The potential of Artificial Intelligence and the advantages of Big Data are growing exponentially in every industry. We can now monitor business progress for years in the future or create an unbelievably predictive customer experience, learning who people are on Google, what their preferences, desires, and purchases. It all became way easier with analytics at hand. AI advances, medical breakthroughs, hardware tools, and new algorithms in medicine are highly praised as well. Medicine has always been at the center of investigation, attention, and investment for scientists and developers. However, it is all about data that is the heart of technology in Healthcare. Without Big Data in healthcare vaccines would be ineffective, treatment would probably be much worse and take much more time to be developed and tested. We have been discussing what influence Big data has on Education, and now let’s see how people have combined Big Data with Medicine and what exact benefits of big data in healthcare we receive nowadays.
What is big data in healthcare?
Natural language text (electronic health records about what you eat
how you exercise as an e.g.) is transformed into clinically important descriptions and medical terminology. Personal healthcare information is withdrawn from different resources like wearables, mobile applications, EMR and EHR systems. It is classified into:
- Clinical data. Include patient/disease registries, clinician practice, information like culture, ethnicity, geography, the demography of people
- EHR. Usually include demographics, medical history, medication, laboratory test results, allergies, immunization status, radiology images, personal physical like age and weight, and billing information (find out all the EHR advantages and disadvantages here)
- Omics data which is a molecular characterization of a person, a richer picture of clinical status
Data engineers deal more with tools to discover data and the way to structure it before it is examined by data scientists in healthcare. The process of data mining and data analytics in healthcare involves bioinformatics and demand specialists to be knowledgeable at R, Python, linear algebra, probability, and calculus. The problem with data in Healthcare is that it is growing and extending across different locations. Data scientists have to find a creative way to make this data accessible, to find solutions for rural areas and third world countries.
The vital role of Big data in healthcare is used to combat infections. Today coronavirus disease has made the largest Universities and research institutions to drop everything and launch the inspection of COVID-19 nature and cure for the virus. The impact of technologies and AI on combatting coronavirus is critical and it is one more proof of how Big Data is the world’s best hope to find a vaccine against the virus.
The world-scale examples are Oxford University’s Big Data Institute, Palantir, Microsoft Research, Harvard University, Health Data Research UK, Cambridge Research Institute, The University of Texas Medical Branch, the Massachusetts Institute of Technology, Boston University, the University of Washington’s Institute for Protein Design and these are just a few on the list.
The future of big data is a hot topic and challenge for the software industry leaders. By 2025 almost 50% of data will be public and personalization of data can become an issue for enterprises not being able to store an exponential amount of datasets. IDC predicts that 30% of data will be real-time, which means data will be processed by consumers and organizations on the spot, allowing them to take action immediately.
Instant access to real-time health data
Big data hype is around streaming and tracking in real-time the spread of diseases to investigate where they originated and, using specially developed algorithms, predict where and when they may advance.
Since then, a method of tracking the movement of diseases by gathering call data records from national mobile network operators was developed. During an outbreak, they can analyze phone calls and text messages for health information and updates on local, regional, and national scales.
Using machine learning approaches and drawing useful insights from data to communicate with lab scientists and to communicate clearly and transparently with patients and the public. Nowadays during the dangerous pandemic caused by Covid-19 bodies pharmaceutical companies, international healthcare consultancies, health tech SMEs and multinational software companies use data to inform society and minotaur virus spread and changes in the number of infected people. In China, thermal-tracking screens measure people’s body temperature and if it is not a norm, people suspected of transmitting the virus and all those people which were in contact with them on board of the plane or somewhere else are contacted and recommended to self-isolate immediately.
Find everything about EMR integration systems in healthcare!
Federated Analytics for breakthrough researches
Cloud storage solutions mark a step forward in protecting medical records if we compare what the medicine was some 10-20 years ago and where it was kept. Nevertheless, there is a huge concern about how to expand the availability and integration of big data in healthcare. The great amount of information is available among silos of hospitals, doctors’ offices, research labs, government databases, and health insurance providers. Tremendous amounts of data come from wearables, genome sequencing centers and private clinics also possess data that could replenish cloud storage with rare information.
To some extent, it restricts people from owning their data to access this data, kept at highly secured servers. Scientists could take risks and make the data more open, more available following the concept of federated analytics. Federated data would allow data scientists to generate considerable insight from the combined information in distributed places easily. Federated Machine Learning is a term firstly coined by Google in 2016 and the idea is that such federated machines do not move data at all and does not require to migrate data to a centralized location. On the other hand, we have HIPAA compliance, we contemplate how medical data becomes so valuable and sensible it is exposed to hackers attacks. Federate analytics is a future of Big Data and still has room for improving, especially in terms of privacy.
Increased transparency and personalized care
Modern technologies across the world allow us to keep data safe, analyze it accurately, so ultimately people receive better-personalized care. To put it simply, data scientists collect data of patients, researchers compare it with the data of millions of other people, distinguish differences, and find where anomalies occur. Assigned treatment for particular diseases is prescribed and results will be later sent to the database, again compared to the results of patients cured similarly some 2 years ago. In such a way, medicine receives a chance to find out what influences the health state of people and what has improved with treatment over the years.
Preventative over the reactive care
Big Data science can help establish proactive health care instead of reactive that is already treating illness. That means that data from all sources will be consolidated, analyzed and results will be shown in the healthcare software. Innovations like voice recording apps, used to analyze voice records and prevent cardiovascular diseases, can help us shift medical practices to a cost-effective level in all senses. Through such platforms, doctors can track symptoms, check-in on the recovery progress, ask follow-up questions, and even collaborate with other doctors for advice.
Preventative medicine can be achieved if people choose a road to adopt globally Medtech solutions to their lives and simply install software for their smartphones. Either aging or young people will benefit from software that can predict the sharpening of diseases they have. It would push them to turn for medical help before the situation is critical and needs surgery. Data analytics in healthcare is an ideal tool that gives digital solutions the core value – to preserve people’s lives.
Welcome to Telemedicine
Big Data in health care saves time, money, and energy of both doctors and patients. The inventory says that during the 3 years treatment course, one woman visited the hospital over 900 times. That situation occurred in Oakland, California, where a woman who suffers from mental illness and substance abuse went to a variety of local hospitals on an almost daily basis. The problem was in lack of shared medical records between local emergency rooms and hospitals. This situation is met all over the world, especially in third world countries.
Due to the recent boom in the development of healthcare apps, people have more possibilities to monitor their health and receive consultations remotely. AI algorithms and NLP tools empower doctors with the ability to refine and aggregate personal patient data and deduce from their meaningful and actionable interpretations. Fitness and sleep trackers along with numerous other health monitors provide doctors with a more precise view of the health conditions of the patients. It gives users the ability to video chat with board-certified physicians and psychologists to have your health questions answered and in some cases even receive a written prescription.
Even though remote healthcare is still in its infancy, the emergence of telehealth technology serves to lower healthcare costs and lead to healthcare improvement. Also, mobile healthcare applications are a great solution to make communication between doctors and patients the way easier. The user can check in with the doctor and even send them picture messages through a secure messaging app.
Biomedical engineering and Automated surgery management
The implementation of Big Data in the medical sphere aids to solve the issues of time, money and energy for physicians and surgeons easily. Imagine a healthcare solution that assists medical specialists scattered across countries in 3D modeling and printing of surgical implants and managing the flow of their work on the model via one platform. Software engineers work side-by-side with scientists and researchers in Healthcare to create solutions that automate every piece of surgeons’ work. The relationship of Big Data and software development is considerable to find drugs in time, to diagnose faster, to make data analytics in healthcare better than ever.
All in all, investment in Big Data analytics, promoting healthcare apps, creating accessible data sets for medical discoveries will not only leverage healthcare systems globally but will expand the advantages of Big Data across a variety of related domains.