How to Run Analytics for More Actionable, Timely Insights: A Healthcare Data Quality Framework
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The document outlines a healthcare data quality framework that emphasizes the importance of high-quality data for timely and actionable insights, especially in the context of COVID-19. It details a systematic approach for healthcare organizations to measure, monitor, and improve data quality throughout the data lifecycle, encouraging the establishment of a data quality culture. A coalition of data experts and leadership support is recommended to ensure effective implementation of quality data processes.
How to Run Analytics for More Actionable, Timely Insights: A Healthcare Data Quality Framework
- 1. How to Run Analytics for More Actionable, Timely Insights: A Healthcare Data Quality Framework
- 2. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Analytics for More Actionable, Timely Insights COVID-19 response and recovery demands data fit to drive timely, actionable insights at an unprecedented level. As a result, health systems increasingly recognize data quality as a prerequisite for clinical, financial, and operational analytics.
- 3. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Analytics for More Actionable, Timely Insights To quantify data quality, healthcare data teams can use measurable data attributes that demonstrate whether it is fit for a specific purpose. Good and transparent data quality instills confidence in the insight provided, which accelerates sound decision making. Conversely, poor data quality degrades confidence, ultimately delaying or leading to wrong decisions.
- 4. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Analytics for More Actionable, Timely Insights Organizations tend to understand the value of data quality, but the fundamentals of a system that generates quality data and analytics are complex. To meet the COVID-19 urgency for quality data and ongoing data quality challenges, health systems need an actionable structure to navigate the essential phases of a comprehensive and proactive data quality strategy.
- 5. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Analytics for More Actionable, Timely Insights A framework for healthcare data quality provides a systematic way to measure, monitor, and determine if data is “fit for purpose” (i.e., it can serve its intended purpose).
- 6. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. The Four Levels of Healthcare Data Quality Defining data quality levels helps an organization understand the current state of its data quality and whether its data is improving. Data users can follow the Four Levels of Data Quality (Figure 1) to determine quality checkpoints, including whether data quality depends on the context of the data or purpose for its use and whether defining data quality requires subject matter expertise.
- 7. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. The Four Levels of Healthcare Data Quality Figure 1: The Four Levels of Data Quality.
- 8. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. A Framework to Measure Quality Throughout the Data Pipeline Health systems that follow the Healthcare Data Quality Framework (Figure 2) will establish a data quality culture from the ground up and amass the requisite information to drive meaningful improvement, react to crises, and prepare for future emergencies.
- 9. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. A Framework to Measure Quality Throughout the Data Pipeline Figure 2: The Healthcare Data Quality Framework.
- 10. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. A Framework to Measure Quality Throughout the Data Pipeline Think of Data as a Product In the context of data quality, thinking of data as a product means that data results from a process or system that assesses and treats its quality throughout—similar to how a car progresses from raw materials to assembly line to a dealership to expert magazine review. To progress successfully through the automobile manufacturing, sales, and evaluation process, car makers need quality raw materials to take their vehicles from concept to the consumer. Source: Ford Motor Company
- 11. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. A Framework to Measure Quality Throughout the Data Pipeline Think of Data as a Product To ensure they are thinking of data as a product, data engineers can ask themselves the following questions: Have I defined user personas or data users representing people who will use the data now and in the future? Have I defined user stories or data use cases that describe a user completing a task specific to their goal? Have I defined unit tests or data quality assessments that assess whether the process is behaving as expected, and the data are fit for purpose? Have I deployed data quality assessments at the earliest appropriate point in the data pipeline? Have I identified and deployed user personas, user stories, and data quality assessments for the components of the data production process that are upstream of my data product? Have I documented each assessment in a transparent, accessible, and centralized place— allowing new, existing, and upstream/downstream data users to understand what users and use cases the data supports and how the organization defines and ensures data quality?
- 12. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. A Framework to Measure Quality Throughout the Data Pipeline Address Structural Data Quality First Health systems struggle to move to higher data quality levels if the data is not first structurally sound. The levels described above build on each other, and while content and utility assessments will expose structural issues, understanding the root cause is more efficient when leveraging specific structural assessments.
- 13. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. A Framework to Measure Quality Throughout the Data Pipeline Address Structural Data Quality First For instance, determining whether an encounter identifier is unique across encounters and not NULL promotes referential integrity. When a data user then leverages that identifier as a foreign key to link the flowsheet and encounter data together, the user can focus on assessing the quality of the content across these two subject areas, like whether the flow sheet recorded date is during the encounter.
- 14. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. A Framework to Measure Quality Throughout the Data Pipeline Define Content Level Data Quality with Subject Matter Experts Understanding data use cases is extremely important for defining and ensuring the quality of the data content because it requires subject matter expertise and can be context dependent. Potentially different from a data user, organizations must identify a data subject matter expert (SME) for defining content level data quality because that expert will understand the content.
- 15. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. A Framework to Measure Quality Throughout the Data Pipeline Define Content Level Data Quality with Subject Matter Experts The SME tailor definitions based on the context (e.g., the heart rate is appropriate given the patient’s age) to assess whether data quality is sufficient for the intended use cases.
- 16. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. A Framework to Measure Quality Throughout the Data Pipeline Create a Coalition Typically, organizations take a grassroots approach to data quality by addressing it within individual projects or department silos. However, creating a data quality coalition brings together organizational leaders, managers, subject matter experts, and analytics professionals—all with a vested and shared interest in ensuring data quality because it facilitates better decisions.
- 17. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. A Framework to Measure Quality Throughout the Data Pipeline Create a Coalition The team agrees on a standard approach to advance proven processes and avoid spending resources reinventing the wheel. The coalition must have support from leadership at the highest level for organizational alignment in terms of objectives and resources focused on the work.
- 18. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Building Data Quality from the Ground Up Fit-for-purpose quality data has established itself as a strategic imperative as health systems continue to navigate COVID-19 and prepare for an emergency- ready future. The only way to ensure healthcare organization leaders, managers, and providers have data fit for critical decision making is to establish quality at the beginning of the data life cycle and maintain it throughout all processes.
- 19. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Building Data Quality from the Ground Up A structured quality process, such as the Healthcare Data Quality Framework, engages technical and subject matter expertise to define, evaluate, and monitor data quality throughout the pipeline. As a result, health systems don’t just make data-informed decisions—they make quality data-informed decisions.
- 20. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. For more information: “This book is a fantastic piece of work” – Robert Lindeman MD, FAAP, Chief Physician Quality Officer
- 21. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. More about this topic Link to original article for a more in-depth discussion. How to Run Analytics for More Actionable, Timely Insights: A Healthcare Data Quality Framework Why Health Systems Must Use Data Science to Improve Outcomes Taylor Larsen, DOS Marts Data Quality, Director Smartsourcing Clinical Data Abstraction Improves Quality, Reduces Costs, and Optimizes Team Member Engagement Health Catalyst Success Stories Quality Data Is Essential for Doctors Concerned with Patient Engagement Ed Corbett, MD, Medical Officer Self-Service Data Tools Unlock Healthcare’s Most Valuable Asset Cessily Johnson, VP of Terminology & Master Data Management Michael Buck, Senior VP of Clinical Informatics Achieve Data-Informed Healthcare in Eight Steps Sam McCutcheon, Analytics Engineer
- 22. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Taylor joined Health Catalyst in December 2014 as a Data Architect. Prior to coming to Health Catalyst, he worked for the Colorado Department of Health Care Policy and Financing as a Budget and Data Analyst. Taylor has a Master’s degree in Economics from the University of Colorado Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Taylor Larsen
- 23. © 2020 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Health Catalyst is a mission-driven data warehousing, analytics and outcomes-improvement company that helps healthcare organizations of all sizes improve clinical, financial, and operational outcomes needed to improve population health and accountable care. Our proven enterprise data warehouse (EDW) and analytics platform helps improve quality, add efficiency and lower costs in support of more than 65 million patients for organizations ranging from the largest US health system to forward-thinking physician practices. Health Catalyst was recently named as the leader in the enterprise healthcare BI market in improvement by KLAS, and has received numerous best-place-to work awards including Modern Healthcare in 2013, 2014, and 2015, as well as other recognitions such as “Best Place to work for Millenials, and a “Best Perks for Women.”