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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

Spend time reading content for you

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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Clinical Data Warehouse: Why You Really Need One

From time to time, people question whether they really need a clinical data warehouse. The first wave of data warehousing projects didn’t deliver much value and healthcare CIOs had plenty of tasks to accomplish in the meantime. But today’s technology, data, and regulations scream for an analytics solution and a clinical data warehouse can offer this. A hospital should consider reporting requirements and technical requirements. Finally, when an organization has access to its data, leaders are empowered to make informed decisions.

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Database vs Data Warehouse: A Comparative Review

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.

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

One of the most common questions we hear is some flavor of  "should we build or buy a data warehouse?”   This is a very understandable question.  And there are multiple reasons  for either building your own data warehouse or buying it.  We acknowledge that.  And even though we know that we naturally advocate the advantages of buying a data warehouse, we also know that buying a data warehouse is not for everyone, and we understand the circumstances in which health systems will choose to build their own data warehouse.  In this article, we do our best to collect the pros and cons of each scenario, and even suggest a hybrid solution, so that you can better assess your situation, understand your options and make the best decision for you.  

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Data Warehousing in Healthcare: Is It Necessary?

Wondering if you really need a healthcare data warehouse or if the technology you already possess if enough? Data warehousing enables healthcare analytics. It will help fulfill reporting requirements so your analysts can concentrate on analyzing data. It can offer near real-time answers to many questions, whether financial, clinical, or technical. Eventually, you’ll wonder “How did I ge tby so long without it?”

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Hospital Data Warehouse: The Best Reporting Tool for Efficient, Consistent Hospital Reports

Hospital reporting can be a stressful and time-consuming process with finance, quality, human resources and clinical departments scrambling to complete data for reports - often times with maddeningly inconsistent data. Too familiar with this hospital report anxiety? Bobbi Brown explains how a data warehouse can help by enabling efficient and scalable reporting, and enabling consistent reporting that everyone can trust.

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How Texas Children’s Hospital Reduced Their Healthcare Labor Costs

Texas Children’s Hospital, a client of mine, found they needed an immediate solution to address their labor and productivity challenges....The Health Catalyst Labor Productivity Advanced Application delivered a view of staffing levels, volume and productivity across Texas Children's various cost centers. The application enables the business and unit managers to track and manage their resources. It delivers information into the hands of decision makers to help them manage their business. Managers can track performance as often as daily to see exactly how well labor is being allocated and make rapid modifications to counter scheduling problems. In the event that labor utilization outpaces volume, managers can drill further into labor data to understand utilization at the job code level.

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5 Myths You Won’t Need to Worry About When Adopting a Clinical Data Warehouse

  If you’ve been thinking about implementing a clinical enterprise data warehouse (EDW), chances are you have a few questions about the possible problems you’ll encounter. In this Insight, Mike addresses some fears that are actually common myths including: 5) I can't provide broad access to my EDW, 4) users don't need or want the ability to write SQL queries, 3) I don't need an EDW -- my BI tool does everything I need, 2) EDWs are too expensive, and 1) EDWs take too long to complete.

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Late-Binding™ vs. EMR-based Models: A Comparison of Healthcare Data Warehouse Methodologies

  Baffled by the options for healthcare data warehouses? Here, Eric compares two models: Late-Binding™ and EMR-based. Many organizations are taking a wait-and-see approach with analytics solutions provided by EMR vendors and other out-of-the-box solutions. In this post, Eric compares two models of a data warehouse: Late-Binding™ and EMR-based. He also outline important factors to consider when planning for long-term success in data warehousing and analytics.

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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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The New Health Catalyst 2.0 Product Introduction

Read and watch to learn about the Health Catalyst 2.0 platform and product applications. Health Catalyst 2.0 dramatically increases the speed and effectiveness of client implementations. Featured changes include: 1) Improved automation of metadata gathering from multiple-source IT systems into the data warehouse. This means fewer client resources, fewer mapping errors, and implementation EIGHT times faster than before and 2) Scores of powerful new content-driven applications that turn all that data into action. The platform provides the ability to simultaneously automate and standardize improvements across multiple clinical and operational processes.

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4 Options for Choosing the Best Healthcare Analytics Solutions

Analytics is a buzzword in healthcare today. You hear it often: “What does an organization need to succeed in a value-based care environment? Robust analytics.” But what exactly does that mean? Anyone who has looked into implementing “analytics” for their organization knows that a multitude of options for healthcare analytics are available—and each vendor touts its approach to analytics as the best. I’d like to take a moment here to summarize four primary analytics options available to healthcare organizations today: 1) hosted analytics service providers, 2) "best of breed" point solutions, 3) EMR vendors, 4) healthcare data warehouse platform providers.

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Accountable Care Organization Software: 5 Critical Information Systems

More and more, healthcare is molded and critically impacted by the software and information technology that surrounds and supports the industry. As a consequence, the C-level suite beyond the CIO must actively participate in the evolution of their organization’s IT strategy, particularly at the layer of technology where software directly supports workflows and business processes.There are five information systems that are indispensable to the success of an Accountable Care Organization (ACO). Those five critical information systems are 1) An Electronic Medical Record (EMR), 2) A Health Information Exchange (HIE), 3) An Activity Based Costing (ABC) system, 4) A Patient Reported Outcomes (PRO) system, and 5) An Enterprise Data Warehouse (EDW).

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Problems With Relying on EHR Analytics For Your ACO: You Need a True Data Warehouse

Many CIOs, along with their other C-suite colleagues, are anticipating a catharsis on completing massive EHR deployment projects. Before long, however, they come to the unwelcome realization that the EHR is just one component needed to provide the actionable intelligence health systems need to survive in a value-based purchasing environment.

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The Best Solution for Declining Medicare Reimbursements

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?

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Finding $5.7 Million with a Healthcare Data Warehouse

In April 2013, Health Catalyst partnered with North Memorial to develop the Health Catalyst Professional Billing Advanced Application built on our Late-binding™ Data Warehouse platform. The application automates data capture previously done manually by the coding staff. The development process was completed in six weeks. The solution is a perfect marriage of technology supporting workflow processes to become a natural part of billing. The results to date include: 1) 6% increase in notes that had sufficient clinical data for billing, 2) 25% improvement in professional coder efficiency, and 3) a potential to reclaim more than $5.7 million over the next three years. Read our success story to find more details.

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What Does It Take to Develop a Clinical Data Mart in 90 Days?

Designing and implementing a data mart that fits into an enterprise data warehouse can be a very resource-intensive project taking considerable time. Health systems faced with this fact often compromise by bringing in portions of data sources or skipping entire systems wholesale. As time goes on, the maintenance of existing data marts becomes overly burdensome, consuming resources which can contribute to the avoidance of adding new sources of information. Quick data mart development and installation, combined with a low maintenance burden is now a requirement for health systems that want a true enterprise data warehouse. We recently developed a data mart based on the core clinical tables from the Cerner EMR in just 90 days . By exploiting years of health care data warehousing experience, we witnessed an almost perfect storm of events allowing us to accomplish this task.

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Best Practice EDW Award Winners: Stanford and Health Catalyst

Each year, TDWI identifies and honors companies demonstrating “best practices in developing, deploying, and maintaining applications for business intelligence (BI), data warehousing (DW), and related data management (DM) areas.” Nominees are evaluated based on business value, the degree to which the solution “vision” has been implemented, relevance to other organizations, and the innovation association with the approach. This year, Stanford Hospitals and Clinics (SHC) and Health Catalyst were honored as winners of the 2013 Best Practices award for the Enterprise Data Warehousing category from TDWI.

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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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The Best Approach to Healthcare Analytics

Healthcare has remained entrenched in its cottage industry-style of operation, even within huge medical centers and significant medical innovation. The result, as documented by Dr. John Wennberg’s Dartmouth Atlas of Health Care project , is unwarranted variation in the practice of medicine and in the use of medical resources including underuse of effective care, misuse of care, and overuse of care provided to specific patient populations. The root of the problem, Wennberg concludes, is that there is no healthcare “system.” At Health Catalyst, we agree. Healthcare needs to be systematized and standardized in three key areas:1) healthcare analytics or measurement, 2) adoption or how teams and work are organized, and 3) best practice or how evidence/knowledge is gathered, evaluated, and disseminated for adoption.

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