Big Data in Digital Health: Concept, Applications, and Market Outlook

VCBeat has previously published articles introducing Big Data from a theoretical perspective (Click here for details), this article attempts to introduce this “commonplace” concept from the perspectives of its definition, applications, and market.

What Is Big Data?

Big Data, commonly referred to as “big data,” is no longer a novel concept; it is a master key applicable across various industries. However, the term has now reached the point of being “overused,” and “big data analytics” seems to be transforming from a sophisticated buzzword into a lackluster “gimmick.”

In 2001, Doug Laney, then a market analyst at the U.S.-based consulting firm Gartner, articulated the now-mainstream definition of Big Data using the three Vs.The 3 Vs refer to volume, velocity, and variety.。

Volume, as the name suggests, refers to the volume of data. Many factors contribute to the increase in data volume. For instance, there is a vast amount of unstructured data from social media, and an ever-growing quantity of physical health data is being collected through sensors and wearable devices. In the past, storing large volumes of data was a challenge; however, this issue has been largely resolved with the decline in data storage costs. Nevertheless, new challenges have emerged, such as how to locate relevant data amidst massive datasets and how to analyze this relevant data to derive valuable insights.

Velocity, namely the speed of data updates. Currently, various types of data are flowing at an unprecedented pace, requiring immediate response and processing. Rapidly handling changes and updates in data is a challenge faced by most institutions.

Variety, data diversity. Currently, data types are highly varied. Traditional databases contain large volumes of structured data, while unstructured data such as text, emails, audio, and video are becoming increasingly prevalent. How to integrate and process these diverse data types is a key challenge that many organizations now need to address.

Specifically in the field of internet healthcare, Big Data refers to the use of relevant technologies to capture and analyze large volumes of complex data, with the aim of improving patient treatment outcomes and optimizing healthcare service processes.

In fact, although the term “Big Data” appears to refer solely to the volume of data, and Doug Laney’s definition also addresses aspects such as data volume and variety, its scope is not limited to these dimensions. Big Data also encompasses the technologies and tools required for an organization to store and process massive amounts of data.

Especially in the healthcare industry, vast amounts of data are generated across clinical, financial, managerial, and genomic domains, creating a critical need for “Big Data” technologies to process this information.

Big Data in the Internet healthcare sector primarily emphasizes six major categories of information:

1. Data from networks and social media. For example, user interaction data from Facebook, Twitter, LinkedIn, blogs, health and medical forums, and smartphone apps.

2. Data from machinery and equipment, such as data from sensors, measuring instruments, and other devices.

3. Transaction-related data. For example, data and information related to medical insurance claims and payments in various semi-structured and unstructured tables.

4. Biometric identification data, such as fingerprints, genetic information, handwriting, retinal scans, X-rays, and other medical images.

5. Human-recorded data, such as EMRs (For a detailed introduction to EMR, click here), physicians' notes, emails, paper documents, etc.

6. Drug development data related to the mechanism of action, as well as the intended effects and adverse reactions in humans.



How Big Data Is Used in Internet Healthcare

The role of Big Data in the healthcare sector is continuously expanding. According to VCBeat, the basic application process of Big Data in healthcare is as follows:

• Collect and aggregate massive amounts of patient information from various sources;




• Analyze the collected information from various goal-oriented perspectives, such as optimizing patient diagnosis and treatment, and improving the efficiency of the healthcare system;




• Apply the results of data analysis to improve patient treatment and enhance the return on investment in healthcare systems;

The application of big data in the healthcare sector is gradually being rolled out, with its benefits becoming increasingly prominent. Its current primary significance lies in:

• By analyzing clinical data, provide more proactive treatment and care for patients to improve disease treatment outcomes;




• Enhance support for clinical decision-making through analysis of the latest databases;




• Improving the design of clinical trials through the use of statistical tools and algorithms;




• Supporting personalized medicine through the analysis of large datasets;




• Optimize business decision support to ensure appropriate allocation of medical resources;

The Current Market and Trends of Big Data

According to a market research report by R&R, the growth rate of Big Data in the healthcare sector alone reached 23.7% from 2012 to 2017, with the total market size reaching $10.8 billion.

McKinsey also predicts that if the potential of big data and its role in the healthcare value chain are fully leveraged, healthcare spending in the United States alone could be reduced by $300 billion to $450 billion.

Drivers for the Application of Big Data in Healthcare:

• The need to improve clinical treatment outcomes;




• The Need to Improve the Efficiency of Medical Data Management;




• Rapid growth in EHR adoption coverage;




• Focus on Value-Based Medicine;




• The Need for Personalized Medicine Based on Big Data Analytics;




• The Need to Improve Medical Decision Support;




• The need to reduce drug R&D costs;




• The need to reduce clinical trial costs;

Barriers to the Application of Big Data in Healthcare:

• Shortage of Information Technology Talent in the Healthcare Sector;




• Lack of Data Transparency in the Medical Field;




• Capital constraints;




• Concerns about patient privacy;




• Traditional data analysis is low-cost;




• Lack of interoperability between healthcare systems;

The storm of Big Data has long swept across all sectors globally except healthcare. As its rapid expansion continues within the medical field, a growing number of institutions will recognize the critical role Big Data plays in serving patients, healthcare professionals, and even their own operations.

 

VCBeatTrending English Terms in Internet HealthcareChief Consultant for Analysis: Zhao XinyuanCurrently serves as the CEO of Beijing Yingtai Kelong Technology Co., Ltd. and concurrently holds a position as a member of the Technical Steering Committee of HL7 China.

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