This paper enumerates the structural and cultural challenges of Big Data implementation in Healthcare organizations in the hierarchy of numbers, juxtaposed along the binary of the developed and developing world, which depicts the inherent digital divide which reflects in the adoption of emerging technologies.

Introduction

Big Data is the currency of the digital era, as oil was for the twentieth century. This article maps two major concepts; Big Data and Healthcare. Defining these two terms will help in informing the reader to comprehend the epistemic origins of the application, which in turn will help in unpacking the technological black box. Big Data is defined as;

“An evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information.”[1]

And, Healthcare is defined as;

“The act of taking preventative or necessary medical procedures to improve a person’s well-being. This may be done with surgery, the administering of medicine, or other alterations in a person’s lifestyle. These services are typically offered through a health care system made up of hospitals and physicians.”[2]

Healthcare is a public good, as it is a social justice issue inter twined with our fundamental human rights. Big Data is a byproduct of twin drivers, Information Communication Technology (ICT) revolution and globalization. Data in the healthcare space is generated furiously at every node of the value chain: Payer, Provider and Global Health. The Internet of Things paradigm generates multitude of data points per second as well with numerous tracking health indicators on their smart phones. Hence, from electronic Emergency Health Records to Insurance, Data in the Healthcare space is notionally ‘Big’.

The Opportunities

Medical Practitioners decide upon the course of treatment on the basis of data which is gathered from monitoring instruments to gauge various parameters and the outcomes of diagnostic tests. Doctors leverage data to extrapolate health trends to recommend treatments to their patients particularly while treating oncological ailment. Electronic Health Records are the basic unit of medical information which needs to be stored. There are hundreds of millions of such records in federal health system architectures in the developed world.

In contrast there is unstructured, fragmented data in the medical services market in the developing world due to the standard practice which is mostly handwritten prescriptions and hard copy filing of medical reports. These data points often get lost in the complex web of files and a comparative cannot be drawn at ease between two longitudinal data points which is the basis of medical decision making. Digitization brings benefits of on boarding this enormous data set on the mainstream decision making grid such as on the enterprise architecture or on the cloud.

This treasure trove of data is an incredible opportunity to mine consumer insights for healthcare service providers. According to a research study released by BIS Research in 20153, the growth rates are in double digits for the big data analytics market across its financial, clinical and operational segments. The clinical analytics pie of the healthcare analytics market is growing at 22.54% CAGR from 2015 to 2020 while the financial slice is more than half the overall market space as depicted in figure below.

Challenges

Big Data in Healthcare as argued in the previous section is a huge opportunity to improve decision making capabilities through enhanced data storage and enabling sophisticated enablers to make sense of the data. In this section, it is our endeavor to cut through the market rhetoric and ask some hard questions regarding the actual acceptance rate of Big Data as a critical lever to leverage value in the healthcare space.

The major structural barrier in the implementation of Big Data platforms in the developing world are the following three factors:

Capital: The initial deployment of capital in order to digitize paper records and have systems which can present the data in a format which is conversant with the end user; the medical practitioner is prohibitory for small players. The consumer in the global south is cost sensitive and the back end enablers to improving decision making is not a priority for the patient. If the infrastructure up gradation costs is passed on to the patient, then it is a discomforting factor.

Skills Barrier: Medical school education pedagogy has not evolved for decades in the global south and seems to have frozen in bureaucratic and intellectual stasis. The data analytics paradigm has not seeped in to the medical school curriculum in the developing world, and there is an evident skills chasm between the potential of the market as seen from the technology industry perspective and its adoption on the ground by the healthcare service actors.

Service Providers are ‘Tech Laggards’: Doctors consider additional data entry work into electronic database systems for medical records as a ‘burden’ on top of their existing medical duties.

The recent changes in the regulatory landscape in the Unites States compulsorily mandated electronic medical records as per The Health Information Technology for Economic and Clinical Health (HITECH) Act and Obamacare, the landmark legislation expanding medical coverage in the United States has made electronic medical records a part of the health services conversation. The quest for documentation from a cultural perspective is an effort to leave a paper trail in lieu of any legal action. The big data intervention here does not create greater efficiencies; rather it adds a new layer of bureaucracy, which has a negative externality.

Big Data in the Healthcare space is an opportunity from a techno-deterministic standpoint as it is a data rich environment, but in order for Big Data to become a tool for performance improvement; structural and cultural barriers need to be resolved.

The Market Scenario

There are numerous small, medium and large healthcare providers which are utilizing big data depending on the availability of skills and infrastructure. Pharmaceutical research organizations with well-equipped infrastructure and domain expertise are leveraging on big data in oncology and other clinical trials for the toxicity and safety analysis of the medicinal products [4,5]. Big data analytics is enabling value based future for the healthcare organizations which helps them prioritize healthcare outcomes for patients through precise data. Healthcare payers are implementing predictive analytics to detect fraudulent claims. Medium scale healthcare providers are not able to benefit from big data analytics despite of established infrastructure because of technical expertise scarcity and lack of cultural perspective as explained above. Small healthcare organizations still rely on decentralized data for decision making, making big data analytics implementation strenuous due to the paucity of spare capital.

The Big Data phenomenon is well underway in developed economies. Increasing healthcare expenses and focus on value based care are driving the demand of big data analytics in the healthcare domain. Healthcare providers are moving towards evidence based medicine for systematic clinical data analysis. Meanwhile from 2010, many new innovative health-care applications and smart devices have been developed with 40% of the lot rendering predictive capabilities.

In low and middle income countries, presence of advance IT systems is limited or non-existent which end up health data on paper records. The growing and ageing population is acting as a key driver for this market which demands value based metrics for enhanced healthcare outcomes. As the healthcare infrastructural spine gets digitized, the opportunities for big data in healthcare are manifold, but this will take time as the digital inequities are symmetrically mapped in the healthcare space as in the financial inclusion sphere.

Conclusions:

Healthcare operates at the intersection of socioeconomic variables, public service delivery, demography and technology. Big Data if the correct questions are asked, this paradigm has the potential to better create decision making tools for medical professionals across the board. The attempt of the authors in this paper has been to identify the gaps in the wide scale implementation of Big Data vis-à-vis the ticket size of the players in the healthcare arena which in turn is the intellectual anchor of the paper.

Refernces:
[1] http://searchcloudcomputing.techtarget.com/definition/big-data-Big-Data, 8th August 2016
[2] http://www.businessdictionary.com/definition/health-care.html, 8th August 2016
[3] BIS Research Industry Report on ‘Big Data in Healthcare’, August 2015
[4] Bertsimas D, O’Hair A, Reylia S, Silberholz J (2013) An Analytics Approach to Designing Clinical Trials for Cancer. MIT Working Paper.
[5] https://www.hpcwire.com/2016/05/18/machine-learning-fighting-cancer , 16th August 2016)

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