I have worked for over 30 years in Healthcare, 20 of those in Healthcare IT and data analytics, and over that period I have seen a lot of ‘movements’ come and go.
As yet I am unsure whether Population Health Management (PHM) deserves to be described as a movement. What is undeniable is that it is currently all the rage — the new black if you like — with many IT vendors and consultancy gurus claiming expertise in the subject.
This is perhaps unsurprising given that PHM is inextricably linked to Accountable Care Systems (ACS) — the latest manifestation of integrated care systems — a concept that is beginning to take seed here in England and which is being promoted by many as a leading driver of healthcare reform internationally.
When thinking about PHM the first challenge is to agree a definition.
One of the better narratives that I have seen lately is this from the Institute for Healthcare Improvement (IHI).
Note the distinction made between population, population health and population management. I think this is helpful because in much of the literature on the subject these terms often become interchangeable.
The starting point for any ACS is an understanding of the population or sub-populations that the system is being designed to serve. A precise understanding here can only be achieved by looking at the data. You simply can’t do PHM without the data.
It is indisputable, I would argue, that any ACS needs access to advanced analytical capability and in the NHS at least these skills can be a scare resource. Martin Bardsley at the Health Foundation has written very eloquently on this.
If you are pursuing a PHM strategy it is essential that you run the numbers as a matter of routine and that your deployment of analytics — people and tools — is forensic in nature. Furthermore, you then need to acquire an intimate understanding of the meaning of those numbers. It is not the data per se that adds value, but the insights that are derived from that data.
Better insights will produce better outcomes.
If you are looking to bend the cost curve by focusing on the frail elderly alone then you are missing a trick.
Sollis’s work in this field has to date centred on the identification of relevant populations around which PHM programmes can be targeted. This means population segmentation or the stratification of populations into sub groups that require particular services at specific intervals.
Working with our healthcare clients, which include Vanguards and Sustainability and Transformation Partnerships (STPs), we have invested a huge amount of time running the numbers. When we do so some fascinating insights emerge.
For example, we have completed analysis across our client base analysing the relationship between frailty, multimorbidity and cost across the health economy. We mapped frailty across geography and looked at key drivers of cost and utilisation in primary and secondary care.
We have found that when the severity of frailty increases, costs and resource use rise. Frailty isn’t the only driver of high cost though. Adding multimorbidity into the analysis shows that patients who are multi morbid without any frailty marker can have a bigger impact on the health economy than frailty alone.
This is interesting I think for a number of reasons. For many embarking on their PHM journey a key challenge is knowing where to start. An obvious starting place might be the frail elderly, not least because NHS England — through the GP Contract — has given the frail elderly such high prominence. Furthermore, the general public are bombarded with the message that one of the reasons for the current NHS financial crisis is that it is, in part at least, the result of an ageing population.
Simple then. Target the elderly.
Except that from the analyses we have undertaken, if you are looking to bend the cost curve by focusing on the frail elderly alone then you are missing a trick. The relationship between the frail elderly, emergency admissions and high cost is not what you might think.
It is multimorbidity that is driving high costs and multimorbidity is not confined to the over 65s.
None of this is to say, of course, that PHM programmes should ignore the frail elderly. Of course not. It is just that in the world of PHM, where population segmentation is an essential early task, and when asking the entirely legitimate question, “where do I start?” it is advisable that you invest time and effort in the analytics up-front. Run the numbers. The answers you get might not necessarily chime with the answers you expected.
A successful PHM strategy of course involves a lot more than data. Ultimately, it is people that make PHM happen. It is health and care professionals acting upon the insights generated from the data that make PHM a reality. Equally importantly, it is activated and engaged populations and patients that make the difference.
So the data is only part of the story.
But it is a big part and you sure as hell can’t have a story without it.