There is heaps-full of data existing in the healthcare domain that has been generated historically, by means of record keeping, compliance & regulatory requirements, and patient care. (1) The current trend suggests faster digitization of this large amount of data, known as ‘Big Data’, that have been stored as hard copies over time. Big Data can facilitate a wide range of medical and healthcare functions, such as clinical decision support, disease surveillance, and population health management; with primary goals of providing better quality of healthcare delivery as well as reducing the costs. (2,3)

Big data are often identified by 5V’s in terms of Volume, Velocity, Variety, Value, and Veracity. The patient data collected often amount to peta or zeta bytes in volume. The speed and rate at which data is received from the patients explains the velocity. The miscellaneous data sets classified as the structured, semi-structured and unstructured data sets like clinical reports, EHRs, and radiological images represent variety; whereas veracity is when the true and reliable data sets with accessible and genuine data are provided. The collected data are transformed to provide meaningful understanding, thus explaining their significance in 5V’s. (4)

While multiple discussions on healthcare data are focusing excessively on probable risks and misuses of the data, there are also enormous profits from extending healthcare data usage. A whole host of use cases are available to prove the widespread value being created by data analytics, across all stakeholders of the healthcare system, including patients, healthcare professionals and providers, payers, researchers, biopharmaceuticals and medical device companies, and regulatory authorities. Patient and healthcare associations in the past, while discussing the big data applications, have focused on the potential risks linked to exploitation of personal health records (such as predictions of individual or family health risks). Progressively, however, patients are accepting the countless benefits of data analytics, while still being vigilant about possible risks linked to data misuse. (5)

Data analytics, in conjunction with latest technologies, can help healthcare providers expand care pathways and services “beyond their walls”. For example, new measurement devices (such as wearable, ingestible or implantable sensors) can convey data that will prompt a provider to determine patient crisis. For instance, a provider can foresee and avert any complications like diabetic foot and evade amputation by monitoring vital signs in diabetic patients. In psychiatric or neurological patients, accurate supervision of a combination of indicators can provide better certainty of a crisis. Growing number of continuous monitoring services that rely remarkably on connected objects and data analytics are already altering care paradigm for chronic patients. Examples of these include Bioserenity solutions for epilepsy, Ginger.io for chronic conditions, several congestive heart failure programs being undertaken worldwide, or Diabeo for individualized insulin dosing.(5)

As we can see, data analytics has the potential to put together predictive models and categorize patients based on the probable/future healthcare risk they might carry, in order to modify treatment protocols to their profile. These models are crucial in deciding the success of disease management programs. With increasing digital technologies, life sciences and healthcare are on the verge of a revolution. Continuous improvement in the global and quick analysis methods, initiated with genome sequencing, along with the increasing digitization of vast information, now creates substantial quantity of data. The exploitation and analysis of these big data create new prospects, while also helping address technological, scientific and medical challenges.

References

  1. Raghupathi W. Data Mining in Health Care. In: Kudyba S, editor. Healthcare Informatics: Improving Efficiency and Productivity. 2010. pp. 211–223.
  2. Burghard C. Big Data and Analytics Key to Accountable Care Success. 2012.
  3. Fernandes L, O’Connor M, Weaver V. J AHIMA. 2012. Big data, bigger outcomes; pp. 38–42.
  4. Sahoo PK, et al. Analyzing Healthcare Big Data With Prediction for Future Health Condition. IEEE 2017; 4:9786-9799.
  5. Unlocking the full potential of data analytics for the benefit of all. Healthcare Data Institute. November, 2015. 

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