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Sri Rajamani

Aiding Analytics Adoption Via Metadata-Driven Architecture: If You Build It, They Will Come

A key feature of effective analytics infrastructure in healthcare is a metadata-driven architecture. In this article, three best practice scenarios are discussed:

  • Automating ETL processes so data analysts have more time to listen and help end users
  • Using a metadata repository to enhance data literacy among users and improve trust in data, thus enabling data governance policies
  • Improving turnaround time for data analysts who support frontline staff who, in turn, monitor interventions based on evidence-based medicine that is constantly changing

The article unravels the components of the metadata-driven architecture as part of an overall analytics platform. Learn the methodology for creating faster data results, generating speed to value, and realizing systemwide analytics adoption.

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Drew Cardon

From Installed to Stalled: Why Sustaining Outcomes Improvement Requires More than Technology

The big first step toward building an outcomes improvement program is installing the analytics platform. But it’s certainly not the only step. Sustaining healthcare outcomes improvement is a triathlon, and the three legs are:

  1. Installing an analytics platform
  2. Gaining adoption
  3. Implementing best practices

The program requires buy-in, enthusiasm, even evangelizing of analytics and its tools throughout the organization. It also requires that learnings from analysis translate into best practices, otherwise the program fails to produce results and will eventually fade away. Equally important is that top-level leadership across the organization, not just IT, supports and promotes the program ongoing. We explore each of the elements and how they come together to create successful and sustainable outcomes improvement that defines leading healthcare organizations.

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Ann Tinker Leslie Falk

Outcomes Improvement Readiness: Learn Where Your Organization Stands

Healthcare organizations need to make lasting, systemwide improvements to make the transition to value-based care models. Starting this work is tough, but a new tool from Health Catalyst will show the way. This 25-question assessment based on an integrate literature review of outcomes improvement research, will show how organizations are performing in five main categories:

  • Adaptive leadership and culture
  • Analytics
  • Best Practices
  • Adoption
  • Financial Alignment

With this information, organizations can start on the path to sustainable outcomes improvement.

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Mike Doyle

How to Avoid the 8 Most Common Pain Points in Becoming a Data-Driven Healthcare Organization

What does it mean to be a data-driven organization? What are the advantages of achieving this status? How can my organization get there? These are all great questions, but they are outnumbered by the eight common pain points every healthcare system encounters along the way:

  1. Conflicting versions of the truth
  2. Lacking a culture that supports data transparency
  3. A lack of trust in the data
  4. Data volume and overload
  5. Struggling to find an effective system
  6. “We already tried that”
  7. A dearth of data-savvy staff
  8. Lack of executive sponsorship

Anticipating and absorbing those pain points is part of the secret to success. This article explores them further, as well as the advantages of letting data guide critical healthcare business decisions.

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Adoption - Additional Content

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Aiding Analytics Adoption Via Metadata-Driven Architecture: If You Build It, They Will Come

A key feature of effective analytics infrastructure in healthcare is a metadata-driven architecture. In this article, three best practice scenarios are discussed:

  • Automating ETL processes so data analysts have more time to listen and help end users
  • Using a metadata repository to enhance data literacy among users and improve trust in data, thus enabling data governance policies
  • Improving turnaround time for data analysts who support frontline staff who, in turn, monitor interventions based on evidence-based medicine that is constantly changing
The article unravels the components of the metadata-driven architecture as part of an overall analytics platform. Learn the methodology for creating faster data results, generating speed to value, and realizing systemwide analytics adoption.

Read More
My Folder

From Installed to Stalled: Why Sustaining Outcomes Improvement Requires More than Technology

The big first step toward building an outcomes improvement program is installing the analytics platform. But it’s certainly not the only step. Sustaining healthcare outcomes improvement is a triathlon, and the three legs are:

  1. Installing an analytics platform
  2. Gaining adoption
  3. Implementing best practices
The program requires buy-in, enthusiasm, even evangelizing of analytics and its tools throughout the organization. It also requires that learnings from analysis translate into best practices, otherwise the program fails to produce results and will eventually fade away. Equally important is that top-level leadership across the organization, not just IT, supports and promotes the program ongoing. We explore each of the elements and how they come together to create successful and sustainable outcomes improvement that defines leading healthcare organizations.

Read More
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Moving Healthcare Outcomes Improvement Projects to the Finish Line

There are many ways to approach outcomes improvement in healthcare. Health Catalyst advocates the three-systems methodology whose individual components remain firm: best practices, adoption, and analytics. There are also various ways to interpret the three systems and this article uncovers nuances in how they are defined. With this unique perspective, organizations may be better able to understand how to develop outcomes improvement projects that not only launch with enthusiasm, but sustain energy over the long-term. Furthermore, outcomes improvement done right is scalable so that small firms, those with fewer resources, can adapt the methodology to improve their performance.

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My Folder

Outcomes Improvement Readiness: Learn Where Your Organization Stands

Healthcare organizations need to make lasting, systemwide improvements to make the transition to value-based care models. Starting this work is tough, but a new tool from Health Catalyst will show the way. This 25-question assessment based on an integrate literature review of outcomes improvement research, will show how organizations are performing in five main categories:

  • Adaptive leadership and culture
  • Analytics
  • Best Practices
  • Adoption
  • Financial Alignment
With this information, organizations can start on the path to sustainable outcomes improvement.

Read More
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Improving Patient Safety and Quality through Culture, Clinical Analytics, Evidence-Based Practices, and Adoption

According to the Centers of Disease Control (CDC), an estimated 70,000 patients die each year from hospital-associated infections (HAIs): contrast the CDC statistic with the fact that only 35,000 people die each year in the U.S. from motor vehicle accidents.  Learn key best practices in patient safety and quality including:  patient safety as a team sport, the added challenges of healthcare being the most complex, adaptive system, and how culture, analytics, and content contribute to improve outcomes and lower costs.

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How to Avoid the 8 Most Common Pain Points in Becoming a Data-Driven Healthcare Organization

What does it mean to be a data-driven organization? What are the advantages of achieving this status? How can my organization get there? These are all great questions, but they are outnumbered by the eight common pain points every healthcare system encounters along the way:

  1. Conflicting versions of the truth
  2. Lacking a culture that supports data transparency
  3. A lack of trust in the data
  4. Data volume and overload
  5. Struggling to find an effective system
  6. “We already tried that”
  7. A dearth of data-savvy staff
  8. Lack of executive sponsorship
Anticipating and absorbing those pain points is part of the secret to success. This article explores them further, as well as the advantages of letting data guide critical healthcare business decisions.

Read More
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A Data-Driven Culture: Making Data a Part of Everyday Decision Making

Healthcare organizations are establishing data-driven improvement processes to improve the quality of care at a lower cost. Implementing an analytics infrastructure, clinical content, and deployment processes required to achieve success can be a challenge. Surprisingly, building the technology infrastructure is the relatively easy part. Ensuring clinicians are utilizing the data in every day decision making and creating a data-driven culture is more difficult. Senior leadership engagement is crucial, driving the organization to undergo a purposeful change, and making analytics and improvement everyone’s responsibility.

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Healthcare Analytics Careers: New Roles for the Brave, New World of Value-based Care

Job titles can be leading indicators of the direction an industry is moving and the same holds true for healthcare. The new healthcare economic model—from fee-for-service (FFS) to value-based—is driving a change in roles and responsibilities for professionals seeking healthcare analytics careers. Motivated by CMS and commercial payers, healthcare organizations are realizing the need to find and hire new types of healthcare professionals, a Chief Population Health Officer or Vice President of Clinical Informatics, who are focused on value. Senior leaders are seeking to build teams that have the ability to bring together analytics, best-practice clinical content, and process improvement to create long-term, sustainable change across their healthcare systems.

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The 4 Clinical Teams Needed to Drive Sustainable Improvement

As the healthcare industry shifts from a fee-for-service to pay-for-performance and accountable care organizations are under greater pressure to make improvements to their clinical, financial and operational outcomes. As clinical quality improvement efforts grow systematically improving and sustaining care across the organization becomes more challenging. In order to ensure sustainable, long-term change a cross-functional, team-based approach that accelerates the implementation of change throughout the organization is necessary. This is the adoption system. Without an adoption system, improvement initiatives become a series of one off projects that may have a temporary positive impact, but soon return to the baseline level.

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Successfully Rolling Out Healthcare Improvement Initiatives with Key Influencers

Rolling out a healthcare improvement initiative and getting buy-in from clinicians can be tricky. Paul Revere’s midnight ride shows how finding and using key influencers can help prompt action. This knowledge can be used to drive success of hospital data-driven improvement initiatives too. Achieving buy-in of new best practices and process across an organization is not easy, but it is imperative to success. Picking the right people for these teams means picking the innovators and early adopters in the organization, as the Paul Revere example illustrates. Once these people are identified, putting them into three key teams (the Guidance team, the Workgroup, and the Clinical Implementation team) will get the organization the right deployment system to drive successful improvement initiative on a system-wide basis.

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The Best Organizational Structure for Healthcare Analytics

After working with many healthcare organizations to help them implement the appropriate EDW for their needs, we’ve learned how important it is to create cross-functional teams from across the organization. Why? These cross-functional teams will simultaneously improve clinical and financial outcomes and demonstrate ROI. By following this approach, you’ll experience the following advantages:

  • Removal of organizational barriers between team members
  • Prioritization of BI and analytic efforts according to institutional readiness and need
  • Engagement and prioritization from appropriate leadership
  • Buy in from each level of the organization to improvement goals

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Keys to a Successful Health Catalyst Data Warehouse Platform and Analytics Implementation

We have found that many customers have similar questions about how the implementation process works when rolling out a Health Catalyst Late-Binding ™ data warehouse platform and analytics solutions.  So, we thought it would be useful to produce a document that we hope will answer the majority of these and other common questions. The keys for a successful Health Catalyst implementation are outlined step-by-step format. Pre-step (most important): Identify key personnel resources needed on the health system side, 1) Implementation Planning, 2) Deploy Hardware, 3) Technical Kickoff Meeting with the Client and Health Catalyst Deployment Teams, 4) Access Source Data, 5) Install Platform, 6) Load Data, 7) Install Foundational Applications, 8) Install Discovery Applications, and 9) Install Advanced Applications At the beginning of the project, Health Catalyst will begin a collaborative implementation planning process resulting in a timeline tailored to each project. Some projects can be accelerated, with the initial phase completed in 90 days.  Your health system will have questions specific to your organization and your circumstances. We are happy to answer those in person.

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How to Sustain Healthcare Quality Improvement in 3 Critical Steps

Ronald D. Snee, a fellow with the American Society for Quality, articulates that organizations don’t hold quality and cost gains because they don’t make improvement the backbone of their organization. Rather, they approach improvement as a series of initiatives. He states, “Many organizations focus on sustaining the gains only after improvement has been achieved. Intuitively, that may seem the correct sequence, but it is in fact backwards. The time to focus on sustaining improvement gains is well before the initiative is launched.” In this article, I review 3 critical organizational steps that can help sustain those gains.

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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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