Bridging the divide between population health analytics and the care management workforce

In the field of Population Health Management, it is increasingly understood that when thinking about patients we need to consider the whole person and not just the disease. Patients are not defined by their clinical condition alone.

At Sollis our Population Health Management endeavours are continuously guided by the work of Professor Barbara Starfield[1] . It was she in her seminal paper “The hidden inequity in health care”[2] who argued that disease orientated views of patients were outdated and that in the field of population health, our focus should be “whole-patient orientated”.

As part of her argument, she cites the clinician Sir William Osler who stated that, it was more important to know “what sort of patient has a disease than to know what sort of disease a patient has”.

Professor Starfield’s call to arms was centred around the need for medical care to be moved to where it was needed most; that is towards the care of patients and populations and not the care of diseases per se.

To this end I argue that when thinking about interventions that support at need populations then care management services need to be whole person based.

By this I mean that at the heart of Population Health Management endeavours there is a need to identify and addresses the root causes of behaviours that lead to a deterioration in health status. These root causes – often related to the social determinants of health – are often undiagnosed or under diagnosed behavioural or substance use issues.

For certain population groups – for example socially and psychologically vulnerable groups – there may be no underlying medical condition or at the very least there may be a limited presence of such conditions. Here the issues may be social in nature described in terms of the need for food support, housing, or transport needs. There may be mental health issues that makes accessing services or social support harder. In such cases the challenge for the workforce is often the need to identify the barriers to self-efficacy.

If we accept that a disease orientated view of populations is outdated, then it follows that the care management services that we need to wrap around people should not be the sole preserve of clinicians. Indeed, given the continuing pressures on primary care services here in the UK, if we are looking for GPs alone to deliver on Population Health Management then we will fail; not because there is not the energy, vision and desire to succeed, but because clinicians by their own admission are not always best qualified to deliver the care management programmes required.

This is why we believe that the Additional Roles Reimbursement Scheme (ARRS) offers such hope and potential for the delivery of Population Health Management. I say potential because nothing is a given here.

If we accept that care management services need to be crafted around a whole-person model of care, then ensuring the right workforce is put in the right place is central to success or failure.

The root causes of which I speak are often best addressed by a combination of health & well-being coaches, social prescribers, and care coordinators. Furthermore, If Covid has taught us anything then surely it tells us that we ignore the power of wider community/neighbourhood assets at our peril. The ARRS workforce may deliver some of the bench strength and skill set that is required, but on its own it may not be enough.

In summary nothing gets done without the right workforce. The challenge for health analytics companies like my own is to ensure that the data and more importantly the insight delivered from our endeavours is delivered safely and securely into the hands of the workforce who needs it most. This means the delivery of robust analytics that drive the precision targeting and identification of those members of the population who might best benefit from a care management intervention. This means the application of data science which assists in the identification of the most impactable medical, health, social, environmental, and behavioural risk factors for programme outreach.

In summary our task is to make sense of the data and then through a process of workflow automation deliver to the workforce who then delivers for the patient/citizen.

The road to hell is often (if not always) paved with data dashboards. The true data and analytics challenge is to deliver the insight – through algorithms and evidence-based business rules – into the hands of the workforce who best qualified to act on that insight. This is end to end Population Health Management.

This is as much a workflow challenge as anything else and in the thousands and thousands of words I have seen written about Population Health Management, it is often a neglected and often unspoken area.

In the final analysis the success or failure of Population Health Management rests with the workforce. This workforce will take on many hues. It will be focused; it will be collaborative, and it will be unencumbered by a purely medical model of intervention.

It is the duty of analytics, Artificial Intelligence (AI) and data science experts to put into the hands of the workforce the insight(s) by which they might weave their magic. Done correctly it is a job that has the potential to change people’s lives.

Purveyors of data dashboards alone need not apply.