Healthcare emerges as one of the promising sectors where big data analytics and its applications could receive their own recognition.
Industrial needs for big data in the healthcare sector
The healthcare domain has been historically known as a generator of large volumes of data, driven by patient care, record keeping, and compliance requirements. In healthcare systems, data generation has already reached exabyte levels (with 1 exabyte equal to 1 billion gigabytes).
Increased demand for healthcare coverage, escalating healthcare expenditure, yearning for less labor-intensive filing processes, etc. prompt the need for digitizing these copious amounts of information, known as big data, and analyzing them effectively, to enhance the overall effectiveness and quality of medical care delivery.
Big datasets are, yet, so abundant and complex that they are difficult, or even impossible to handle using traditional data management tools. This is where the adoption of big data analytics in healthcare becomes crucial. In fact, it is expected that by 2022, the global healthcare big data market will increase gradually at a compound annual growth rate off 22.07%.
3 Ways Healthcare Can Extract Values from Big Data Analytics
The applications of big data analytics in the clinical sphere are threefold:
1. Allowing personalized medicine
Patients often expect to avail a wide range of medical care services at a reasonable cost with tailored suggestions. Precision or personalized treatment is a new approach that takes into account a patient’s lifestyle, genetics, etc., to decide the most appropriate course of treatment. Big data fosters novel opportunities to provide patients with customized careby controlling medications, supervising patients, etc. For instance, the Covid-19 pandemic has overwhelmed global health systems, urging hospitals to postpone scheduled treatments and surgeries. A mobile application enabled by big data analytics could carry out a personalized risk assessment of patients waiting for surgeries and prioritize patients based on the level of medical urgency.
2. Optimizing hospital operations
Business leaders have relied on big data to recognize consumers' behavioral patterns and produce customer-centric business solutions. In the clinical sphere, big data analytics could be deployed to identify patterns and thereby optimize hospital operations. For example, four hospitals in Paris have been relying on massive amounts of data from various sources to put forward daily predictions of the number of patients expected to be at each hospital.
Another pressing issue in hospital management is data security. As patient records yield valuable personal information, they are a prime target for cyber-attacks. Big data analytics might hold promise to reinforce data security due to its ability to identify and flag any change in behavior or network traffic that suggests a cybercrime.
3. Increasing early diagnosis and treatment
The threats of new disease variants and recent improvements in data technology have placed early diagnosis at the center of attention. Traditionally, when making a diagnosis, physicians usually rely on their patients’ personal data such as their medical history, lab tests.
However, as of now, by using data extracted from a plethora of electronic medical records, DNA and RNA sequencing, metabolomics, proteomics, etc., complex algorithms can pinpoint patterns with meaningful diagnostic information. These large volumes of data could also be used to compare with historical groups of patients who had been diagnosed and treated and then decide the appropriate course of treatments.
The embracing of big data analytics in healthcare seems to lag behind other sectors due to obstacles such as medical confidentiality, budget constraints, data silos, etc. However, despite such challenges, the aptness of big data analytics to add values to the healthcare domain are becoming increasingly evident.
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Catalyst (2018). Healthcare Big Data and the Promise of Value-Based Care.
Forbes (2019). How Healthcare Is Using Big Data and AI to Cure Disease.
Khanra et al. (2020). Big data analytics in healthcare: a systematic literature review.
Zillner and Neururer (2016). Big Data in the Health Sector.