How to Win Over Data Doubters by Answering Common Data Questions and Refocusing on What Matters
Stephanie Wells MSN, RN, CPXP, PCCN | Director, Patient Experience, Methodist Health System
Kyndall White, CPXP | Project Leader, Organizational Effectiveness, Methodist Health System
August 13, 2019 - 2:00-3:00 PM ET
For patient experience data to serve as a foundation for improvement, healthcare staff must trust the data. By candidly addressing questions of bias, reliability, validity and accuracy, health systems can build their capacity for improvement and empower patient experience champions to translate data into action. Methodist Health System has committed itself to establishing data management and use practices that eliminate data questions from staff and leaders so that resources are devoted to innovative improvement efforts. This webinar will share how Methodist Health System leverages training sessions, meeting cadences, and dashboards to support staff and leaders in understanding their data and positively impacting their patients’ experiences.
- Define bias in the context of patient experience surveys and describe ways to account for bias in survey result interpretation.
- Identify areas of concern for reliability, validity, and accuracy of patient experience data and best practices in data management to address these concerns.
- Summarize ways to utilize the Data-Information-Knowledge-Wisdom Hierarchy as a lens for acting on patient experience data findings.
*This webinar is an encore presentation from the Patient Experience Conference 2019.
Learn More about Methodist Health System:
Methodist Health System has been improving and saving lives through compassionate, quality healthcare for more than 90 years. Ten North Texas hospitals proudly carry the Methodist name, either by ownership or through affiliation. The system currently has more than 25 Methodist Family Health Care centers and Methodist Urgent Care centers, 1,737 licensed hospital beds, more than 8,500 employees, and works with more 1,500 physicians through a variety of relationship models.