Have you ever ask yourself why data matters in healthcare? Most of us believe the Artificial Intelligence (AI) is enabling us to do tasks at the human-level performance. Furthermore, we all know AI craves data. So it is clear why it is essential. However, in particular domains such as healthcare, due to the privacy restrictions, it brings more questions: Can we even have access to the data? If yes, are we able to use it? Knowing that data matters is not enough to understand how to collect and use it. The data volume is growing at the speed of light in healthcare, and the majority of the time, we cannot touch it due to privacy. Now, let’s see why big data is a unique topic in the healthcare sector.

Introduction

Centers for Medicare & Medicaid Services announced that healthcare expenses estimated as 17.9% of America’s Gross Domestic Product (GDP) in 2016. With costs anticipated to touch approximately $6 trillion by 2026, this business needs adjusting. Big data is what makes that happen, primarily. Henceforth, it is necessary to investigate what is big data and why it matters in healthcare?

Big data in healthcare and its growth rate

As everyone’s chasing it, experts consider “big data” necessary on the menu of vital healthcare demands. Big data is data that is too big or complicated for conventional database systems. Stakeholders, usually characterized and recognize big data with three main factorsQuantityVelocity, and Diversity.

Electronic Medical Records (EMRs) have expanded the amount and diversity of available data drastically. The velocity of data collection and streaming into the healthcare society is much higher than the processing ability. Furthermore, the variety of provided data (kinds of data formats for processing such as structured and unstructured data) creates a lot of demand for expert human resources as data scientists. The big challenge is how to use this massive data to advance health domain and patient care in particular.

Researchers anticipate that the amount of significant data growth is faster in healthcare compared to other areas over the next seven years. This growth rate forecast is according to an International Data Corporation (IDC) report sponsored by Seagate Technology. Healthcare data will see a Compound Annual Growth Rate (CAGR) of 36 percent through 2025.

The anticipated growth rate of healthcare data by 2025. From 2019 to 2025, an approximately 5-fold increase is anticipated.
The anticipated growth rate of healthcare data by 2025. From 2019 to 2025, an approximately 5-fold increase is anticipated.

Motivations behind embracing big data in healthcare

Three significant transformations in the healthcare business create the motivation behind the enactment of big data utilization: the massive amount of data availablegovernment statutes and regulations, and an emphasis on personalized care.

  • With the advent of Electronic Health Records (EHRs), the volume of useful health data increased drastically. This phenomenon created a need to adopt mechanisms to handle and process massive data. Health systems should utilize big data methods and technologies to manage, investigate, and use this information efficiently.
  • In the USA, as we mentioned before, healthcare costs account for about 18 percent of GDP, equaling roughly about $600 billion. Such high costs put a lot of financial burden on the patients and resulted in government moves pointed to reducing this difficulty, most prominently the Affordable Care Act (ACA). As a consequence, more healthcare stakeholders are gathering and investigating big data to enhance patient care quality and efficiency.
  • In healthcare, just like any other sector, customers desire excellent, personalized care, define new levels of expectations. Health organizations and stakeholders are turning to rely on big data to meet this expectation by providing data-driven, accurate, reliable care.

AI and the need for big data

Healthcare organizations are trying so hard to comprehend and utilize AI as a tool to resolve their business needs. Despite the eye-catching success of AI in other domains such as Computer Vision and Speech Technology, the experts failed to demonstrate a similar level of provision in the healthcare domain. It is mostly due to the privacy concerns in healthcare which is not an issue in the majority of other domains. AI itself needs a lot of data to operate and succeed. Without data, AI and Machine Learning fail to deliver what we desire and expect from an intelligent system. You can refer to the article “How to Frame, Organize, and Manage a Competitive Machine Learning Project?” to gain further knowledge about what matters in an AI project.

Summary

In this article, we investigated the definition of big data, its importance in the healthcare industry, and the grounds for its usage. Although many stakeholders are desperately looking to incorporate big data using AI in their business, the privacy restrictions in healthcare remains a hurdle for this aim.

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Categories: Uncategorized

Sina Torfi

Currently, as a CS Ph.D. student, I'm a research assistant at Virginia Tech. My research is mainly about Machine Learning & Deep Learning and their applications in Computer Vision and NLP. I'm interested in developing software packages and open-source projects.

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