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.
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Comparing the Three Major Approaches to Healthcare Data Warehousing: A Deep Dive Review (White Paper)
The task to improve healthcare presents a significant challenge to providers, health systems, and payers. But according to the Institute for Healthcare Improvement, if health systems focus on achieving the objectives of the Triple Aim, they will be able to meet the ongoing government mandates to improve care. A key component for meeting the Triple Aim will require the ability to overcome the current data warehouse challenges the healthcare industry faces. Because of constantly changing business rules and definitions, health systems need to choose a data warehouse that’s able to bind volatile and nonvolatile data at different stages rather than the early binding approach that’s inherent with traditional data warehouses. The best type of healthcare data warehouse should offer a late-binding approach, which will provide the following critical characteristics: data modeling flexibility, data flexibility, a record of changes saved, an iterative approach, and granular security.
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.
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.
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.
Describing healthcare data models can quickly get very technical. We prefer to use an analogy: making and sticking to a grocery list. With this analogy, audiences can quickly see the differences between dimensional, enterprise and adaptive data models and determine which one will work best for their organization’s needs.
The star schema approach to data warehouses is simple and straightforward. Its design is considered best practice for a wide variety of industries. But it lacks the flexibility and adaptability necessary for the healthcare industry. A Late-Binding™ approach, on the other hand, is designed specifically for the analytics needs of healthcare providers. It offers the flexibility to mine the vast number of variables and relationships in healthcare data effectively and leave room for the inevitable future changes.
Recently, I invited a group of my colleagues to share some examples of unexpected benefits they had witnessed at healthcare organizations that feature powerful, thriving EDW initiatives. The number of responses I received was overwhelming; more than I could possibly hope to include in one blog post. With a goal of hopefully sharing all of them within a continuing series, here are some excerpts, reprinted with permission and in the words of the “EDW Elders” within our company. These include 1) negotiating with insurance companies, 2) Stage 1 Meaningful Use self-certification, 3) data quality issues, 4) financial data comparisons, 5) EMR user log data, and 6) employee satisfaction data.
CEOs and CIOs of health systems often ask me how much a healthcare enterprise data warehouse will cost them. As I delve into the topic with them, it becomes clear that what they are really concerned about is their return on investment. These executives are aware that many data warehousing projects require significant upfront investment but may not deliver a return for years, if ever. That feels very high risk to them—and to me as well. I’d like to share what I believe is the lowest-risk, most economical plan for investing in a healthcare enterprise data warehouse.
You have options when it comes to data warehouses – but which one is right for your healthcare organization? Discover the difference of the Late-Binding (TM) data warehouse architecture. And see why this unique system offers quick time-to-value and the agility necessary to meet the changing demands of the healthcare industry.
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.
Chances are, if you are reading this blog, you have heard some flavor of the “build vs. buy” question in the context of data warehousing. For example, here are two conflicting ways that I’ve personally heard this question posed: “Do we need to buy [a data warehouse], or can we build it?” “Are there any vendors we can buy this from, or will we have to build this?” As you can imagine, both approaches resonate differently with different people, cultures, and strategies, and the same basic questions sound very different depending on who is asking it.
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The Homegrown Versus Commercial Digital Health Platform: Scalability and Other Reasons to Go with a Commercial Solution
Public cloud offerings are making homegrown digital platforms look easier and more affordable to health system CTOs and CIOs. Initial architecture and cost, however, may be the only real benefit of a do-it-yourself approach. These homegrown systems can’t scale at the level of commercial vendor systems when it comes to long-term performance and expense, leaving organizations with a potentially costly and undereffective platform for years to come. Over his 25 years as a health system CIO, Dale Sanders, President of Technology for Health Catalyst, has observed both the tremendous value of healthcare-specific vendor platforms, as well as the shortcomings of homegrown solutions. He shares his insights in a question-and-answer session that addresses pressing issues in today’s digital healthcare market.
Most health systems suffer from data clutter and efficiency 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.
Smartphone applications, home monitoring equipment, genomic sequencing, and social determinants of health are adding significantly to the scope of healthcare data, creating new challenges for health systems in data management and storage. Traditional on-premises data warehouses, however, don’t have the capacity or capabilities to support this new era of bigger healthcare data. Organizations must add more secure, scalable, elastic, and analytically agile cloud-based, open-platform data solutions that leverage analytics as a service (AaaS). Moving toward cloud hosting will help health systems avoid the five common challenges of on-premises data warehouses:
- Predicting future demand is difficult.
- Infrastructure scaling is lumpy and inelastic.
- Security risk mitigation is a major investment.
- Data architectures limit flexibility and are resource intensive.
- Analytics expertise is misallocated.
Looking for a way to share his extensive experience with data warehousing in healthcare, in 2002 Dale Sanders wrote what many consider to be the “EDW Bible.” It’s a document with guidance that, if followed, will drive value and utilization from a data warehouse. We’ve made that report available now.
What are the differences between a database and a data warehouse? A database is any collection of data organized for storage, accessibility, and retrieval. A data warehouse is a type of database the integrates copies of transaction data from disparate source systems and provisions them for analytical use. The important distinction is that data warehouses are designed to handle analytics required for improving quality and costs in the new healthcare environment. A transactional database, like an EHR, doesn’t lend itself to analytics.
Episode Analytics Now Mission Critical as Outcomes Meet Incomes: Partners HealthCare Paves Volume-To-Value Path With Late-Binding Data Warehouse
In this reprint from Microsoft, Dennis Schmuland, MD, FAAFP (Chief Health Strategy Officer, Microsoft US Health & Life Sciences), sits down with Sree Chaguturu, MD (Vice President and Chief Population Health Officer, Partners HealthCare) to learn how Partners HealthCare has prepared for the tipping point of value-based care.
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.
I am one of the brave souls who takes the time to read the report issued each spring by the Medicare Payment Advisory Commission (Medpac). The report shows the numbers of Medicare beneficiaries and claims are growing; healthcare organizations are increasingly losing money on Medicare; payment increases certainly will not keep pace with declining margins; and Medicare policies will continue to incentivize quality and push providers to assume more risk. But the report also reveals that some healthcare organizations—referred to as “relatively efficient”—are making money from Medicare with an average 2 percent margin. How do you become one of these organizations? And how do you target and counter Medicare trends that impact your business?
The Data Operating System (DOS™) is a vast data and analytics ecosystem whose laser focus is to rapidly and efficiently improve outcomes across every healthcare domain. DOS is a cornerstone in the foundation for building the future of healthcare analytics. This white paper from Imran Qureshi details the seven capabilities of DOS that combine to unlock data for healthcare improvement:
Using a supermarket analogy, this article helps healthcare leaders understand what data lakes are (open reservoirs for vast amounts of data), why they’re essential (they reduce the time and resources required to map data), and how they integrate with three common analytic architectures:
- Early-Binding Data Warehouse
- Late-Binding Data Warehouse
- Map-Reduce Hadoop System
What do health systems risk when they hold onto an older enterprise data warehouse (EDW) perspective? By thinking about the EDW as a tool for only historic data that’s not highly reliable and can’t support important decisions, organizations miss out on near real-time (NRT) reporting and valuable decision-making resources. Far from an outdated tool, today’s EDW is capable of meeting rising demands for timely, quality data. Health systems can ensure their EDW reaches its full potential by prioritizing it among their technology and properly supporting it—with the best equipment and human resources. The well maintained EDW is not stuck in the past, but rather, an invaluable tool to move healthcare analytics forward.
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
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:
- 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.
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