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Agnostic Analytics Solutions vs. EHRs: Six Reasons EHRs Can’t Deliver True Healthcare Interoperability

As enterprisewide analytics demands grow across healthcare, health systems that rely on EHRs from major vendors are hitting limitations in their analytics capabilities. EHR vendors have responded with custom and point-solution tools, but these tend to generate more complications (e.g., multiple data stores and disjointed solutions) than analytics interoperability. To get value out of existing EHRs while also evolving towards more mature analytics, health systems must partner with an analytics vendor that provides an enterprise data management and analytics platform as well as deep improvement implementation experience. Vendor tools and expertise will help organizations leverage their EHRs to meet population health management and value-based payment goals, as well as pursue some of today’s top healthcare strategic goals:

  1. Growth.
  2. Innovation.
  3. Digitization.

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Healthcare Analytics Platform: DOS Delivers the 7 Essential Components

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:

  1. Acquire
  2. Organize
  3. Standardize
  4. Analyze
  5. Deliver
  6. Orchestrate
  7. Extend
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.

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Seven Ways DOS™ Simplifies the Complexities of Healthcare IT

Health Catalyst Data Operating System (DOS) is a revolutionary architecture that addresses the digital and data problems confronting healthcare now and in the future. It is an analytics galaxy that encompasses data platforms, machine learning, analytics applications, and the fabric to stitch all these components together. DOS addresses these seven critical areas of healthcare IT:

  1. Healthcare data management and acquisition
  2. Integrating data in mergers and acquisitions
  3. Enabling a personal health record
  4. Scaling existing, homegrown data warehouses
  5. Ingesting the human health data ecosystem
  6. Providers becoming payers
  7. Extending the life and current value of EHR investments
This white paper illustrates these healthcare system needs detail and explains the attributes of DOS. Read how DOS is the right technology for tackling healthcare’s big issues, including big data, physician burnout, rising healthcare expenses, and the productivity backfire created by other healthcare technologies.

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What Is a Healthcare Data Lake and Why Do You Need One? Imagine a Supermarket

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:

  1. Early-Binding Data Warehouse
  2. Late-Binding Data Warehouse
  3. 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.

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The Healthcare Data Warehouse: Evolved for Today’s Analytics Demands

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.

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A 5-Step Guide for Successful Healthcare Data Warehouse Operations

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:

  1. Start with a Leadership Commitment to Outcomes Improvement
  2. Build the Right Team
  3. Establish Effective Partnerships with IT
  4. Develop Interest and Gain Buy-In
  5. Pivot Toward Maintaining Success
Successfully implementing and sustaining EDW operations is about establishing and managing priorities and understanding the enterprise-wide implications.

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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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Pragmatic Innovation in Healthcare: Taking Risk and Establishing Partnerships

Investment and innovation in healthcare is driven by health system providers partnering with entrepreneurs. During my time at venture capital companies, I saw how sharing risk could marry the concept of innovation with pragmatism. Health Catalyst uses Pragmatic Innovation as an operating principle. This is evident on a company-level and in the risks we take with our client-partners, such as Allina Health. Earlier this year, Health Catalyst and Allina Health announced an exciting innovation in healthcare: a true partnership to improve outcomes. Each party took a risk, and each will share in the improvements derived.

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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.

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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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