolutionize the way we approach medicine, if only we could manage to keep user data secure.
