Data Warehouse / EDW

Starting and sustaining an enterprise data warehouse (EDW) for a sizeable healthcare organization might seem as challenging as, say, forming a new country. While it is an arduous undertaking, there are plenty who have gone before. In this article, one EDW operations manager shares five steps for success:

Start with a Leadership Commitment to Outcomes Improvement
Build the Right Team
Establish Effective Partnerships with IT
Develop Interest and Gain Buy-In
Pivot Toward Maintaining Success

Successfully implementing and sustaining EDW operations is about establishing and managing priorities and understanding the enterprise-wide implications.

What most often restricts IT innovation at a healthcare organization? It’s not limitations of the tools for innovation (the data infrastructure) or the workforce, but the organizational culture of the health system. A culture that’s too focused on past failed initiatives and their consequences won’t identify opportunities that lead to new ideas. They likely have the right parts for a great idea, but aren’t enabling those parts for innovation.
Organizations can build and environment that fosters innovation in healthcare IT by operating with three principles:

Give teams the freedom to fail.
Remember the adjacent possible.
Leverage organizational networks.

Healthcare data is positioned for momentous growth as it approaches the parameters of big data. While more data can translate into more informed medical decisions, our ability to leverage this mounting knowledge is only as strong as our data strategy. Hadoop offers the capacity and versatility to meet growing data demands and turn information into actionable insight.
 
Specific use cases where Hadoop adds value data strategy include:

Archiving
Streaming
Machine learning

In an industry known for its complex challenges that can take years to overcome, health systems can leverage healthcare data warehouses to generate seven quick wins—reporting and analytics efficiencies that empower healthcare organizations to thrive in a value-based world:

Provides significantly faster access to data.
Improves data-driven decision making.
Enables a data-driven culture.
Provides world class report automation.
Significantly improves data quality and accuracy.
Provides significantly faster product implementation.
Improves data categorization and organization.

Health systems that leverage healthcare data warehouses position themselves to do more than just survive the transition to value-based care; they empower themselves to achieve and sustain long-term outcomes improvement by enabling data-driven decision making based on high quality data.

Too much is at stake in value-based healthcare and the technology needed to provide it. When it comes to investing in the best healthcare analytics tools for delivering data-driven care management and outcomes improvement, executives should compare these seven points to determine whether an electronic health record or an enterprise data warehouse should be the foundation of their analytics platform:

Incorporating data from a wide range of sources
Ease of reporting
The data mart concept
Relevance of each to value-based care
Relevance of each to managing population health
Surfacing results of sophisticated analysis for physicians at the right time
Ability to combine best practices, data, and technology tools into a system of improvement

This executive report starts by examining the origin of EHRs and EDWs, then dives into the value derived from both in terms of their contributions to the major issues impacting healthcare delivery today.

As vice president of technology for a healthcare IT company, I’m often asked what should be considered when selecting a solution for healthcare analytics. Healthcare organizations have many choices when selecting a healthcare data warehouse and analytics platforms. I advise them to consider the following fundamental criteria: 1) time-to-value (measured in months, not years), 2) experience as a predictor of future success, and 3) extensibility to meet your needs.

Most health systems suffer from data clutter and efficient problems.  As a result, analysts spend most of their time searching for data, not performing high value work.  There are three steps that can help you address your data management issues: 1) find all your dispersed analysts in the organization, 2) assess your analytics risks and challenges, 3) champion the creation of an EDW as the foundation for clinical data management.

Which is a faster data-warehousing model, early-binding or late-binding? Skipping the suspense, the late-binding approach is the speedier option. Binding has to do with how data is modeled within the EDW. Early-binding requires business rules to be set up early in the analysis process. This means early-binding isn’t very flexible or adaptable to changes. On the other hand, the late-binding approach is all about speed and flexibility. Business rule decisions can happen at the last moment, right when the analysis takes place.

The data lake style of a data warehouse architecture is a flexible alternative to a traditional data warehouse. It allows for unstructured data. When a warehousing approach requires that the data be in a structured format, there are constraints on the analyses that can be performed because not all of the data can be structured early. The data lake concept is very similar to our Late-Binding approach in that data lakes are our source marts. We increase the efficiency and effectiveness of these through: 1. Metadata, 2. Source Mart Designer, and 3. Subject Area Mart Designer.

Most industries use enterprise data warehouses (EDWs) to create meaningful analytics on their operations and processes. Healthcare has long struggled with implementing and maintaining EDWs. One reason for this is that a lot of the data healthcare uses is unstructured, meaning there are few to no restrictions on it. And this unstructured data can exist in several systems within the organization. Additionally, health systems must pull data from many sources, such as EMRs, financial systems, and patient satisfaction data. The early-binding approach to data warehousing makes the binding decisions early in the process and, thus, lacks the agility healthcare needs to respond to ever-changing business rules and requirements. This approach can also take a long time to implement. Late-binding data warehousing has a much faster time-to-value and allows users to create analytics based on what-if scenarios. Plus, it can change to reflect the always-moving world of healthcare analytics needs.

It’s no secret that the failure rate of data warehouses across all industries is high – Gartner once estimated as many as 50 percent of data warehouse projects would have only limited acceptance or fail entirely. So what makes the difference between a healthcare data warehouse project that fails and one that succeeds? As a former co-founder of HDWA, Steve details six common reasons: 1) a solid business imperative is missing, 2) executive sponsorship and engagement is weak or non-existent., 3) frontline healthcare information users are not involved from start to finish, 4) boil-the-ocean syndrome takes over, 5) the ideal trumps reality, and 6) worrying about getting governance “perfect” immobilizes the project.

Healthcare organizations preparing for the value-based payment model shift have found their internal resources pushed to the limit. Often, in an attempt to address regulatory timetables, systems will use point solutions rather than move toward a long-term strategy of developing robust clinical analytics. If an organization is using their EHR for analytics, they will soon discover that these built-in analytics packages cannot help them identify opportunities for cost effectiveness and clinical best practices. Sophisticated data management and healthcare analytics solutions, however, can provide leaders with the integrated clinical, financial, and patient satisfaction data they need to transform their systems into data-driven enterprises.

A health system shares their story about using proven strategies to care for patients in an accountable care model by using data to drive those strategies. Gregory A. Spencer, MD, FACP, CMO, and CMIO at Crystal Run Healthcare also discusses why Crystal Run Healthcare moved towards analytics and data warehousing as well as the 6 requirements their health system had as they searched for a partner: 1) The solution needed to hit the ground running. 2) The solution needed to provide quick, actionable data. 3) There needed to be a library of analytical applications. 4) The healthcare data model needed to be able to evolve. 5) They needed to be taught how to fish for the data. 6) A long-term relationship with the vendor was important.

When creating a healthcare data warehouse, typically time-to-value will take one to two years. But using our data warehouse tools, we’ve reduced that time to months. Usually a lot of manual labor goes into extracting data from EHRs or other sources systems. Metadata mapping helps by indicated where data is located in each system. However, that mapping process is also typically time-consuming and onerous. Using Health Catalyst’s Source Mart Designer, the mapping is automated and ETL scripts become a cinch. Then we use our Atlas tool to make search for specific data easier and more intuitive.

Most healthcare data is now electronic and with that comes electronic healthcare databases. A healthcare database replaces the old paper documents, file folders, and filing cabinets. They take the form of EHRs, practice management systems, costing systems, patient satisfaction programs, and more. The benefits of databases are equal to the benefits that run on them. They can be stored externally and backed up in a secure place. However, healthcare databases have two big problems: There is an overwhelming amount of raw data with little ability to gain targeted, actionable knowledge from that data. And databases are siloed preventing the across-the-organization insight that is needed in today’s world of value-based care. The answer is a data warehouse, which sits on top of all the other databases providing sophisticated analysis.

Health systems generate and collect enormous amounts of healthcare data. Health systems also need to analyze the data for many different needs, such as quality improvement, operations, research, and financial analytics. The best solution an organization can use to aggregate all of this data is an enterprise data warehouse with the following five qualities: new source data feeds that can be developed quickly, a flexible architecture, data definitions that match their context, a single source of truth to support all use cases, and customized data access.

What are the hottest trends in healthcare data warehousing and analytics?  Read about them from Dale Sanders, one of the industry’s foremost experts. Dale has been one of the most influential leaders in healthcare analytics and data warehousing since his earliest days in the industry and is frequently introduced as the leading authority on healthcare data warehousing and business intelligence in the United States.

It can be confusing to know whether or not your health system needs to add a data warehouse unless you understand how it’s different from a clinical data repository. A clinical data repository consolidates data from various clinical sources, such as an EMR, to provide a clinical view of patients. A data warehouse, in comparison, provides a single source of truth for all types of data pulled in from the many source systems across the enterprise. The data warehouse also has these benefits: a faster time to value, flexible architecture to make easy adjustments, reduction in waste and inefficiencies, reduced errors, standardized reports, decreased wait times for reports, data governance and security.

Learn how your existing data warehouse could benefit from Health Catalyst’s advanced analytics applications. Options available include: Custom applications, Cloud data warehouse, Parallel platform, Feeder data warehouse, New data warehouse platform.

Healthcare data is not linear. It is a complex, diverse beast unlike the data of any other industry. There are five ways in particular that make healthcare data unique: 1. Much of the data is in multiple places. 2. The data is structured and unstructured. 3. It has inconsistent and variable definitions; evidence-based practice and new research is coming out every day. 4. The data is complex. 5. Changing regulatory requirements. The answer for this unpredictability and complexity is the agility of a Late-Binding™ Data Warehouse.

Hosting data in the cloud is a hot topic in the IT world, and now health systems are starting to embrace the technology, since it’s secure and HIPAA-compliant. While it’s not suitable for all organizations, many will benefit by using cloud hosting for their EDW. This article will explain the pros and help you understand if your organization is a good candidate.

Want to know the best healthcare data warehouse for your organization? You’ll need to start first by modeling the data, because the data model used to build your healthcare enterprise data warehouse (EDW) will have a significant effect on both the time-to-value and the adaptability of your system going forward. Each of the models I describe below bind data at different times in the design process, some earlier, some later. As you’ll see, we believe that binding data later is better. The three approaches are 1) the enterprise data model, 2) the independent data model, and 3) the Health Catalyst Late-Binding™ approach.

Recently Gartner came out with a seminal healthcare IT report with a striking recommendation. They acknowledged the all-consuming effort that has surrounded the wave of EMR adoption and rollouts. However, they suggested a new wave was coming. They recommended that once an EMR was rolled out, the top IT priority was to develop an enterprise-class data warehouse. They also listed 5 classic mistakes that could be avoided based on 25 years of data warehousing experience. Read further to get the highlights and a free copy of the actual report.

Business intelligence may hold tremendous promise but it  can’t answer healthcare’s challenges unless it’s built on the solid foundation of a clinical data warehouse. Learn the definition of business intelligence, why a clinical data warehouse is needed for any healthcare BI strategy, the various options in data warehousing, which one is most effective for hospitals and the industry and why.

The most common types of data architectures for EDWs are: the enterprise data model, the summary data mart (also called the star schema), and the adaptive or Late-Binding™ data model. These three models differ in when they bind (early to late). So what does it mean to bind data late? It doesn’t mean to bind everything late, but the model recognizes that rules and vocabularies that are volatile should be bound later. Once a clear analytics use case calls for the data, then bind it. The binding option for a late-binding architecture are: Bind in the source system. Bind during ETL to the source mart. Bind in the source mart. Bind during ETL to the customized data mart (SAM). Bind in the SAM. Bind in the visualization layer.