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.
Enterprise Data Warehouse / Data Operating system
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:
These seven components will reveal how DOS is a data-first system that can extract value from healthcare data and allow leadership and analytics teams to fully develop the insights necessary for health system transformation.
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
Data lakes are useful parts of all three platforms, but deciding which platform to integrate a data lake with depends heavily on a health system’s resources and infrastructure.
Once understood and appropriately integrated with the optimal analytics platform, data lakes save health systems time, money, and resources by adding structure to data only as use cases arise.
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
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:
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.
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.
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.
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.
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.
Best Solution to Aggregate Healthcare Data Including Clinical, Financial, Research, Population Health, and More
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.