Taylor Larsen is the Sr. Director of Data Quality and Operations for the Data Business Unit at Health Catalyst where his team is building solutions that leverage data science, cross-client comparisons, and embedded subject matter expertise to assess, demonstrate, improve, and monitor healthcare data quality and processing performance to facilitate better and more reliable data-informed decision making.
Taylor is operationalizing a vision for data quality at Health Catalyst that systematizes a shift from structural and content-focused data quality to data quality defined by the measurable utility and insists data quality improvement is prioritized within the context of outcomes improvement governance. Taylor earned his master’s degree in economics from the University of Colorado and his professional experience includes time with Health Catalyst’s Data Science team and at Colorado Medicaid, which provides a unique perspective to draw from in his data quality work.
Healthcare organizations increasingly rely on data to inform strategic decisions. This growing dependence makes ensuring data across the organization is fit for purpose more critical than ever. Decision-making challenges associated with pandemic-driven urgency, variety of data, and lack of resources have further highlighted the critical importance of healthcare data quality and prompted more focus and investment. However, many data quality initiatives are too narrow in focus and reactive in nature or take longer than expected to demonstrate value. In this session, you’ll learn actionable ways to help your organization guard against the data quality challenges uncovered this past year and be better prepared to respond in the future.
Participants will learn:
- How data profiling and data quality assessments can increase data quality transparency, expedite root cause analysis, and close data quality monitoring gaps.
- How to leverage AI to reduce data quality monitoring configuration and maintenance time and improve accuracy.
- How defining data quality based on its measurable utility can provide a scalable way to ensure data are fit for purpose and avoid cost outstripping return.