Collaborative Partnerships and a Three-System Approach to Driving Healthcare Transformation

Healthcare organizations are among the most complex forms of human organization ever attempted to be managed, making transformation a daunting task. Despite the challenges associated with change, Texas Children’s Hospital identified that it needed to evolve into a data-driven outcomes improvement organization.

Texas Children’s embarked on a journey to transform care, building a three-systems approach—analytics, best practice, and adoption—designed to develop a data-driven quality improvement organization that could achieve outcomes improvement expediently and at scale across the entire organization. Texas Children’s leadership knew that the foundation for clinical systems integration would be meaningful, actionable data. That realization prompted the organization to implement the Health Catalyst Analytics Platform including a Late-Binding™ Data Warehouse (EDW) and a broad suite of analytics applications.

After deploying the analytics platform supported by multidisciplinary quality improvement teams, Texas Children’s was able to improve patient outcomes related to the following:

  • 35 percent relative decrease in hospital-acquired conditions (HACs).
  • 44 percent relative decrease in LOS for patients with Diabetic ketoacidosis (DKA).
  • 30.9 percent relative reduction in recurrent DKA admissions per fiscal year.


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Turning Data from Five Different EHR Vendors into Actionable Insights

When healthcare information systems don’t talk to each other, countless inefficiencies and patient safety issues may arise.

Community Health Network (CHNw) believes in delivering outstanding care to every patient. In order to minimize patient safety risks and inefficiencies resulting from using different EHRs, CHNw embarked on a journey to integrate its healthcare information technologies. After implementing a Late-Binding™ Data Warehouse from Health Catalyst that integrates all key data sources, CHNw now has a consistent and comprehensive perspective for multiple patient encounters across the enterprise. It has achieved the following results:

  • Data from multiple EHR vendors, including four inpatient EHRs and two ambulatory EHRs, plus five transactional systems—HR, patient experience, patient safety, finance, and supply chain— were integrated within 12 months.
  • More than 55,000 data elements and over 18 billion rows of data were incorporated.
  • Patient-to-patient matching was implemented for over one million patients across the four inpatient EHRs. This is vital for managing patient populations.
  • Operational efficiency was improved by 70 percent, with data architects spending an estimated 15 percent of time supporting interfaces compared to an estimated 40-50 percent before the integration. In one example, CHNw linked its ERP/costing system to the EDW’s EHR source marts with just a single interface; previously, this would have required building separate interfaces for all six EHRs.
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Effective Healthcare Data Governance: How One Hospital System is Managing its Data Assets to Improve Outcomes

As healthcare invests in analytics to meet the IHI Triple Aim, data has become its most valuable asset—and one of the most challenging to manage. Healthcare organizations must integrate data from a complex array of internal and external sources. To establish a single source of truth, The University of Kansas Hospital deployed an enterprise data warehouse (EDW). However, they quickly realized that without an effective data governance program clinicians and operational leaders would not trust the data. Led by senior leadership commitment, The University of Kansas Hospital established processes to define data, assign data ownership and identify and resolve data quality issues. They also have 70+ standardized enterprise data definition approvals planned for completion in the first year and have created a multi-year data governance roadmap to ensure a sustained focus on data quality and accessibility.

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Improving Healthcare Data Quality to Drive Lower C-Section Rates

Cesarean deliveries have become one of the most common surgical procedures performed in the United States each year.  Between 1998 and 2008, the rate of cesarean delivery in the United States rose by 50 percent — from 22-33 percent of all births. Many healthcare stakeholders have turned their attention to reducing this rate for clinical, financial and regulatory reasons. Read how this healthcare system developed a Women and Newborn’s population health registry and discovered they had to start first with addressing their healthcare data quality issues.

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Other Content in Data: Quality, Management, Governance


How to Use Text Analytics in Healthcare to Improve Outcomes—Why You Need More than NLP

Given the fact that up to 80 percent of clinical data is stored in unstructured text, healthcare organizations need to harness the power of text analytics. But, surprisingly, less than five percent of health systems use it due to resource limitations and the complexity of text analytics.

But given the industry’s necessity to use text analytics to create precise patient registries, enhance their understanding of high-risk patient populations, and improve outcomes, this executive report explains why systems must start using it—and explains how to get started.

Health systems can start using text analytics to improve outcomes by focusing on four key components:

  1. Optimize text search (display, medical terminologies, and context).
  2. Enhance context and extract values with an NLP pipeline.
  3. Always validate the algorithm.
  4. Focus on interoperability and integration using a Late-Binding approach.

This broad approach with position health systems for clinical and financial success.

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Data for Improving Healthcare vs. Data for Exasperating Healthcare Workers

For better or worse, hospitals are obligated to collect and report data for regulatory purposes. Or they feel compelled to meet some reputational metric. The problem is, an inordinate amount of time can be spent on what is considered data for accountability or punishment, when the real focus should be on data for learning and improvement. When time, effort, and resources are dedicated to the latter, it leads to real outcomes improvement.

Deming has three views of focusing on a process and this article applies them to healthcare:

  1. Sub-optimization, over-emphasizing a single part at the expense of the whole.
  2. Extreme over-emphasis, also called gaming the system.
  3. The right amount of focus, the only path to improvement.

With data for learning as the primary goal, improving clinical, operational, and financial processes becomes an internal strategy that lifts the entire healthcare system.

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When Healthcare Data Analysts Fulfill the Data Detective Role

There’s a new way to think about healthcare data analysts. Give them the responsibilities of a data detective. If ever there were a Sherlock Holmes of healthcare analytics, it’s the analyst who thinks like a detective. Part scientist, part bloodhound, part magician, the healthcare data detective thrives on discovery, extracting pearls of insight where others have previously returned emptyhanded. This valuable role comprises critical thinkers, story engineers, and sleuths who look at healthcare data in a different way. Three attributes define the data detective:

  1. They are inquisitive and relentless with their questions.
  2. They let the data inform.
  3. They drive to the heart of what matters.

Innovative analytics leaders understand the importance of supporting the data analyst through the data detective career track, and the need to start developing this role right away in the pursuit of outcomes improvement in all healthcare domains.

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The Surprising Benefits of Bad Healthcare Data

Bad healthcare data is inevitable. Whether it happens as a result of human input error or an incorrect rule, bad healthcare data will happen. And rather than ignoring it, hiding it, or scrubbing it, health systems need to take a more transparent approach.

Bad healthcare data, when approached correctly, has four surprising benefits:

  1. Provides valuable feedback to application users/data consumers.
  2. Inspires an improvement culture.
  3. Creates a Snowball Effect of Success.
  4. Improves Data Accuracy.

It’s not easy to make the shift from fearing bad data to embracing it, but there are several steps systems can take to start creating a data transparency culture:

  1. Empower: encourage data consumers to provide feedback.
  2. Share: Provide a mechanism for sharing feedback.
  3. Act: dedicate time and resources to respond and act.

Health systems prepared and willing to fix bad data will ultimately improve data quality.

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Integrating Data Across Systems of Care: Four Perspectives from Industry Leaders

How to integrate data across systems of care depends on the organization’s perspective. In this report from the Scottsdale Institute, learn how leaders from Health Catalyst, Cerner, Geisinger, and CHI have tackled issues such as population health, HIEs, value-based payments, and data governance. Ultimately the starting point isn’t really how to integrate the data, but why the data needs to be integrated in the first place. The approach changes, for example, when an organization needs to combine data for a regulatory report versus using data for real-time patient-physician interaction.

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Healthcare Data: Visual Discovery and Governance on any Device (Webinar)

In this webinar, Donald will offer his strategic insights into these data reporting trends including how they relate to current and future product development at Qlik, widely considered to be the leading cross-industry data visualization company in the world. Dale will serve as moderator and provocateur in this interactive Q&A interview-style forum.

Dale and Donald are two of the most interesting future analytics thinkers within and outside of healthcare, well-known for their ability to stretch the minds of audiences. Known for bold and insightful predictions, Dale Sanders brings more than three decades of analytics experience to the discussion. While he began his career managing data for the National Security Agency during the touch-and-go of nuclear warfare operations in the ’80s, (read more here)he saw an important opportunity to make a difference in healthcare and moved to serve as CIO at several organizations. He has an unusually accurate track record for predicting emerging changes in industries and organizations, and aligning those predictions with data and cultural strategies that have helped healthcare organizations to thrive on those changes, not just survive.

Joining Dale is Donald Farmer who brings more than 30 years of experience developing and executing data and analytics strategies outside of healthcare. Included in his experience, Donald spent a decade at Microsoft focused on data mining, and to many, he became the face of Microsoft’s Business Intelligence business. Steeped in technology, Donald is a widely sought after speaker because of his breadth and depth of technical experience and his ability to translate ones and zeros into human-readable plans. Dale and Donald will share the time to talk about the impact of evolving technology, business intelligence tool sets, and analytics consumption models as they relate to the following:

  1. Innovative current techniques and future trends in the visual exploration and discovery within data, as opposed to generating reports from data
  2. Delivery of the right data at the right time with the right visual discovery tools to any platform from desktop computers to smart phones
  3. Governance models for this new data environment that allow for exploitation of its full value in context to the decision making situations while avoiding privacy and security violations

We encourage you to tune in for what could be one of the most interesting healthcare analytics webinars of 2015. Please join us.


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Demystifying Healthcare Data Governance

As the Age of Analytics emerges in healthcare, health system executives are increasingly challenged to define a data governance strategy that maximizes the value of data to the mission of their organizations.

Adding to that challenge, the competitive nature of the data warehouse and analytics market place has resulted in significant noise from vendors and consultants alike who promise to help health systems develop their data governance strategy. Having gone on his own turbulent data governance ride as a CIO in the US Air Force and healthcare, Dale Sanders, Senior Vice President at Health Catalyst will cut through the market noise to cover the following topics:

  • General concepts of data governance, regardless of industry
  • Unique aspects of data governance in healthcare
  • Data governance in a “Late Binding” data warehouse
  • The layers and roles in data governance
  • The four “Closed Loops” of healthcare analytics and data governance
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