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    Big Data application in Healthcare industry

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    Anything that involves maintenance or improvement of health through prevention, diagnosis, and treatment of disease, illness, etc. falls under the domain of healthcare. Healthcare data The data which is related to health conditions, reproductive outcomes, causes of death, and quality of life for a person or a group as a whole is considered as healthcare data. It includes clinical data along with environmental, socioeconomic, and behavioral information related to the health of an individual. A Huge amount of data is collected because of human interaction with health care systems.

    Healthcare Automation

    Automation has played major role in almost all the industrial domains making the process much easier compared to traditional methods. It has led to an improvement in the quality of life of the human population. People now have more choices and low costs because of automation. One of the reasons for an increase in the availability of data in the healthcare domain is automation. It has led us to more digitalization which indeed leads to more generation of data. And upon its analysis, we can make a smart inference and improve our quality of life.

    Big Data Application in Healthcare:

    Big data is a result of a large amount of information created by the digitization of data, which is then consolidated and analyzed for specific purposes. When applied to healthcare, it uses health data of people or an individual and helps us in preventing disease, cutting down costs, etc. Few goals which can be achieved using Big Data Applications in Health care are:

    1. Better treatment for patient which results in patient satisfaction.
    2. Improvement of the health of the population as a whole.
    3. Reduction in healthcare costs.



    • Volume defines that how huge is something that we measure and surely by the name “Big “data we can say that the data volume is indeed huge, and it is said that this data will increase in size even more in the coming years.
    • The Electronic Health Record is an important tool in the Healthcare industry. We can store and collect data from this system where individual patient’s data is entered and maintained like, medical test, doctor’s prescription, drugs used, images etc.
    • EHR is said to be large scale and complex data representation. In 2012, worldwide digital healthcare data was estimated to be equal to 500 petabytes and is expected to reach 25,000 petabytes in 2021.


    • Variety is one the most interesting developments in technology as more and more information is digitized.
    • Data stored in the health records can be structured, semi-structured or unstructured which is gathered from various sources like Patient/Member conversations, Health Community Blogs, Social Media, EHR records.
    • The EHR groups inhomogeneous data, i.e., on the one hand structured data in the form of standardized medical info, such as DICOM, or using the ICD codes, but on the other hand the most valuable data could be found in doctor’s notes written in natural language.


    • Veracity deals with the uncertainty of the data that is present with the big data scientist. The data that represents patient’s information needs to be accurate and based on that the decisions about the treatment are to be made.
    • Most of the data that we deal with in Health care Industry is full of noise and human errors, also the data is not always complete. Kohn et al. [1] report that due to clinical mistakes which were avoidable each year patients die 44 000 98 000 in USA. Therefore, methods to detect wrong treatments and/or diagnose and clean the EHR are still being developed.


    • Velocity is the frequency of incoming data that needs to be processed. •Data is in motion, new information about patient is added, some medical records are updated, and a lot of other patient related data is filled in the system and needs to be analyzed.
    • The analysis of the data stream seems to be one of the crucial topics for data scientists.
    • There is a need to process the data in real time coming from streaming data like Remote Patient Monitoring, data from sensor devices, Telemedicine etc.


    Data interoperability is a major concern for organizations of all types, sizes, and positions along the data development era. The differences in the designing and developing of Electronic Health Records (EHR) has restrained the ability to move data between various organizations, which often creates a problem for the clinicians as there is not enough information they need to make key decisions, follow up with patients, and develop strategies to improve overall outcomes. The Healthcare industries are currently working hard to the data interoperability and get rid of the organizational obstacles. Developers now share data easily and securely using tools and strategies like FHIR and public APIs, also partnerships like Common Well and Carequality helps to easily share data. To adopt these strategies and methodologies for secure and easy transfer of data the organizations need to have enough commitment, Funding, communication with the organizations. All the Organizations cannot meet the above requirements; hence they lag in sharing of patient data.


    Using Electronic Health Record (EHR) platforms, integrated medical devices, and health information exchange (HIE), the health informatics professionals try to analyze, manage and communicate the data that is received from various healthcare facilities HIE is important in today’s healthcare industry. It is necessary for interoperability and merging it with the informatics, we can achieve meaningful health information interoperability. HIE is making advances in the data sharing concepts. In this we keep EHR systems as a center and whenever any data is need for a patient, or if any EHR automatically checks a state immunization registry, or when a provider checks the prescription monitoring database all the data needs to be accessed by the Providers native EHR.


    Healthcare organizations are building their IT infrastructures to be more flexible and scalable to meet the growing data demand. With value-based incentives for data analytics and the increased number of connected media devices constantly collecting data, organizations are challenged with storing clinical data in a way that is both HIPAA-compliant (Health Insurance Portability and Accountability Act of 1996) and easy for authorized users to access. There are three healthcare data storage options:

    • On-Premises data centers guarantee you an easy access, control and security for storage of data. But they are expensive to scale and maintain.
    • Cloud storage is useful for extension, disaster recovery, requires less cost but some of its issue is with the healthcare centers compliance and the security issues in which we need to carefully choose a cloud partner that understand the essence of HIPPA.
    • The hybrid data storage seems to be the best option at this moment it’s a combination of public and private cloud service models which can be the best from security perspective and easy for accessing data.


    Organizations need to ensure their data is stored securely and is accessible to protect patient data. Clinicians must also have access to data where and when they need it for a successful data storage option. In this regard:

    • Organizations can choose to store the sensitive and more bandwidth intensive data, such as images on an on-premises servers so they can be accessed quickly.
    • Caching data locally and using WAN optimization techniques to reduce data traffic. This often means use of a least recently used, or LRU, algorithm, where the most recently accessed data stays in cache and is expired or replaced with newer data over time.
    • Using multiple cloud vendors and service models to host different parts of their data centers or multi-cloud storage models. Provide security based on the provider’s authentication mechanism. Also, encrypting data in transit across the network.
    • Using Hyper-convergence that can also be run in a cloud or on-premise environment and lets IT administrators control all virtual deployments from one place. This allows for less user error and faster technology speeds.

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    Big Data application in Healthcare industry. (2021, Aug 26). Retrieved from

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