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Dan Lowder

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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Dale Sanders

Why Data Governance Requires a Henry Kissinger

The number of partnerships and collaboratives in healthcare continues to climb. One of the many complications of these deals involve integrating and governing data. In fact, 100% of the 2014 Pioneer ACOs reported that they had difficulties with data integration, which had a major and negative impact on performance. Right now, data governance in healthcare is in a transitionary stage not unlike the U.S. in the 1980s. Leaders who manage the data governance in these partnerships must be like a data-savvy version of Henry Kissinger, able to bring the data of loosely affiliated organizations together for the benefit of all.

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Brian Eliason Neil Anderson

Why the Data Steward’s Role Is Critical to Sustained Outcomes Improvement in Healthcare

The data steward is critical to sustained outcomes improvement, yet they tend to be underappreciated members of the healthcare analytics family. Combining the invaluable technical expertise of a data analyst with the vital clinical knowledge of an experienced caregiver, the data steward’s skills and proficiency at both positions brings value beyond measure to any outcomes improvement project. Unfortunately, all too often, their role is non-existent even though potential candidates for the job are located in multiple data sources throughout the organization. Among other responsibilities, the data steward:

  1. Reinforces the global data governance principles.
  2. Helps develop and refine details of local data governance practices.
  3. Is the eyes and ears of the organization with respect to data governance and the governance committee.
  4. Provides direction to peers regarding appropriate data definitions, usage, and access.
  5. Anticipates local consequences of global changes

For innovative health system leaders who have specifically recognized this emerging role, the ROI of data stewards who help achieve improved outcomes is very worthwhile.

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Dale Sanders

Demystifying Healthcare Data Governance: An Executive Report

Finding the perfect data governance environment is an elusive target. It’s important to govern to the least extent necessary in order to achieve the greatest common good. With the three data governance cultures, authoritarian, tribal, and democratic, the latter is best for a balanced, productive governance strategy.

The Triple Aim of data governance is: 1) ensuring data quality, 2) building data literacy, and 3) maximizing data exploitation for the organization’s benefit. The overall strategy should be guided by these three principles under the guidance of the data governance committee.

Data governance committees need to be sponsored at the executive board and leadership level, with supporting roles defined for data stewards, data architects, database and systems administrators, and data analysts. Data governance committees need to avoid the most common failure modes: wandering, technical overkill, political infighting, and bureaucratic red tape.

Healthcare organizations that are undergoing analytics adoption will also go through six phases of data governance including: 1) establishing the tone for becoming a data-driven organization, 2) providing access to data, 3) establishing data stewards, 4) establishing a data quality program, 5) exploiting data for the benefit of the organization, 6) the strategic acquisition of data to benefit the organization.

As U.S. healthcare moves into its next stage of evolution, the organizations that will survive and thrive will be those who most effectively acquire, analyze, and utilize their data to its fullest extent. Such is the mission of data governance.

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John Wadsworth

Health Data Stewardship and Its Importance in Healthcare Analytics

Health data stewards are keepers of tribal knowledge, and they’re invaluable when a health system launches or expands a healthcare data analytics initiative. Their intimate and expansive knowledge of how data is collected to represent workflow across different systems can save days’ worth of time (and cost) in the development process while improving the accuracy of the analytics output. But getting anything more than a few spare moments of their time can be difficult because health data stewardship isn’t part of their job description. While it may seem difficult to justify at first, organizations need to formalize the role of the health data steward. The investment will ultimately return many times its value as the organization realizes the advantage of the analytics.

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Brian Eliason Jason Burke Pete Hess

Master Data Management in Healthcare: 3 Approaches

Master data management is key for healthcare organizations looks to integrate different systems. The two types of master data are identity data and reference data. Master data management is the process of linking identity data and reference data. MDM is important for mergers and acquisitions and health information exchanges. The three approaches for MDM are: IT system consolidation, Upstream MDM implementation, and Downstream master data reconciliation in an enterprise data warehouse.

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Eric Just

Healthcare Data Stewardship: The Key to Going from Information Poor to Information Rich

Poor healthcare data stewardship is part of the problem for health systems that feel like they are “data rich and information poor.” But this can be fixed two ways: implementing a data warehouse and improving data stewardship. Without appropriate healthcare data stewardship, even the best infrastructures become underutilized and poorly understood by knowledge workers who could be generating value with the data every day. Data stewards will become critical partners to the data warehouse team in creating a thriving user base. They are the data librarians who advise and guide users, and help them get the most value out of the enterprise data warehouse.

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Dan LeSueur

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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Dale Sanders

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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Ann Tinker Kathleen Merkley

Overcoming Clinical Data Problems in Quality Improvement Projects

Starting your clinical quality improvement projects with access to data you’ve never seen before is exciting! But as analysis starts, you notice missing and incomplete data. Data quality problems are one of the most common but unexpected initial challenges of any substantive clinical quality improvement. project. Anny and Kathy both share keys to success learned from years of experience to overcome that trough of despair.

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Dale Sanders

7 Essential Practices for Data Governance in Healthcare

While information and data security is a long-standing body of practice and knowledge in corporations, data governance is less mature, especially in healthcare. As a result of this lower maturity, there is a tendency to operate in extremes, either too much governance or too little. Over time, as data and analytic maturity increases, the healthcare industry will find a natural equilibrium. In this post, Dale identifies simple practices of data governance in 7 areas: 1) balanced, lean governance, 2) data quality, 3) data access, 4) data literacy, 5) data content, 6) analytic prioritization, and 7) master data management.

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Data: Quality, Management, Governance - Additional Content

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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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