Population Health Management: Doing Causal Inference for Impactibility Assessments for Your Local Population (PHM-10)

Date: 8th November 2022, 35pm 

Population Health Management: Doing Causal Inference for Impactibility Assessments for Your Local Population
PHM-10

8th November 2022, 35pm 

In this online training session, population health management experts David Sgorbati, Santosh Kumar and Joe Lillington of the Health Economics Unit will teach the basics of causal inference concepts and techniques for population health management. 

We will explore the concept of causation and its role in healthcare management, and discuss how the relationship between variables can be framed. Common approaches to causal inference in machine learning will be explained during the session. 

Learning Outcomes

Attendees will leave with: 

  • An understanding of the role, language, and approaches of causal inference in healthcare. 

Prerequisites

It is recommended that attendees have a basic understanding of population health management and population segmentation to attend this session.
You may wish to attend:

This session is designed to give valuable insights for analysts; therefore basic numeracy skills are required. 

Agenda

  • Why do we need causal inference? 
  • What can causal inference tell us? 
  • What is the difference between association, correlation and causation? 
  • Which techniques allow us to estimate causation? 
David Sgobati

David Sgobati is an experienced computer scientist who has worked for several national, regional, ICS and provider teams in the NHS, contributing to the deployment of machine learning techniques to improve patient care.

In the first 10 years of his career, David worked for several machine learning, artificial intelligence and robotics start-ups, developing software for clients in a wide range of industries, including manufacturing, logistics, and marketing. Since joining the NHS in 2014, his focus has been on using advanced analytics techniques to support problem-solving across organisational boundaries; he is passionate about the use of data as a tool to foster better conversations and to generate actionable insights.

Joe Lillington

Joe Lillington. Joseph has broad experience across data science, physics, and engineering, having previously gained degrees from Edinburgh, Imperial, and Cambridge. He gained his PhD thesis in machine learning and computational modelling at the University of Cambridge, before joining the Health Economics Unit. 

Joseph enjoys supporting the NHS by applying the skills that he has gained in his research training. He is passionate about applying his data science knowledge within healthcare systems to improve patient outcomes. He also strongly believes in the value of using machine learning to support population health. 

Joseph is always keen to collaborate on projects and learn about the interesting work of others. 

Santosh Kumar

Santosh Kumar. Santosh is a dedicated machine learning researcher. He is experienced in applying advanced machine learning techniques and natural language processing to solve real-world data-driven problems. 

Passionate about improving healthcare systems through the application of machine learning explainability, privacy-preserving machine learning algorithms, and Artificial Intelligence (AI), Santosh has used these techniques to outstanding effect in a variety of cases. This includes: 

  • Extracting information from MRI/CT images for early breast cancer prediction 
  • Developing unemployment prediction tools by analysing extensive volumes of public sector data 

Santosh is always keen to collaborate and is happiest when he is able to apply and extend his expertise in machine learning to provide the greatest benefits to the population. 

 

Reading Materials

Pre-requisites

It is recommended that attendees have a basic understanding of population health management and population segmentation to attend this session.
You may wish to attend:

This session is designed to give valuable insights for analysts; therefore basic numeracy skills are required. 

Audience
This course is free and available to all those working in the Midlands Public Health and Social Care sector , e.g. NHS, Public Health, Local Authority, ICBs

Duration
2 hours

Location
Online – delivered via Teams

Date: 8th November 2022, 35pm  

Registration: Please use the online link below

Please see further Population Health Management sessions from the Health Economics Unit – available to book now!

For more information about this course, please contact:

Training & Development Operational Lead, Rachel Caswell