Dispersed information is a top challenge that today’s healthcare organizations are desperate to address. The current disorganization rampant among top healthcare organizations is alarming, with the slow gathering and compilation of data increasing the potential for error. By adopting common IT data analysis techniques such as data warehousing, it may be possible for healthcare organizations to reduce waste and improve health data management.
What Is An Enterprise Data Warehouse?
Common in the world of IT, data warehousing has the potential to completely revitalize the messy and inefficient clinical data management setup plaguing many of today’s top healthcare organizations. At its base level, an enterprise data warehouse is a centralized data collection used to create analytical reports. This data is often gathered from disparate sources, and thus, can be very difficult to access if not correctly organized.
Data Warehouses Versus Clinical Data Repositories
Data warehouses are frequently confused with clinical data repositories, as both store vast collections of healthcare information. A few key differences make data warehouses more efficient and reliable than their repository counterparts.
Clinical repositories consolidate such information as patient demographics, diagnoses, admissions and transfers. This collection of information is valuable, but its lack of flexible analytics makes it all but impossible for healthcare analysts to track patients as they traverse the entire healthcare continuum. Conversely, data warehouses can be used to integrate information from an array of sources, all the while providing an accurate overview of patient costs and satisfaction levels. This helps to improve health data management for both patient and provider.
Why The Lack Of Data Warehousing?
At one time, data warehousing was considered a cutting-edge development in healthcare analytics. Since then, a perceived lack of return on investment has convinced many industry leaders to abandon all warehousing efforts. Instead, many healthcare organizations have adopted the aforementioned clinical data repositories. While initially easier to manage, clinical data repositories can lead to significant data management inefficiencies, as well as a lack of accurate healthcare reporting.
Once a healthcare organization has decided to improve health data management via data warehousing, it is essential to choose an approach that meets the unique needs of that organization. Common warehouse models include the following:
- Enterprise Model –
The enterprise model is a complex, but comprehensive approach to warehousing that involves the advance construction of a large centralized data warehouse. This approach was very common in the early days of warehousing, but while healthcare organizations initially exhibited enthusiasm, many struggled to follow through on overly ambitious plans. One of the biggest challenges attributed to the enterprise model is the need to make major decisions in advance without the ability to adjust for sudden, unforeseen changes. In an increasingly unstable healthcare system, this lack of adaptability can be catastrophic.
- Independent Data Marts –
A common alternative to the enterprise model of data warehousing, independent data marts allow healthcare organizations to start small, building targeted data marts for each department and later combining them to create larger analytic infrastructure repositories. According to data mart expert Ralph Kimball, it may be helpful for healthcare organizations to view data warehouses as unions of data marts. Although easier to implement than the standard enterprise model, the independent data mart approach is often not as comprehensive or organized as its alternative. However, it may be an excellent solution for smaller healthcare organizations looking to avoid the expense and time-consuming nature of large enterprise systems.
- Hub and Spoke Systems –
The hub and spoke model of data warehousing is increasingly becoming the go-to approach for mid to large healthcare organizations. This warehousing setup combines the comprehensive nature of the enterprise model with the accessibility of data marts. The hub and spoke approach involves an extract transform and load (ETL) process that draws data from disparate source systems — including data marts — and integrates the information in an easy-to-access warehouse, known as the hub. If needed, this hub-based information can be transitioned to the spokes for greater precision or enhanced control among specific departments.
No one approach to data warehousing is ideal for all healthcare organizations, but in general, the concept of data warehousing serves as a welcome step up from the limited scope of data repositories. A data warehouse can vastly improve analytic organization and flexibility, and, ultimately, improve health data management.