Healthcare Data Integration

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Big Data and machine learning (ML) have sparked a radical transformation across every sector, creating opportunities to optimize operations and streamline decision-making. As the biggest producer of data worldwide, the healthcare industry is especially well-positioned to extract actionable business and medical intelligence from Big Data. The process of transforming enormous amounts of data into useful knowledge begins with healthcare data integration.

In this article, we discuss data integration in the healthcare industry—what it is, best practices, challenges, and how it can benefit healthcare organizations.

What Is Data Integration?

Broadly speaking, data integration is the process of combining multiple data sources into a single, unified set. For integrated data to be usable, it must first be cleansed and transformed into a uniform format. Once it is uniform, it can be analyzed to provide valuable business intelligence (BI) and business analytics (BA).

Big Data and the Healthcare Industry

Guide to Healthcare Data Integration

Source: RBC

 

Between traditional hospital and clinic visits, wearables, and telehealth services, the rate of data capture in healthcare is growing rapidly. Today, the healthcare industry generates 30% of the world’s data each year, the most of any industry. By 2025, the compound annual growth rate of data production is projected to reach 36%—faster than any other industry.

With all this data, you might imagine that the healthcare industry is the most fervent champion of Big Data technologies. In fact, it’s quite the opposite: Only 56% of the industry is leveraging the full power of Big Data. Meanwhile, it’s estimated that the other 44% are experiencing some $342 billion in lost revenue as a result of their reluctance.

So, what gives? Below, we discuss some of the challenges posed by data integration in the healthcare environment.

 

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Healthcare Data Integration Challenges

Why have 44% of healthcare organizations avoided investing in data integration? Let’s look at the five most common data integration challenges.

1. Lack of Standardized Data Formats

The absence of standardized data formats is a major challenge when integrating healthcare data. Over the years, medical establishments have collected data in a variety of formats—many of which are incompatible with other formats. 

There is no agreed-upon standard for healthcare data formatting at this time. This not only complicates data collection and storage but also data integration and analysis. Without uniformity, much of the valuable data collected in the healthcare industry is inaccessible to researchers.

2. No Standardized Data Handling Processes 

The data collection and data analysis processes are usually performed by separate entities, leaving plenty of room for information to get lost in translation. Additionally, data ownership is prone to ambiguity. In the coming years, it will be essential to standardize data handling and ownership through stricter protocols.

Healthcare Data Integration

3. Ambiguity of Data Privacy and Confidentiality

Many healthcare organizations have a murky understanding of privacy and confidentiality laws regarding data sharing and health information. On the one hand, organizations must protect data from unauthorized access; on the other, sharing a data source under specific conditions is imperative for medical insight. 

4. Data Inconsistency Across Applications

The healthcare system stretches far and wide, creating a complex network of dissimilar nodes. As a result, a multitude of incompatible applications are used from one provider to the next.

The health system as a whole will require new compliance standards to ensure uniformity across applications. As it is, many separate applications used by healthcare organizations contain the same data but remain inaccessible for cross-platform use. Even a slight discrepancy in comparable data sets can lead to a reduction in data-driven insight.

5. Growing Pool of Data Sources

Between new medical devices and evolving wearables, the number of data sources is rapidly expanding. Many of these new technologies are busy collecting personal health data on a 24/7 basis.

This has generated an enormous amount of potentially useful data without standardized processes or guiding regulations to leverage it to its full potential. Data from wearables can be very difficult to integrate, partially due to its format and partially due to privacy concerns. 

With the challenges of medical data integration now laid bare, let’s turn our attention toward best practices to address and overcome these challenges.

Medical Data Integration Best Practices

Despite the challenges holding many organizations back from leveraging useful data, these five medical data integration best practices can help you make the most of your data. 

1. Leverage Data Lakes and Data Warehouses

Data lakes and data warehouses are both types of storage repositories. On the surface, they’re similar. Both are used to store and centralize data and both allow users to run queries, compile reports, perform analysis, and retrieve data. Here’s how they differ: 

Data warehouses cleanse and store structured data in relational databases. This makes them particularly well-suited to generating business intelligence (BI), providing a clear and descriptive picture of what is and what has been. Microsoft Azure and Amazon Redshift are two examples of popular data warehousing products. 

Data lakes not only store structured data but unstructured data and semi-structured data, too. Contrary to warehouses, lakes are best used for Business Analytics (BA), which can isolate the why behind past and present trends.

2. Know Your Data

Before doubling down on data hoarding, healthcare organizations should first consider the types of data worth collecting. This includes where it originates from, how it will be used, by which programs, and how often it must be updated or maintained. 

Answering these questions will sharpen the boundaries of your data integration strategy, increasing the signal-to-noise ratio.

Medical Data Integration

3. Plan for the Future of Tech

One of the major hurdles in data integration is keeping pace with the latest system updates, newest technologies, and revised compliance regulations. In order to maintain the highest standard of data integration, it’s important to use flexible and scalable data integration tools

Adaptability is one of the keys to successfully navigating the ever-changing digital landscape.

4. Regularly Assess Your Systems

While the best data integration strategies are almost entirely automated, it’s crucial to perform certain tasks manually. In particular, organizations must stay up-to-date with the latest compliance regulations and evolving best practices. As they say in medicine: An ounce of prevention is worth a pound of cure.

5. Invest in Cloud Computing

The sheer volume and velocity of Big Data require immense processing power to handle. By moving your storage away from on-premises data centers to a cloud-based model, you can simultaneously create scalability while harnessing more processing power. 

This accelerates ETL (extract, transform, and load) time and reduces storage concerns—all for a lower cost than running on-premises data centers.

Another major advantage of cloud computing is that it allows systems to talk to each other in real-time, providing researchers with consolidated data for optimized decision-making. While it’s unlikely that every organization will adopt the same procedures, cloud storage is sure to improve overall data quality.

Monitor and Manage Your Data Better:

Trust Your Medical Data Integration With True North

Medical data integration is fundamental in improving health care delivery. While it can be an expensive undertaking in the short term, the long-term savings and rewards are priceless. When done correctly, data integration offers many benefits, including:

  • Improved unification of systems;
  • Consolidated population health data;
  • Improved collaboration between employees and across departments;
  • Boost in efficiency and productivity;
  • Reduction in the number of integration errors;
  • Reduction in the time spent correcting errors;
  • Increase in the potential for actionable insight;
  • Simplification of business intelligence (BI) and business analytics (BA);
  • Long-term improvements in patient care and positive health outcomes.

Nevertheless, healthcare data integration is not a one-size-fits-all solution. At True North, we can help your organization implement the right strategy to meet your goals. Contact us today to learn more.

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