During the industrial revolution, oil was gold. It was the driver of the revolution, a marker of wealth, and the main ingredient for economic growth. In the digital revolution and the internet of things age, data is the new oil: it fuels and shapes decisions, strategies, and results in all sectors, including the health sector.
Data routinely generated from patient records, research labs, pharmaceutical companies, and health insurance organizations are now used to improve all Healthcare components. Prevention, diagnostics, treatment, and health system efficiency have all been revolutionized by big data’s smart use.
What is Big Data?
Big data is the large volume of structured and unstructured data generated in a business entity or organization daily, which can be analyzed to enhance decision making.
Big data in Healthcare is caused by personal apps, medical records, hospitals, research labs, health tech organizations, healthcare organizations, pharmaceutical companies, and any entity with access to health information.
How is Big Data used in Healthcare?
Big data has several applications in Healthcare. Some of them include:
Epidemiologic Modelling
The world witnessed an unprecedented disruption with the outbreak of the COVID-19 pandemic. During the pandemic, big data helped predict the rate of infection and monitor the effectiveness of control measures. One of the establishments that used real-time data to provide insights during the pandemic was the John Hopkins COVID-19 global resource center which used big data to provide projections and global estimates of COVID-19.
Big data made the epidemiologic modeling of the COVID-19 disease possible. Using big data, scientists can predict and track disease outbreaks, just like the example above. The COVID-19 pandemic has been totally devastating, but it is unlikely to be the last. As the world prepares to face the pandemic threats of the future, epidemiologic modeling will become more relevant in keeping the world healthy.
Genetic Engineering
Medical practice has evolved. Doctors can now use the congenital abnormalities behind a disease process to treating it. Big data plays a role in understanding the diseases’ genetic basis by looking at how genes, tissues, and cells interact to cause disease.
More often than not, complex diseases like cancer, Parkinson’s disease, and various metabolic conditions result from a combination of genetic abnormalities. These abnormalities are best understood by using machine learning approaches to analyze big data. When these genetic processes are well understood, precision medicine can be combined to ensure the best patient outcome.
Precision Medicine
Precision Medicine involves a care model in which every patient is treated as an individual and gets specific treatment. Predictive analytics using big data makes this easier by using analyzed electronic health records from patients treated to decide what is best for patient care. Its application in precision medicine is more relevant in the aspect of pharmacogenetics in cancer therapy.
Remote Patient Monitoring
Remote Patient Monitoring fulfills the dual role of prevention and management of chronic diseases. It involves using wearable devices, which help in data collection. They do this by reading and transmitting biological parameters like blood pressure, pulse, heart rate, and physical activity.
Wearable devices also help in monitoring cardiac patients’ activity levels. This makes it possible for physicians to monitor patients without necessarily prolonging their hospital stay. This reduces the risks associated with prolonged hospitalization and improves patient outcomes.
Intelligent Drug Design
Drug discovery is one aspect of health that has been significantly affected by big data. The past decade has witnessed the launch of public depositories of chemical structures and potential drug candidates. These depositories consist of hundreds of millions of chemical structures, drugs, and drug candidates.
These depositories help drug companies designing and recruiting new drug candidates for clinical trials and increasing the success rates of drug compounds – a more economical solution. An example of how big data facilitates drug development is the speed of development of the Covid-19 vaccine.
Population Screening
Big data has also found application in the prevention of diseases through screening of populations. Big data helps with the identification of populations with high-risk patients for non-communicable diseases, including cancers. This allows for targeted screening, thereby improving the cost-effectiveness and efficiency of the screening process.
Improving the Health System’s Efficiency
Efficiency is critical to ensure the sustainability and effectiveness of the healthcare sector; this is why the loss of $260 billion (about 6.19% of global healthcare expenditure) annually is unjustifiable. Some of these losses are due to the system’s inefficiencies, while others result from deliberate perpetuations of fraud and abuse.
In either case, big data analytics and machine learning have become relevant tools in tackling fraud in health systems. Big data and machine learning identify patterns of fraud and abuse or wasteful claims at regular intervals. This ensures that these transactions are identified and stopped before payments are made. Big data analytics required for fraud and abuse is advanced and requires a layered approach to implement.
Challenges to The Use of Big Data in Transforming Healthcare
Nature of Medical Data
Unlike Data generated by entertainment and other technological giants, health data are by nature confidential. When these data are used in analytics, it can result in litigation. A recent example is Flo’s case, a period and ovulation tracking app that had to settle the Federal Trade Commission for sharing data with third parties, Google and Facebook inclusive.
Infrastructural Challenges
Structure and unstructured data sets in a data lake will not yield anything unless analyzed. The amounts of data generated are massive, and analysis requires a considerable level of cloud computing infrastructure and technical know-how that is not readily available in many developing nations.
Conclusion
Despite its implementation and scale-up challenges, Big data use has been a transformative tool for 21st-Century Healthcare. Big data has permanently changed Healthcare and will continue to influence modeling, research, prevention, and practice. Big data is taking away the guesswork and uncertainty in Healthcare and replacing it with facts and figures.
Humanity can look forward to a future where doctors have the tools to prevent disease before it even occurs and to adopt the best approach to care when prevention fails. Big data in medicine is the present and the future. Efforts need to be made to surmount the associated challenges during the transformation of the Healthcare industry one kilobyte at a time.
