Kimberley Hill | DATA ANALYTICS FOR HEALTH CARE SUMMIT

Kimberley is a data and analytics governance enthusiast with a track record of leading large-scale data governance initiatives. She brings more than 20 years of experience in the healthcare system to her role as the Director, Data Governance& Analytics at Grand River Hospital. She is responsible for the development and execution of an integrated enterprise-wide data & analytics governance framework, as well as leading the Decision Support team. Previously, Kimberley was the Director of Information Governance at eHealth Saskatchewan where she initiated the establishment of enterprise data governance practices and supported her team’s efforts in data capture and reporting for several provincial strategic priorities. Kimberley has a Masters of Public Policy (MPP) with a focus in Health Systems Research. She is a passionate individual who believes that data and analytics are a key to better outcomes, and more importantly better health for citizens in our communities.

All Sessions by Kimberley Hill

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Day 1: Dec 2, 2020

Day 2: Dec 3, 2020

11:30 am

Panel: Measurement, Governance and Standardization

Lessons Learned in Setting Measurement, Governance and Standardization Practices Pre and Post Covid-19

How to measure, govern, and deploy data standards across the healthcare industry is a perennial challenge. This has also been challenged by the vast number of new data sets captured since Covid-19. But organizations are racing to establish measuring and reporting criteria on a variety of fronts, including on vaccinations. Join the discussion as we address:

· The importance of measuring and standardizing data to improve decision making

· What is the status of data standardization in the healthcare industry

· How Covid-19 affected data measurement and its implication for standardization and governance

· What we have learned over the last year, and where we see changes going forward

Gain a better understanding of the current and prospects of a measurement, governance and data standardization.