The Role of Big Data in Healthcare

One of the most significant applications of big data in healthcare is its ability to improve patient outcomes through personalised care.

Big Data refers to a vast amount of data that is such that traditional data processing tools cannot manage them. The vastness and complexity of the data are part of the attributes that require it to have advanced storage, analysis and processing needs. These data are generated in torrents at rapid intervals, encompassing; structured, semi-structured and unstructured data, with sources spanning various sectors and industries. Big Data are often characterised by their; volume, variety, velocity, value, veracity and variability.

The term ‘Big Data’ came to be in the early 2000s and become a fad in the lexicon of the digital transformation era by the mid2010s. The digital transformation era saw the emergence of big data which has become something of a game changer across various sectors, healthcare inclusive. The avalanche of data generated almost daily poses a great potential to revolutionalize healthcare delivery, leading to improved patient outcomes, population health benefits, and cost reductions, amongst others.

Harnessing big data analytics can unlock tremendous insights from electronic health records, medical imaging, wearable devices, health IoT devices, and genetic information, empowering healthcare providers to make informed decisions, enhance treatment plans, and optimize resource allocation. We explore the role of big data in healthcare and its transformative potential in the industry. One of the most significant applications of big data in healthcare is its ability to improve patient outcomes through personalised care. Healthcare providers can identify patterns and trends from personalised health information, in arriving at a targeted holistic approach towards optimized healthcare delivery. Through big data analytics, healthcare providers can identify the best treatment regimen for individuals based on their medical history and other factors, leading to better outcomes and a higher quality of life. For example, it can help them identify patients who are at risk of developing certain conditions.

Through early identification, they can take adequate precautionary measures to reduce the risk of the patient developing the condition. It can also augment and optimize the diagnosis process, by comparing a patient’s medical history and symptoms against a vast database of similar cases, leading to a more comprehensive diagnosis. Another foremost application of big data analytics is in population health management. By analyzing aggregated data from a particular focus group, bloc, or community, policymakers and healthcare providers can identify trends, patterns and risk factors within specific population demographics that can help in developing targeted interventions and preventive strategies in furtherance of population health benefits.

For instance, big data analytics can help in identifying communities at higher risk of chronic diseases, such as; diabetes, cancer or cardiovascular conditions and communities plagued with unique diseases. By proactively targeting this population with sensitization, education campaigns and early screening programs, healthcare providers can prevent these diseases. Big data analytics can be deployed towards disease surveillance, in predicting and proactively curbing the outbreak of a pandemic, reducing the burden on the health system in the long run.

The role of big data analytics cannot be glossed over in the area of medical research. It offers medical researchers unprecedented access to a large volume of data and means of collecting this data. Pharmaceutical companies can use this data to optimize their clinical studies and trials. They can identify patterns and trends in predicting the efficacy of a medication for a particular patient group. In turn, this data can drive big medical breakthroughs and advancements that save lives By pairing big data with advanced analytics techniques like machine learning, medical researchers can create predictive models with vast applications such as; IBM Watson Health

The application of big data in healthcare can lead to tremendous cost savings and improved operational efficiencies without compromising on quality. By analyzing historic data on patient flow and hospital admissions, healthcare providers can allocate staff and resources more efficiently. For example, big data analytics can help providers identify patients who are at risk of re-admission. Through early identification, steps can be taken to prevent readmission, leading to cost savings Big data analytics can also help providers in identifying areas of waste reduction. By analyzing data on medical procedures and treatments, healthcare providers can identify areas where unnecessary tests or treatments are being performed, equally translating to cost savings. The cited instances are just a few of the practical applications of big data analytics in healthcare and the role it plays therein and this role is rapidly expanding, presenting numerous other opportunities for improving patient care, optimizing service delivery, medical advancements and cost savings. Despite its potential benefits, big data also poses challenges and concerns that must be addressed such as; the ethical concerns surrounding patient data privacy, as healthcare providers must ensure patient data is not shared or used without their consent.

You most certainly cannot shy away from security when you talk about data privacy. As such, providers need to ensure that patient data –due to its sensitive nature, is stored and processed securely to prevent breaches. Providers must equally invest in robust data governance policies & frameworks, data transparency, and necessary infrastructure and expertise Another challenge with big data in healthcare that is often overlooked is data integration. Healthcare data – due to their various origins are often non-federated and in diverse formats, making it challenging to integrate and analyze. Healthcare providers need to overcome these impediments, making the data integrated and standardized, if they are to make meaningful insights with it.

With continuous advancements in technology and the evolution of data collection methods, the healthcare industry stands to benefit significantly from the insights derived from big data analytics.

However, a few of the highlighted challenges such as; data privacy and security, data quality, and data integration need to be addressed with a combination of the right tools and strategies. By so doing, providers can fully harness the value of big data in healthcare and usher in a new dawn of precision medicine, proactive healthcare management, and cost-effective healthcare.