Population Health Management

6 session course: 16th January – 30th March 2023

Population Health Management

6 session course: 16th January – 30th March 2023

This course will provide attendees with an understanding of the key concepts underlying the Population Health Management (PHM) cycle and the approaches taken at each step, without descending into complex technical details.

There will be practical activities, interactive sessions and useful examples, with 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.

Learning Outcomes

  • An understanding of what the PHM cycle is and of how PHM thinking and methods can benefit your population.
  • The ability to articulate and know when to apply PHM analytical techniques.
  • The ability to interpret and evaluate segmentation, risk stratification, impactibility modelling and causal inference.
  • An understanding of the underlying need for health systems, the challenges that systems face working together, and potential ways to address those challenges.

Audience and prerequisites

You don’t need to have attended previous training sessions to understand this course.

This course is designed to give valuable insights for employees across the system, whether you’re analytical, management or clinical.

Although participants of the course will not be analysing data themselves, they will leave with a good understanding of the analytical techniques, the pitfalls and biases of these approaches and be better able to interpret and use the results of these kind of analyses.

If you are looking for an analytical course – please sign up to Population Health Management for Analysts, which follows the same curriculum as Population Health Management but adds 5 sessions dedicated to programming and statistical techniques

Sessions

Introduction to PHM

16th Jan 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

Understanding population segmentation for your local population

7th Feb 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.

How systems work together

15th Feb 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.

Understanding risk prediction and stratification for your local population

2nd Mar 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.

Understanding impactibility for your local population

14th Mar 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

Closing the PHM cycle: An introduction to Evaluation

30th Mar 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.

Audience

This course is free and available to all those working in health and care, e.g. NHS, public health, local authority, ICBs

Duration
2 hours
1.00 – 3.00 pm

Dates

  • 16/01/23
  • 07/02/23
  • 15/02/23
  • 02/03/23
  • 14/03/23
  • 30/03/23

Location
Online – delivered via Teams

Registration now closed.

About the trainers

Andi Orlowski, director

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 Sgorbati, chief analyst

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 Humphreys, health economist

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 Ettinger, senior health economist

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 Lillington, senior data scientist

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 Kumar, lead data scientist

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.

 

Sophie Hodges, lead client service manager

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.

 

William Rawlinson, senior health economist

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.

 

For more information about this course, please contact:

Training & Development Operational Lead, Rachel Caswell