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population health Public Health

Building a population health management system

I thought I’d pretty much all I know on on “population health management” – see the tagged blogs. I thought I’d run out of things to say but then someone asked me an interesting question that made me think. The question was along the lines of “ahhh this is all very interesting and useful but what so I have to “do”.”

In my view it is critical that this work around population health management gets beyond the space of “nice to do analytics” and pushes into linking intelligence with operations (one of the key themes of Team of Teams – read about it)

Thus it is critically important this stream doesn’t over focus on analytic stuff but gets into service models for multi morbidity (accepting there are some tricky overlaps between frailty, functional impairment, multi morbidity)

I’d even go beyond service models (accepting that there has to be some “service” about it, but get into risk management models for stratified pops

The notion of prevent / delay is all in this space….. we under play the prevent on a grand scale etc

We DO, however, need an analytic basis, and organisational capacity & capability for this, but what use is it

Here are some extra thoughts on the analytic bit (having said I worry we over focus on it…). I’ve written a lot on this already – see previously tagged blogs..see especially five capabilities and revisiting segmentation.

Obviously some form of analytic capability and capacity is needed. This is often based on the sort of stuff SYMPHONY project have done, similar in Bradford, Sheffield, I’ve often seen many of the consultancies churning out the same – mostly based on the same mould. Mostly it’s excellent stuff.

  • It’s not useful for big organisational stuff in the guise and mould our organisations are currently set up.
  • It’s use is population health management, and clinical stuff management stuff. Organising around needs rather than costs, and ensuring we are arranging our response around needs rather than current service delivery priorities.
  • It’s also useful for changing cultures / hearts and minds etc. Getting us all to think outside the organisational & service boxes how we organise our world and in a population focused way.
  • mostly it’s using using data in risk stratification systems – to provide proactive care to cohorts, segmented by some method. There are a number of tools some commercially available, some open source
  • This sort of data set and capability needs some maintaining and maybe some investment and certainly sorting the upstream data architecture.
  • Often mental health and primary care and social care remains a black box data wise – again needs sorting
  • Thus focus of analysis must be to support and enable the management of risk across the whole pop – primary, sec and tertiary prevention

In terms of skill sets needed

Building the analytic basis for this requires

  • the ability to link data on need, service use and outcomes across multiple different sources of data.
  • a requirement for a deep understanding of Information Governance,
  • the strengths and uses of different data sources,
  • the background IT architecture,
  • how the data is put together at source from frontline care processes.
  • Any system will need to capability, and capacity, to do this in an ongoing manner.
  • Then there is the question of linking intelligence with service design and implementation of interventions. A different set of competencies altogether.

The skillset needed is very unlikely to be found in one person.

  • the PH curriculum describes a good set of the skill sets needed – esp around epidemiological underpinnings, analytic, evaluative and evidence base
  • There’s a bunch of IT skillsets needed for the background data architecture
  • Then a bunch of service planning skill sets needed for specification of service models and working out how to arrange the delivery to meet the specification
  • Then a bunch of leadership skills needed in terms of hearts and minds of those who we need to deliver + move on from current paradigms
  • Clinical skills – generalist – and experience needed in terms of pulling all together into a coherent whole in a way that will fly in the real world. Ash Paul talks about a clinically skilled manager and a managerially skilled clinician. He’s got a good analogy there

You should understand the strengths and limitations of the data that feeds your analysis

  • Go spend some time with the clinical and social care teams that deliver the care processes on which the data is built
  • Go spend some time in the coding department
  • Go spend some time in a team doing the analysis
  • Get to be black belt in all this. Or at least yellow belt.

Nuff said?

You should understand the weaknesses of this sort of cohort analysis

The other big weakness of the interpretation of the data in the mould it is currently crunched is that it assumes the population is static. Don’t underestimate the fluidity of the cohort over time.

There’s a lot of fluidity in who is “in” the top x% over time – from memory of those in the top 5% say only 25% of them are in the top 5% consistently over longish time period. Critically this is a factor in the limited predictive value of these models – they are cohort analysis and ecological fallacy when we try to apply to individuals.

As Rupert Dunbar Rees of outcome based healthcare says it is “vital is to operate a dynamic model where we can measure flows between the relevant segments, and how these vary on a day-to-day basis (and what population characteristics are driving the flows). Static, descriptive only, models are next to useless, as you can’t really measure the prevent/delay dimension”. You need to be able to do this stuff continually as a process one as a one off static. He is right.

THE critical issue in population health management is prevent and delay progression up the risk triangle. Assuming the population is fixed is obviously hopeless. On the things that really make a difference to outcomes in population health I’d encourage you to not stray too far from risk factors, both downstream and upstream

This sort of analysis tends to draw the mind to the top 5% that are heavy users, and draw conclusions of if only we could sort that we would be champion. This is wrong on so many fronts 1) see the conclusions of Barnett MM study 2) see the uber classic from Roland and Abel, 3) accumulated evidence over a few decades says a sole focus on top x% will not achieve the big net population changes  hoped for. If a whole population shift is needed, then a whole pop approach needed. Aim must be to prevent and delay – we’ve now the data in Sheffield to prove this is the only viable option in the long term.

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