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The Important Use of Big Data in Healthcare
Published at November 24, 2022
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The Internet of Things (IoT) in healthcare is a rapidly changing industry, and big data is driving innovation in the sector. Related to this, the IoT offers great potential for new business models and changes in how companies work.

By 2020, Malaysia's IoT market potential is expected to reach $2.2 billion. The vice chancellor of Universiti Teknikal, Prof. Dr Sharin bin Sahib, presented at the 6th Asia IoT Business Platform: IoT Malaysia and predicted that this growth will continue to increase exponentially beyond 2020 to reach $10.5 billion in 2025, according to AIBP.

Further, Vinod Khosla, a billionaire investor stated that data science and software would do more for medicine. In this context, Forbes magazine estimated that the IoT in Healthcare would be worth $117 billion globally by 2020.

Besides, the medical experts from Malaysia shared the belief that the perks of IoT in healthcare namely remote diagnosis, big data analytics, and integrated healthcare solutions could prevent the challenges of the healthcare systems.

The healthcare systems' challenges such as an ageing population, increasing cardiovascular disease, and stroke incidence rates, as well as rising chronic illness prevalence.

According to Dr Yau Teng Yan, IoT and big data offer the possibility to do away with traditional "trial and error medicine," enabling decision-making based on real-time data and a comprehensive understanding of the patient's environment and history.

What is Big Data in Healthcare?

According to the Journal of Big Data, big data refers to huge amounts of information that are impossible to manage with conventional software or web-based platforms. It has more processing, storage, and analytical power than is usually used. Despite the existence of numerous definitions of big data, Douglas Laney's is the most popular and well-known.

The development of software systems and their capabilities have led to widespread acceptance of the practice of digitization of all clinical exams and medical records in healthcare systems.

Big data in healthcare refers to the gathering, examination, and use of consumer, patient, physical, and clinical data that is too large or complicated to be understood by conventional data processing techniques.

What are examples of Big Data in Healthcare?

Examples of big data in healthcare include:

1. Electronic Health Records (EHR)

It’s the huge data type that is most frequently used in healthcare. Every patient has a personal digital file that contains, among other things, data on their demographics, medical history, allergies, and the results of laboratory tests.

2. Electronic Medical Records (EMR)

EMR is similar to EHR, EMR keeps the standard clinical and medical information gathered from the patients.

3. Pharmacy prescription

Records from a pharmacy, insurance claims and many other sources can be used to identify trends of overprescribing by medical providers. They can also be used to determine how many people take more medication than is prescribed.

4. Real-time monitoring

Real-time data collection has been used in a variety of applications, including the management of hospital beds, surgical day care units (procedural suites for prolonged periods of recovery), the supply and demand for bays, predicting the most likely day of discharge for a specific patient, and more.

5. Patient engagement

Patients or customers are interested in smart devices that continuously record their every move, heart rate, sleeping patterns, and other data. To find hidden health risks, all of this crucial data can be integrated with other trackable data, for example, on-demand healthcare apps.

The Benefits of Big Data in Healthcare?

Big Data can be used for a variety of purposes in healthcare. Some examples include:

  • A better understanding of patient populations and their needs.
  • Improving diagnosis and therapy to improve the patient's care.
  • Measuring the effectiveness of medical treatments and interventions.
  • Forming marketing and patient care proposals.
  • Predicting future healthcare needs for better treatment.
  • Reduce the costs of healthcare treatment.

The Three Characteristics of Big Data Analytics

There are some characteristics of big data analytics to provide insights into clinical data. For brevity’s sake, here are the three key characteristics you can understand, according to Healthyanalytics.

1 . Volume

Volume refers to “how much data”.

Clinical notes, claim data, lab results, gene sequences, medical device data, and imaging studies are all rich sources of information that can be integrated into interesting ways to yield unique insights.

To manage the volume of data available, organisations must design storage methods, either on-premises or in the cloud. Additionally, they must make sure that their infrastructure can support the next V on the list without impeding crucial operations like provider communications or EHR access.

2. Velocity

Velocity refers to “the data accessed, transferred, and created speeds”.

With the development of the IoT, medical devices, genomic testing, algorithms, natural language processing, and other innovative data production and processing techniques, the amount of data flowing over the world's wires devoted to healthcare will increase.

At the point of care, some of this data must be updated in real-time and displayed right away. System response time is an important statistic for organisations in these situations and can give such goods products.

3. Variety

Variety refers to “the different types of source”.

The more sorts of information you can combine, the richer the insights will be. However, meaningful data comes in all shapes and sizes. Healthcare organisations are collecting data from numerous sources, and their current goal is to figure out how to combine that data.

When data sets are stored in different places or use different formats, it’s impossible to compare them, which limits the insights providers can learn about their patients or operations.

Application programming interfaces (APIs) and new standards like fast healthcare interoperability resources (FHIR), which make it simpler to overcome walled gardens and increase variety, are helping health IT developers begin to deconstruct the issue.

Conclusion

Big data analytics has more applications and advantages every year, and the adoption rate implies that practitioners are starting to pay attention. For providers, there are still obstacles to get over, especially for compliance and data protection.

These important advantages can only become more effective as technology advances; data is the key to the healthcare of the future.

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