Population Health Management — Empowering the Change Makers

At its simplest level Population Health Management (PHM) is a system that uses data and analytics to evaluate and improve the health of the population. The ultimate aim is the improvement of both clinical and financial outcomes. Data and analytics are vital enablers of any successful Population Health Management (PHM) programme, however ultimately it is people that make the change.

For the purposes of this article, I refer to these people as ‘change makers’ because change is essential; a different way of doing and a different way of thinking.

In order to genuinely deliver on the personalised care agenda then you need a precise understanding of the specific needs, wants and desires of the local populations to be served. The change agenda is about people not institutions. Whilst people ultimately deliver change, tools and processes can provide the evidence base and insights which fuel the engine of that change.

In this opinion piece I outline what I believe are some of the key steps in driving forward a successful Population Health Management strategy.

Know your population

The first step to improving population health is to analyse and understand the health status and needs of the population to be served. If you do not know the current health status of your local population, then it is impossible to know what services need to be in place in order to deliver on the needs of the individuals within those populations. Advanced analytics tells us that multi-morbidity is the norm and it is multi-morbidity — not age nor frailty (not in isolation anyway) — that drives demand and cost.

Population Health Management focuses on populations with a common need. Population segmentation, risk stratification and population profiling tools, such as Sollis Clarity, provide forensic analysis of key health and care data sets. Tools such as these deliver the evidence base upon which the change makers can weave their magic

Segment for impactability

When planning for place based care, harness the power of population segmentation.

The purpose of segmentation is to enable specific groups, or cohorts of patients to be identified and explored in more detail, and ideally to define groups of similar patients who may be suitable for a particular intervention or new care programme.

Population segmentation techniques identify the health and care needs within populations, enabling integrated care systems to tailor new care models to homogeneous patient groups. This type of analysis often supports and informs the establishment of capitated budgets and can even extend to the design of personal health budgets which lie at the heart of the personalised care agenda.

Providers are rewarded for working together to deliver services that meet the outcome requirements of the target populations. This in essence is the part of the WHY of Integrated Care Systems (ICS).

To set budgets and measure outcomes, change-makers need to understand the needs of the total population. Populations need to be segmented into impactable cohorts with budgets being set with a consideration to the morbidity burden of that population. Casemix analysis can help here, with capitated budgets being set for those population cohorts in greatest need.

Measuring outcomes is one method of assessing the value of changes to the co-ordination, integration and delivery of healthcare. Trend data for activity, costs and patient risk profiles, along with population profiling, can provide a valuable indication of the effectiveness of treatment patterns and service provision.

Insight eats data for breakfast

Data analytics should be applied at both a whole population level — segmented and risk profiled — and individual patient level. Population profiling and case finding are two sides of the same coin. Therefore the PHM technology platform through which insights are delivered must be proven and scalable, particularly where these solutions are centred on large scale data acquisition, processing and data management.

Technology and service offerings must be tailorable, and deployed as close to the patient as possible. This provides clinicians and other members of the multi-disciplinary team (MDT) the very best chance of transforming services.

Software and services that serve Population Health Management should be flexible enough to allow local experts to bring their experience and local knowledge to bear. In the world of Population Health Management data scientists will be to the fore. People generate insight not widgets alone.

For change makers insight is everything.

Share data #datasaveslives

On the journey towards a health and wellbeing system that incentivises collaboration and where the focus is on wellness and prevention as opposed to just fixing illness, we need to liberate the huge data assets that are within touching distance. Data provides insight and the underutilisation of those data assets — as is the case today — is nothing short of a national scandal. Patients and carers expect data to be shared and they are dumfounded when they learn it is not.

Data sharing and data analytics are critical to the implementation of Population Health Management strategy. With it, you have a deliverable strategy. Without it, you have empty rhetoric.

Engage patients and carers

Throughout this piece, I have framed the change makers almost exclusively in terms of service professionals already operating within the health and care system. Whilst these groups are of course critical, we should not forget that they exist as just one set of actors on the stage. The myriad of bodies that constitute the third sector have vital roles to play here and lest we forget, possibly the most powerful group of change makers out there are patients and carers themselves. In the end analysis, they are quite likely the ‘uber’ change makers. Empowering them with the tools and insight needed to affect change is perhaps our greatest challenge.

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