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Mike Doyle

Early- or Late-binding Approaches to Healthcare Data Warehousing: Which Is Better for You?

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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Steve Barlow

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.

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Doug Adamson

Data Warehouse Tools: Faster Time-to-Value for Your Healthcare Data Warehouse

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.

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Dale Sanders

10 Trends in Healthcare Data Warehousing That Every Health System Needs to Know

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.

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Cherbon VanEtten

A New Way to Look at Healthcare Data Models

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.

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Steve Barlow

Star Schema vs. Late-Binding™: Best Approach for a Healthcare Data Warehouse

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.

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Mike Doyle

6 Surprising Benefits of Healthcare Data Warehouses: Getting More Than You Expected

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.

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Dan Burton

What Does a Data Warehouse Cost? How to Get a Return on Your Investment

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.

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Dale Sanders

The Late-Binding Data Warehouse Explained (white paper)

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.

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Steve Barlow

6 Reasons Why Healthcare Data Warehouses Fail (Executive Report)

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.

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Mike Doyle

Build vs. Buy a Healthcare Enterprise Data Warehouse: Which is Best for You? (Executive Report)

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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Data Warehouse / EDW - Additional Content

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Three Must-Haves for Generating Innovation in Healthcare IT

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:

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

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Hadoop in Healthcare: Getting More from Analytics

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:

  1. Archiving
  2. Streaming
  3. Machine learning

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The Top Seven Quick Wins You Get with a Healthcare Data Warehouse

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:

  1. Provides significantly faster access to data.
  2. Improves data-driven decision making.
  3. Enables a data-driven culture.
  4. Provides world class report automation.
  5. Significantly improves data quality and accuracy.
  6. Provides significantly faster product implementation.
  7. 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.

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Evaluating an EHR-Centric vs Data Warehouse-Centric Analytics Strategy: Seven Points to Ponder

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:

  1. Incorporating data from a wide range of sources
  2. Ease of reporting
  3. The data mart concept
  4. Relevance of each to value-based care
  5. Relevance of each to managing population health
  6. Surfacing results of sophisticated analysis for physicians at the right time
  7. 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.

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Questions You Should Ask When Selecting a Healthcare Analytics Platform

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.

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Clinical Data Management: 3 Improvement Strategies

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.

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Early- vs. Late-Binding Speed: Which Is the Faster Data Warehouse?

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.

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Data Lake vs. Data Warehouse: Which is Right for Healthcare?

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.

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Early- or Late-binding Approaches to Healthcare Data Warehousing: Which Is Better for You?

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.

Read More
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6 Reasons Why Healthcare Data Warehouses Fail

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.

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Data Management and Healthcare: Why Databases and EMRs Don’t Make the Cut on Their Own

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.

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Data-Driven Healthcare That Works: a Physician Group Perspective

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.

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Data Warehouse Tools: Faster Time-to-Value for Your Healthcare Data Warehouse

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.

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Healthcare Databases: Purpose, Strengths, Weaknesses

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.

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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.

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10 Trends in Healthcare Data Warehousing That Every Health System Needs to Know

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.

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Clinical Data Repository Versus a Data Warehouse — Which Do You Need?

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.

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5 Reasons Healthcare Data Is Unique and Difficult to Measure

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.

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EDW Cloud Hosting: Is It Right for Your Health System?

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.

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Healthcare Data Warehouse Models Explained

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.

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Shifting from EMRs to Clinical Data Warehousing and Analytics

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.

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Healthcare Business Intelligence: What Your Strategy Needs

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.

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The Best Data Architecture: Know When to Bind Your Healthcare Data

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.

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A New Way to Look at Healthcare Data Models

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.

Read More
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