Big Data

The Future Challenges Of Big Data In Healthcare

Nastel® Technologies
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Advancements in the technological capabilities of data generation, from sequencing DNA to health watches, have led to the phenomenon of big data. Big data refers to data that is rapidly generated, remarkably large and difficult to accurately interpret. Access to these kinds of data stores is revolutionizing many industries, such as banking, agriculture and science. As a result of their applications in healthcare, the field of life sciences is becoming one of the biggest users of supercomputers, which are being used to effectively store, manage and interpret data.

The Covid-19 pandemic has especially highlighted the potential of utilizing technology to increase efficiency with remote patient care and telehealth. Considering the popularity of virtual health; it is clear that the healthcare industry will increasingly rely on artificial intelligence and big data to improve gaps in our healthcare system. The switch to electronic health records in clinics has opened up the possibility of applying data models to actively use this information to provide proactive healthcare instead of this information remaining as a large store. Thus, the concept of a “data-driven physician” is gaining traction, since physicians can access more clinical data than ever before. Clinicians could then have earlier access to critical health information that enables them to manage conditions before a crisis occurs and mitigate a poor prognosis.

Big data in healthcare could serve a multitude of purposes. Similar to the banking industry, AI and data models could be used to recognize both external (third parties) and internal (unauthorized healthcare workers) patient data breaches. In terms of patient care, analytic models are being developed and tested for risk prediction and diagnostic accuracy, ultimately working to minimize physician errors. These processes could be continued to be developed to scan a patient’s file for lab values and other determinants then notify the doctor which patients are most at risk for certain diseases. Furthermore, the use of this data could contribute to placing more emphasis on dry lab practices compared to wet lab practices, which could be more economical. Overall, AI could be used to turn mass stores of patient data into proactive solutions that assist physicians with offering a higher level of comprehensive healthcare.

While future possibilities of implementing AI are incredibly exciting, big data in healthcare has unique challenges. Laws and regulations regarding patient privacy rights would need to be amended in order to allow patient data to be utilized as an asset. Policies that intend to protect patient information must guide data collection, data transformation, data modeling and knowledge creation. Furthermore, infrastructural changes to electronic health records and digital tools are needed in order to establish consistent data entry practices among healthcare providers and clinics (in order to minimize human error).

In addition to the unique challenges in healthcare, big data also struggles with the same challenges when integrating into other industries. The major challenge in several fields has changed from finding ways of gathering data to understanding how to effectively interpret and leverage the data. Advancements in computational biology are essential to be able to store the datasets of potentially every human who has access to modern-day healthcare. In addition to the complexity of managing and storing this data, computers that are capable of doing so are incredibly expensive and space-consuming.

Incredible discoveries in the science community have made data collection of individual health measures simple, from everyday wearables that measure vitals to mouth swabs that map out a genome. The simplicity of collecting data has shifted the focus from gathering patient information to establishing clear directions for the management, storage and interpretation of this information. Efforts made to streamline the usage of this data could have massive economical impacts on the healthcare system, improve the efficiency of healthcare teams and considerably improve patient health outcomes.

This article originally appeared on forbes.com, to read the full article, click here.

Nastel Technologies helps companies achieve flawless delivery of digital services powered by middleware. Nastel delivers Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate. To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s Navigator X fuses:

  • Advanced predictive anomaly detection, Bayesian Classification, and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

 

Nastel Technologies, a global leader in integration infrastructure (i2) and transaction management for mission-critical applications, helps companies achieve flawless delivery of digital services.

 

Nastel delivers Integration Infrastructure Management (i2M), Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate. To answer business-centric questions and provide actionable guidance for decision-makers.
The Nastel Platform delivers:

  • Integration Infrastructure Management (i2M)
  • Predictive and Proactive anomaly detection that virtually eliminates war room scenarios and improves root cause analysis
  • Self-service for DevOps and CI: CD teams to achieve their speed to market goals
  • Advanced reporting and alerting for business, IT, compliance, and security purposes
  • Decision Support (DSS) for business and IT
  • Visualization of end-to-end user experiences through the entire application stack
  • Innovative Machine Learning AI to compare real-time to the historical record and discover and remediate events before they are critical
  • Large scale, high-performance complex event processing that delivers tracing, tracking, and stitching of all forms of machine data
  • And much more

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