Abstract
With the improvement in technology, the data acquisition capability, its storage and analysis have gone up manifold. However, the
costs related to such activities have gone down significantly. This leads to the abundance of data everywhere. Notwithstanding the data omnipresence,
users process data selectively, take intuitive decisions, work under both data-overload and data-poverty.
Arguably, the healthcare sector has enormous challenges in achieving universal basic health services, providing safe, effective, affordable and
timely intervention for patients. The sector involves communication between various stakeholders such as government, healthcare institutions,
doctors, patients, and insurance companies. This byzantine, recursive, and helical process generates enormous data.
In healthcare practice one of the most important issues is that the big data creation is not purposive; availability of data may not be for the
purpose for its use. Secondly, if less data is equally sufficient to make a good quality decision, then big data may bring in confusion. Thirdly, at
a conceptual level, statistics relies on effective sampling methods to generalize and predict about population. Big data analytics also uses above
principles, indicating that the availability of data alone is not enough. Another daunting challenge is data ownership; data gatherers’ claim of
exclusivity is unethical which raises questions about privacy, user rights, and public ownership etc. From the business model perspective, the
data creation becomes free and thus marginal propensity to consume increases exponentially.
This paper takes a contrarian approach and presents the challenges and critics towards the implementation of big data processes in healthcare.
Keywords: Big Data, Healthcare, Analytics, Patient-Centric, Governance, Signal to Noise
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