Harnessing the Power of Big Data in Healthcare: A Five-Pillar Approach

In the past two decades, the healthcare industry has witnessed an unprecedented data explosion. The advent of big data has opened up a world of possibilities, offering a treasure trove of insights that can revolutionize patient care. However, the sheer volume of healthcare data can be overwhelming, and without the right strategies, this wealth of information remains untapped. So, the question arises: how can we harness the power of big data in healthcare effectively? This article delves into the heart of this question, outlining a five-pillar approach to transform big data from an overwhelming challenge into a powerful tool for value-based care. 

Building a Robust Data Architecture: The Backbone of Big Data in Healthcare

In any healthcare organization, data flows in all directions, connecting various partners and stakeholders. This complex network may seem chaotic, but to ensure data reaches the right parties at the right time, a well-structured infrastructure is crucial.

To pave the way forward, we must first comprehend our current position. Organizations need to evaluate their existing systems, checking for compatibility and overlap. This assessment provides an opportunity to spot inefficiencies, make necessary system modifications, and ensure the inbound data is comprehensive and robust.

With the appropriate tools at hand, we can eliminate data silos and enhance accessibility. The ideal architecture takes the form of a data lake, a centralized repository that pulls in real-time analytics and data. To prevent this data lake from becoming an unmanageable black hole, it’s vital to organize it before filling. By establishing data categories, tags, and characteristics upfront, we can streamline file organization and make the unstructured data within the lake easily searchable.

Rapid and Scalable Data Ingestion: The Key to Real-Time Healthcare Insights

Data ingestion, the process of absorbing data from various internal and external sources, is a critical aspect of big data in healthcare. Unlike other industries where data can be processed in batches at set intervals, the pressing nature of healthcare decisions demands real-time data collection for immediate insights.

However, the speed of data ingestion presents a significant challenge. The process of data cleansing is complex and demands substantial time and resources. Before data can be processed and integrated into the data lake, it must be converted into a common format from its disparate origins. As it flows, it must pass through rigorous security checks and be capable of managing an ever-increasing volume of data from countless sources.

Data Governance: Ensuring Trust and Appropriate Use of Healthcare Data

In simple terms, data governance is the stewardship of data within an organization. When underpinned by a robust data architecture, governance ensures a chain of trust, giving both data producers and consumers confidence in the appropriate handling and usage of data. The American Health Information Management Association’s (AHIMA) defines data governance as an organization-wide framework for managing health information throughout its lifecycle.

Implementing a data management structure assigns responsibility and fosters accountability. When executed effectively, it provides providers and administrators with the right information, at the right time, in the right format.

While establishing governance may seem like the most straightforward step in making big data in healthcare usable, its importance cannot be overstated. You can read about Emids’ approach to data governance, capabilities and expertise here

Leveraging Technology for Data Integration: The Path to Usable Healthcare Data

While we’ve discussed the necessity of assessing data for quality, completeness, and compatibility, the question remains: how do we achieve this? The solution lies in a blend of cutting-edge technology and skilled manpower.

Data integration is a fundamental step in making big data in healthcare actionable. Before data can be integrated, it must first be standardized. Over the past two decades, Emids has refined its unique approach to healthcare data standardization, assisting numerous organizations in enhancing their processes.

By leveraging the right tools from a seasoned partner and the supervision of skilled data professionals, you can ensure that your big data efforts drive the desired outcomes.

Bridging the Skills Gap: The Demand for Data Professionals in Healthcare

Imagine a rapidly accelerating conveyor belt, relentlessly delivering products. If we consider healthcare data as the product, it becomes imperative to master efficient and effective collection, management, and storage of data, while also staffing the production line appropriately.

In 2012, the Harvard Business Review dubbed the data scientist as the “sexiest job title of the 21st century.” At the time, the profession was relatively unknown. Fast forward to today, and the demand for data scientists and other mid to senior-level analytical roles has skyrocketed, a trend expected to continue. The World Economic Forum’s 2018 Future of Jobs Report revealed that 85% of the world’s largest companies plan to expand their use of big data analytics by 2022.

However, the supply of professionals equipped to monitor, analyze, and validate big data in healthcare is lagging behind the demand. Rapid technological evolution and data availability have outpaced education, leading to a significant supply-demand gap.

Given the scarcity of professionals suited for data roles and their crucial role in data governance and organizational success, their importance should not be underestimated by hiring managers and administrators.

The Bigger Picture

Harnessing data’s power to benefit your value-based care efforts and ultimately help achieve the Triple Aim may seem daunting, but the challenge is not insurmountable. Following and embracing these five easy-to-digest components will provide a clear path to success.

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