A regular series of open invitation talks from leading voices in data science, AI, healthcare and those with lived experience dealing with multiple long-term conditions (MLTC). This seminar series is part of the AI for multiple long-term conditions: Research Support Facility (RSF) project
Event title: Using routinely collected electronic healthcare record data to study respiratory disease
Speaker: Professor Jennifer Quint, Professor of Respiratory Epidemiology, Imperial College London
About the event
Routinely collected electronic healthcare record data is becoming increasingly commonplace. Its importance is also increasing in clinical practice and for understanding disease trends, informing policy and in the planning and allocation of resources. However, any data set is only as good as what was entered. Professor Jennifer Quint has used routinely collected electronic healthcare record data to study several respiratory diseases, including most recently COVID-19, as well as working to maximising the quality, linkage and usage of these data for clinical and research purposes.
In her talk, Professor Quint will give examples of the value and importance of these data. She will also explore how we use this data in the UK to make national-level decisions.
Everyone is welcome at the session. However, the target audience is clinicians, policymakers and health data science researchers. We hope that attendees will learn more about developing and publishing code within secure research environments and how this learning can be applied to multiple long-term conditions research.
By registering for the event, you are agreeing to the events code of conduct: Events code of conduct The Alan Turing Institute.
After registering, you will receive a confirmation email containing information about joining the meeting.
Header image: Accuray, Unsplash