Smart Health, Smart Data: A Comprehensive Journey into Data Warehousing in Healthcare

Let’s start by looking at some interesting statistics about data:

Each day, a staggering 328.77 million terabytes of data are generated globally, encompassing new, captured, copied, or consumed information. This daily influx translates to approximately 0.33 zettabytes, demonstrating the monumental scale of modern data creation.

Inadequate data quality costs companies $12.9 million annually on average.

Despite the massive number of data generated each day, the number highlights that it can be hard to manage poor data quality, and it can be even harder to organize and utilize the existing good data efficiently.

This is where data warehouses come in. A specialized data management system aims to facilitate business intelligence operations, particularly analytics tasks.

Today, we are going to look at the role data warehousing plays in the healthcare industry. With data playing a key role in improving the overall healthcare system, it certainly helps to understand how a healthcare data warehouse works and benefits clinics.

What Is Data Warehousing in Healthcare?

Definition

A data warehouse is a specialized system for managing data used in business analysis and decision-making. It stores large amounts of historical data from different sources like application logs and transactions. By bringing together various data sources, it allows organizations to analyze data deeply, gain valuable insights, and make better decisions. It serves as a reliable reference for data scientists and analysts, offering a comprehensive view of data over time.

Healthcare data warehousing stores healthcare data that comes from various sources - for example, electronic health records (EHRs), ERPs, patient surveys and patient data, CRMs, financial records, health tracking apps, etc.

Despite storing such a large amount of data, a data warehouse is only one component among the large data management platforms that are made up of multiple layers that eventually use data analytics to transform raw data into usable data.

Healthcare Data Management

The nature of healthcare requires the storing, organization, and management of data to be accurate, reliable, and accessible to those who need it. Moreover, the management of such data requires one to strictly comply with guidelines and regulations like the USA’s HIPAA and Europe’s GDPR.

The Architecture of a Data Warehouse

Data warehouse architecture refers to how a system is set up and organized. It is a centralized repository for structured and semi-structured data sourced from diverse origins.

There are two common types of data warehouse architecture: Individual data warehouse and enterprise data warehouse.

Individual Data Warehouse

More focused on an individual area or function in the healthcare organization, an individual data warehouse (sometimes referred to as an individual data mart) is a more “condensed” version of the data warehouse dedicated to a specific department or unit. Due to its smaller scale, it also receives data from fewer sources compared to its enterprise counterpart. This type of data warehouse is suitable for small to mid-sized healthcare organizations.

Even though this data model provides a more focused view of data that helps the team implement and measure metrics faster, it can cause data redundancy in the long run.

Enterprise Data Warehouse

Another type of data warehouse in healthcare is the enterprise data warehouse. While the individual model takes a bottom-up approach, the enterprise data warehouse takes the top-down approach. This model is more suitable for large health organizations due to the sheer amount of data and its multiple layers of data analytics. The enterprise model is made up of four main layers.

Data Source Layer

As the name suggests, this layer includes internal and external data from multiple sources like EHR, ERP, pharmacy management systems, claim management systems, etc.

Staging Area

The second layer of healthcare data warehouses is data storage. It acts as an intermediate temporary storage for the data stream from the first layer. Here, two methods are used to pick out high-quality data without duplicates, inconsistencies, or inaccuracies. The first method is called ETL, which is short for extract, transform, and load, where data is loaded in its raw form and then modified on demand. The second method is ETL or extract, transform, and load, where data is formatted first and then loaded into the warehouse.

Data Storage Layer

This layer serves as the centralized and unified repository for data storage. Data is loaded into this repository through ETL/ELT methods and is awaiting analysis and reporting. Additionally, it may contain individual data marts, which are subsets of data tailored for specific business functions such as accounting or HR, as well as for different departments like radiology and pediatrics.

Analytics and BI (Business Intelligence)

The 4th layer is where healthcare providers can implement analytical toolkits like business analytics, data mining, statistical analytics, and data visualization or use third-party apps for the same purposes. Additionally, AI (artificial intelligence) and machine learning (ML) are also great tools for making informed decisions.

Data Warehouse for Healthcare: Valuable Integrations

There are four valuable integrations organizations should consider maximizing the use of a data warehouse

Data Lake

Before the warehouse questions the semi-structured data, the data lake is a cost-effective location for this type of data. Moreover, the data stored can be used to train ML models.

ML Software

ML is a great tool for predictive analytics. With the structured, stored data from the clinical data warehouse, ML software produces insights that support diagnoses and personalized healthcare.

Self-service BI Software

BI software is another great integration that supports decision-making. Still using organized and modified data, with techniques of descriptive analytics and data visualization, it automates reporting and creates dynamic dashboards.

Benefits of Healthcare Data Warehouse

Informed Decision Making

A massive volume of data doesn’t mean better decision-making. With a healthcare data warehouse, healthcare providers have a unified source of structured data. Moreover, the data is analyzed through a variety of analytic techniques, allowing professionals to make data-driven decisions instead of doing pure guesswork.

Better Patient Retention

By leveraging comprehensive data insights and adopting a patient-centric approach, healthcare organizations elevate the overall patient experience, improving patient retention rates. Warehouses also give patients the choice to actively participate in their treatment by giving them access to medical data.

Data Security

Ensuring the security of medical data is of utmost importance for every healthcare organization. Data warehouses can help establish role-based access controls, which means that only authorized staff can access sensitive information. In addition, other security measures can be implemented, such as limiting access to data through reports or implementing encryption protocols to prevent unauthorized access.

Streamlines Claims and Payments

It is no secret that processing insurance claims has long been one of the biggest headaches for hospitals. Not only is it time-consuming to retrieve data, but healthcare professionals also need to keep an eye out for fraudulent payouts. With the assistance of data warehousing, this process is streamlined, making the clinic’s and patient’s lives much easier.

Improves Data Integrity

Data warehousing tools improve data quality through the multiple layers that turn raw data into a standardized format and remove inconsistencies and errors while also establishing metrics for data quality. All of this is done before the data reaches its target location.

Challenges and How a Partner Can Help You Navigate Them

Navigating the world of technology might be tricky, and healthcare data warehouses are no exception. From data integration complexities to system compatibility issues, challenges abound. No need to worry, as outsourcing partners are here to save the day. With ready-to-go resources and expert teams, they’re equipped to tackle your problems head-on. Whether you’re facing unique hurdles or seeking to enhance existing features, outsourcing is the solution.

At Orient Software, we’re committed to exceeding expectations and delivering top-notch results. Still on the fence? No problem. Get a business quote within three days, and let us help you make the right choice.