Identifying which low-probability harm events to target first requires a careful mix of clinical judgment and statistical thinking. In this learning lab, you’ll learn how to assess different risk-stratification models like Five Star and proprietary vendor risk scores, and how to calculate your own relative-risk scores to prioritize improvement opportunities. Additional tools like the Number Needed to Harm metric can help you calculate the likely number of people injured by failure to act on a known clinical deficiency, so you can propose an improvement project that clearly communicates the human cost of inaction. This learning lab will engage learners by utilizing interactive polling and whiteboarding. A copy of presentation is included. Presenter: Jason Gilllikin, CPHQ
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Credit CPHQ CE:1.0
Tag(s) On-Demand, Performance & Process Improvement; Health Data Analytics
Publisher NAHQ
Learning Objectives:
- Describe how to replicate the relative-risk equation in non-epidemiological contexts
- Summarize the ability to calculate the number needed to harm metric
- Identify how to integrate existing risk measures (e.g., CMS Five-Star, proprietary risk models) into a prioritized framework for improvement
- Identify strategies for communicating probable harm to justify a performance-improvement initiative
Presenter Biography:
Jason
Gillikin is a clinical analytics manager for Priority Health, the
health-insurance arm of Spectrum Health System. In this role, Jason leads the
research and development of data-driven QI strategies to improve outcomes and
reduce costs for Priority Health’s 600,000 covered lives. He has a degree in
moral philosophy and quantitative political science from Western Michigan
University and has been a proud CPHQ certificant since 2006. Jason co-led the
development of NAHQ’s health data analytics competency and currently serves as
chairman of the recognition of the profession commission.
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