Dr Justin Whatling, vice president population health at Cerner, explains the key to scaling population health management programmes.
Population health management as a concept has been talked about in the NHS for at least five years – but of course has always been the modus operandi of our publicly funded care system – and is accelerating as Integrated Care Systems are formalised and take root.
While significant progress has been made in certain areas, particularly over the past few months, the ongoing covid-19 crisis has also further exposed significant gaps, including the lack of data intelligence capability, that will need to be addressed in the long-term if we are to ever fully unlock the potential of these initiatives and the tools underpinning them. I take a look here at how we can scale population health management approaches and make them sustainable.
The NHS ICS Development programme is building skills and maturing ICSs, using data for insights, making changes and delivering outcomes. But more work is required in one-off analyses, and the ICSs are not yet all investing in the underlying data intelligence and architectures required for routine analysis and to build sustainable programmes. Programmes like Local Health and Care Record Exemplars are necessary to mobilise and sort out data, but they are not sufficient in this aim.
The accuracy of the insights that health and care professionals derive from PHM tools and the subsequent outcomes are highly dependent on the reliability and quality of the input data. Data quality will only improve once it gets used successfully and people see the benefits from it. As such, ICSs need to gear up for investing time in iteratively improving data quality as a never-ending task of shared responsibility.
Building repeatable approaches and methodologies
Addressing health inequalities is a key focus of PHM. To achieve this, local organisations, including local authorities, need to work collaboratively to address vulnerable groups and not just medically at-risk cohorts. This means segmentation and risk stratification models will need to be developed and built using a wider range of datasets that take into consideration social determinants of health such as income level, access to housing, employment status, educational opportunities, and so on. To scale PHM and make faster progress across the country, we will need to share the methods being developed and the learnings from them, regardless of the technology being used.
Such PHM techniques and models have immediate utility in managing pandemics such as covid-19. Areas like North Central London have used them to deliver support services for identified vulnerable populations, and are now using them to target flu vaccination programmes through equity audits that identify demographic or ethnic groups that present lower uptake, helping them adapt campaign communications accordingly.
Other examples include clients using data science to understand significant additional factors, like learning difficulties, that increase the risk of individuals requiring hospital admission if they should get a flu diagnosis. All this is generating valuable learning to apply to covid vaccination programmes to come.
Leveraging community assets
Communities usually know what their problems are, but they don’t always know how to address them or have the resources to do so. A way of enabling targeted interventions is establishing community engagement sessions to discover the root causes underpinning the issues being observed. This way, a comprehensive picture can be established, and solutions can then be co-developed at ground level to meet those unique needs across health, social care and wider community assets.
We have seen health systems around the world successfully apply this. The Healthy Nevada initiative in the U.S., for example, has seen 40 community partners come together to address improvement in health and wellbeing through over 100 community innovations. One of these innovations aimed address local dietary issues through a farm-to-table programme, getting the fresh food produced locally into schools and the community. Another innovation saw the training of judges in mental health issues, allowing them to preside over mental health cases and better take account of those needs when sentencing.
In England, as some localities responded to the first wave of covid-19, they also proved the success of this approach – with food insecurity having more than doubled during the pandemic, we have seen communities rise to the challenge to try and support those struggling. Most of these initiatives did not come directly from the government or government agencies, but from local authorities, charities, and voluntary organisations desperate to stop people from starving.
Implementing this at scale will require more technology architecture to lay down – routine social determinants capture and data integration, establishing directories of all the services, referral services between health and community settings, all set against a comprehensive citizen-centric plan. for a citizen that they orchestrate and not us.
Addressing health inequalities: a pressing challenge
For me, the topic of health inequalities is increasingly feeling akin to what we are seeing with climate change – we see the challenges and then 10 years later with massive deterioration we debate why we didn’t achieve more. Let’s not let the next 10 years play out this way – we have a great opportunity here in the UK with our mature public sector health and welfare services coupled with our direction for integrated care.
The challenge I’d like to pose is for us to stop thinking of our heath and care system challenges as a demand-side problem if only we had enough resource, and instead open our eyes to the supply-side opportunities making the best use of the community assets and services out there for jointly tackling the ever worsening challenge of health inequalities. For this to be successful, we must take the learnings of the pandemic and redress our long-term priorities.