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Big data implementation in the health sector

        Every day more data is generated. We store more information about each person, and we are even beginning to store more information about devices. The Internet of Things is not imaginary. Even our coffee maker will soon track our coffee habits and save them in the cloud, and then offer personalized recommendations and messages. 

Big data is a high-level term used to describe analysis techniques and systems that exploit the large volumes of data that are now captured by businesses
(Chaffey and Ellis-Chadwick p. 248)


In the Health sector, numerous heterogeneous data sources yield a large amount of information related to patients or other cases that Choong and Hyung-Jin (2017, p. 4 ) explain, such as claim records, clinical registries, biometric data, medical imaging, biomarker data, and extensive clinical trials. 





Genomics


According to WHO (2002), "the study of genes and their functions, and related techniques.” Therefore, this field includes the sequencing, mapping, and analysis of DNA and RNA to know what effect they have on diseases.

Costa (2012, p.3) states that individual genomics is crucial for predictive medicine, In which a patient’s genetic history can be worked to define the most suitable medical treatment. Furthermore, if genetic data is combined with other medical data, professionals and researchers will deliver a much deeper and more complete view of each patient's health.


Wearables


These devices can be managed to gather a considerable amount of data from users “making use of different behavioral and physiological sensors, which monitor their health status and activity levels” (Cilliers 2019, p. 1.). The novelty is that doctors and specialists can analyze this information. In this way, the patient can be tracked like never seen before, as Kooman et al. (2020) depict, an example of a chip that contains a multiparameter monitor for electrocardiography (ECG), photoplethysmography (PPG), pulse oximetry (SpO2), and bioimpedance, including memory, processor, and secure wireless communication.



Personalized medicine


    Alternatively, also called precision medicine, is the “incorporation of a wide array of individual data, including clinical, lifestyle, genetic and further biomarker information” (König et al., 2017, p.2). The amount of medical data collected in a person's medical history will increase exponentially. This will open a new door of knowledge for doctors to make tailored decisions for each patient.


On the other hand, a new era for data science begins. Thanks to the large volume of information available, health services will apply artificial intelligence techniques, such as machine learning, to perform advanced analytics and make decisions in real-time.


In conclusion, the emergence of new technical methods and the explosion in the volume of available medical data will lead to a revolution in the world of health and people's daily lives. However, this development presents new challenges, such as the management of privacy. Medical data is undoubtedly sensitive and must be stored with sufficient security and respect for users' confidentiality. Simultaneously, they must be easily accessible to be leveraged by specialists, often from different units or medical centers.

Only companies that will manage Big Data correctly will have the ability to offer their patients this added value, giving the specialists the ability to extract and manage large volumes of data to use it intelligently.


Will Big data become a trouble in the future regarding possible privacy issues? What do you think?


Written by: Carlos Sáez Muñoz



Chaffey D., Ellis-Chadwick F. (2019)  “Digital marketing – strategy, implementation, and practice.”


Choong H. and Hyung-Jin Y. (2017)  “Medical big data: promise and challenges.” [Online] Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5331970/pdf/krcp-36-003.pdf (Accessed: 22 February 2021)


Cilliers L. (2019) “Wearable devices in healthcare: Privacy and information security issues.” [Online] Available at https://www.researchgate.net/profile/Liezel-Cilliers/publication/333511479_Wearable_devices_in_healthcare_Privacy_and_information_security_issues/links/5cfa269f4585157d159912ff/Wearable-devices-in-healthcare-Privacy-and-information-security-issues.pdf (Accessed: 22 February 2021).


Costa, F. (2012) “Big Data in Genomics: Challenges and Solutions.” [Online] Available at http://genomicenterprise.com/yahoo_site_admin/assets/docs/Costa_GLJ11-1212_-_3.330103806.pdf (Accessed: 22 February 2021)


Kooman, J. Wieringa F., Han M., Chaudhuri S., M van der Sande F., Usvyat L. and Kotako P. (2020)  “Wearable health devices and personal area networks: can they improve outcomes in hemodialysis patients?” [Online]. Available at: https://academic.oup.com/ndt/article/35/Supplement_2/ii43/5803065 (Accessed: 22 February 2021).


König I., Fuchs O., Hansen G., Von Mutius E. and Kopp M. (2017) „What is precision medicine?“ [Online]. Available at: https://erj.ersjournals.com/content/erj/50/4/1700391.full.pdf (Accessed: 22 February 2021).



WHO (2002) “WHO definitions of genetics and genomics.” Available at: https://www.who.int/genomics/geneticsVSgenomics/en/ [Online] (Accessed: 22 February 2021)


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Comments

  1. Mandar Butkar22 March 2021 at 12:11

    The use of big data analytics is rapidly increasing. If you don't get ahead of the curve, there's a high risk of big problems; but if you do, there's a high chance of successfully enabling the business.
    You may be wondering what the big deal is — and what makes big data unique and more difficult. The problem is that big data analytics platforms are powered by massive amounts of often sensitive customer, product, partner, patient, and other data — data that typically lacks adequate data security and represents easy prey for cyber criminals.
    Sensitivities about big data security and privacy are a barrier that businesses must overcome.

    Good Data Carlos !


    ReplyDelete
  2. Prajakta Jadhav25 March 2021 at 15:57

    Big data today is more hybrid and multi-cloud structure, this data is spread across various platforms and locations. Privacy in Big data today has to be an integral part while planning cloud integration and data management strategy.
    Strict adherence to data governance policies and providing a specification along with managing the critical data is important factor. Understanding and classifying the sensitive data across available big data by leveraging AI and machine tool learning can help in big data management policies. Recurrent risk analysis for sensitive data, can highlight the risk exposure and help prioritize the available data protection and remediation. Process such as dynamic masking and encryption for big data that rests in data warehouses and data lakes can be addons to the protection capabilities.

    Great Article!

    Comment: Prajakta Jadhav

    ReplyDelete

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