Know your audience

As the NHS celebrates its 70th anniversary and looks to the future of health care, a Senior Population Health Analytics Advisor at NHS England and Head of Business Intelligence at Imperial College Health Partners (ICHP), explains how the ICHP’s advanced work on population health helps partners truly understand their local communities:

Understanding a local population is crucial for anyone working in an integrated care system, planning new interventions or new care models, or looking for the best ways to close the gap on health inequalities – and that’s where population health analysis comes in.

There are several types of ‘standard’ analysis that people expect to see – such as risk stratification and segmentation, which help give a detailed picture of the health and wellbeing of communities and the challenges that different groups face – but ICHP also offers some added ‘extras’ that build on these including, maybe most importantly, impactibility analyses.

The idea of these impactibility analyses is to look in more detail at the ways in which services engage or work with local people, to find any gaps in care or unwarranted variations that need to be addressed, and to investigate the most effective practical changes that could really make a difference.

Most of our support in this area is delivered to sustainability and transformation partnerships and integrated care systems, which have a mandate to be proactive about population health. As a first step, we use risk stratification and segmentation to help them better understand their local population, then we move on to the impactibility analyses, where we investigate which segments highlighted in the previous analysis are most amenable to change.

Identifying which patients are most likely to be amenable to intervention is a key step. Gap analysis enables us to identify and focus on patient care that may have missing elements or ‘gaps’ in care delivery.

For example, we could use gap analysis to look at the diabetes treatment pathway, using NICE as the example of best practice (or another pathway chosen by the locality). The first step on the pathway may be education followed by dietary advice and then blood glucose management with insulin-based treatments. We look for individuals who may not have followed that pathway and identify where it may be appropriate to offer them the missing intervention.

Understanding the variation in the population and clinical and social care outcomes is also important. Where there are differences, we work with our partners to understand why. To do this we can compare the data with peer-matched populations defined by Public Health England, NHS Rightcare or locally, to better understand the variation. We can also use statistical process controls (SPC) and historical population data to see whether changes really can have influence.

Predictive analytics or risk scores also play a role to in helping to predict individuals who are at high risk of developing disease or suffering an adverse event. We can also look for patients that have received duplication in their care, such as undergoing multiple tests for the same condition within a set period of time or being given medication that could be rationalised or improved.

We can then turn our attention to the proposed intervention to ensure that it is appropriate for the population highlighted in the outlined opportunity. A literature and case study review can determine whether the intervention was used previously in a similar population. If it was, we look at the outputs and outcomes observed and therefore understand what we could expect to see. The review can also help us understand how the intervention might translate to the identified target population.

By understanding the potential outputs and outcomes we can support the implementation of a new intervention and carry out a series of evaluations to check whether the change being made is making a difference. We also carry out a health equality impact assessment to ensure that we’re not inadvertently reinforcing health inequalities in a community.

With some issues in population health, changes could be a decade in the coming, so in these cases we can use what we call ‘lead markers’.

For example, if avoiding a stroke is the outcome, we can use the provision of anticoagulation medication to patients – a preventative treatment – as a surrogate lead marker for the outcome (a reduction in strokes). This means we’re not measuring an actual reduction in strokes, but we are measuring the steps we’re taking to deliver that outcome. It’s a bit like counting each mile as an achievement as you run a marathon.

To do this we create a learning health system or a PDSA cycle (plan, do, study, act) and continually iterate the intervention to ensure it’s going the way it should be to achieve the right outcome.

Population health doesn’t change overnight, and I personally thrive on delivering support that helps organisations work over time to deliver a positive patient impact and long term improvements to health and wellbeing.

  • Find out more about this work by visiting the NHS England stand at the LGA Conference where Andi will be on Wednesday 4 July at 8.15am to discuss how to use data to better understand the health and wellbeing needs of your local population. There will also be an NHS Big7Tea at the conference.
Andi Orlowski

Andrzei (Andi) Orlowski is a Senior Population Health Analytics Advisor at NHS England. In this role Andi works with the new care models team to support vanguards and pioneers in their plans to deliver care by providing advice through the Population Health Analytics Network.

Andi is also the Head of Business Intelligence at Imperial College Health Partners where his work ranges from uncovering health inequalities and opportunities to improve NHS services, to helping SMEs and larger healthcare private companies better understand their market and how to best access it.

Through his work Andi collaborates across the AHSN network and works closely with Public Health England. Andi endeavours to create equitable, timely and easily interpretable access to these data sets to the broadest group of stakeholders as possible.

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