11 week course: 12th September to 21st November 2023
This course will provide analysts with a comprehensive overview of the Population Health Management (PHM) cycle and the approaches taken at each step, including details of the analytical tools and methods which will allow you to carry out PHM in your area. The initial sessions will introduce the foundations of R programming and statistics, while later ones will focus on examining key methods and techniques for population segmentation, risk stratification, impactibility modelling and causal inference.
There will be lectures, practical activities, interactive sessions and useful examples, and plenty of opportunity for discussion and questions.
The course will cover the need to look beyond just ‘care’ and to look at ‘health’ and the wider determinants of health more broadly. It will also consider the need to work with additional stakeholders and the challenges that systems face when they have potentially conflicting goals and measures of success.
12th Sep 1-3
What is population health management and how can it help us deliver better health and care? This session offers an overview of what population health management is, its benefits, and methods used to analyse population health.
It will equip you with the ability to articulate and know when to apply population health management techniques.
19th Sep 1-3
Using real-world examples, this session will provide you with an understanding of the benefits of integrated care for population health.
We’ll take a look at how and when systems work well together and the common challenges and explore the tools which can support how system relationships can be measured.
26th Sep 1-3
R can be a powerful tool in population health management. This session uses real-world examples to give an understanding of how to use R in population health management and the fundamental building-blocks of R.
3rd Oct 1-3
Join our expert statisticians in understanding statistical metrics and how to apply these in R. In this session you will learn how to apply statistical testing in R and gain an appreciation of the use of statistics in modelling.
10th Oct 1-3
None of the technical language, all of the benefits! This session will give you an understanding of what segmentation is and the ability to interpret and evaluate segmentation analysis.
17th Oct 1-3
Using standard methods of performing segmentation, including k-means clustering, DBSCAN, and hierarchical clustering, we’ll be exploring what segmentation is and how it can be valuable in population health management.
24th Oct 1-3
Your straight-forward guide to the principles of risk stratification.
Join our expert team exploring how to interpret and evaluate risk stratification analysis.
31st Oct 1-3
Go beyond the principles and dive into an overview of modelling, including the formulation, implementation and validation of techniques, along with good practices.
7th Nov 1-3
What is impactibility and what does it mean for your population?
Join this session to find out and learn the ability to interpret and evaluate impactibility analysis.
14th Nov 1-3
How can causal inference help in PHM and what techniques are available?
This session teaches you about the role of causal inference in healthcare, and it includes an overview of the language and methods that are commonly used.
21st Nov 1-3
Once you’ve learned the principles and techniques of PHM, how can you tell if they’re working? This session looks at the need for evaluating PHM interventions and how you can measure their effectiveness in your area.
This course is designed for analysts and those who have an interest in population health management and R. Numeracy skills will be assumed for the technical sessions.
If you are looking for a less technical course – please sign up to Population Health Management, which covers the core curriculum but omits the programming and analytics sessions.
This course is free and available to all those working in the Midlands Health and Care sector , e.g. NHS, Public Health, Local Authority, ICBs etc
11 Sessions x 2 hours
1.00 – 3.00 pm
Sep to Nov 2023 Programme
Online – delivered via Teams
Registration is now closed.
Andi is a health economist with particular interest in population health analytics, especially addressing health inequalities and the concept of impactibility modelling. He is researching this for his PhD at Imperial College London. He is also a senior advisor for NHS England on population health management in the Operations and Information Directorate and works with STPs and ICSs across the country. Read Andi’s full biography.
David is an experienced computer scientist who has worked in several national, regional, ICS and provider teams in the NHS, contributing to the deploymentof machine learning techniques. 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. Read David’s full biography.
Heather has degrees in Biochemistry and Economics and a Master’s in Public Health, and is pursuing an MSc in Health Economics, Policy and Management. She is passionate about using data to address health economics and has developed courses on the subject. She has experience in qualitative and quantitative datasets, has worked within both the USA and UK health systems, and is particularly interested in population health and allocative efficiency in health. Read Heather’s full biography.
Jack is a health economist with experience in qualitative and quantitative
research, driven by the belief that a health economic approach will benefit the NHS and patients. He has worked in service management, commissioning and national policy, and is currently completing an MSc in Public Health at the London School of Hygiene and Tropical Medicine. He joined the Health Economics Unit from NHS England where he worked chiefly on COVID-19 vaccine delivery. Read Jack’s full biography.
Joseph has broad experience across both data science and healthcare.
He previously gained degrees from Edinburgh, Imperial, and Cambridge. He then completed his PhD thesis in machine learning and computational modelling at the University of Cambridge, before joining the HEU. Passionate about applying his extensive data science knowledge within healthcare systems, he strongly believes in the value of data-driven solutions to improve patient outcomes. Read Joe’s full biography.
Santosh is a dedicated researcher with significant experience in applying advanced machine learning techniques and natural language processing to solve data-driven problems. He is passionate about improving healthcare systems through applications of machine learning, and has used this technique to great success. A keen collaborator, Santosh is happiest when he can apply and extend his expertise in machine learning to provide greatest benefit to the population. Read Santosh’s full biography.
With a background in population health management, Sophie is passionate about providing better evidence to improve decision making in healthcare. She has a first-class degree in Economics with Econometrics (BSc) from the University of Kent and will soon complete a masters in Health Data Analytics (MSc) from UCL. Sophie also has experience in building communities of practice supporting analytical upskilling and developing system intelligence functions. Read Sophie’s full biography.
Will is a health economist with experience in cost-effectiveness analysis and health technology assessment processes, having led the development of numerous cost-effectiveness models, budget impact models, and other health economic models. He is interested in the application of NHS real-world data in health economic modelling, and in the adoption of R as a modelling software. Read William’s full biography.
Yihan Xu, senior health economist
Yihan is passionate about exploring and delivering smart and budget-friendly solutions that improve public health. She has extensive experience in examining and synthesising scientific data to inform and improve the delivery of government services in public health and education, and most recently, she collaborated on a project that enhanced the government’s COVID-19 response. Read Yihan’s full biography.
“Great training session today! The material was well-organized and easy to follow. The trainer was knowledgeable and provided clear explanations and examples. I particularly liked the hands-on activities and the opportunity to practice what we learned. Overall, I feel more confident in my understanding of the topic and I am looking forward to applying what I learned in the future. Thank you!”
“Clustering is somewhat technical and it would be impossible to cover it in great depth and ask people to have a go at exercises in 2 hours. The level of detail and mostly graphical representations was well chosen.”