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The Struggle for Patient Value

The focus for providers and payers is shifting in pursuit of better value for the money spent. As such, the concept of value-based healthcare (VBHC), tabled more than a decade ago, is now an official part of the conversation; albeit far from being a reality.

On the surface, it’s a devastatingly simple model centered on patient value. Where value increases by improving outcomes or, at the very least maintaining outcomes at a reduced cost.

Patient Value = Outcomes/Cost to achieve Outcomes

The central ethos here is that health and not the volume of treatment is the defining outcome for medicine. For the math to work, the value of health is defined in terms of survival years gained or some other relative net improvement. So to allow this to be measured, and therefore paid for, we need better measurements or some proxy for patient and health outcomes; this is far from simple.

It seems many of the obstacles standing in the way of doing VBHC at scale have to do with alignment on which outcomes to measure and how to measure them. This alignment needs to be present between the patient, provider, payer, and the industry. First steps have been taken in areas such as better individual patient-reported outcome measures (PROMs or ePROs). Increased focus on the patient and the uniqueness seems like what we should be going for here. But this can break down when you over personalize the outcomes to each type of condition or individual patient. There also remains the issue that you need to deploy the right care in the right place as well as coordinating care where multiple conditions are present to avoid undesirable health inequalities and unnecessary costs. But the system fails economically if we add complexity while simultaneously trying to improve precision. Given these constraints, we still need leadership to realize a unifying, common set of value criteria and outcomes.

Data helps here a lot. The growing volume of available health data, both structured and unstructured, and the maturation of policies and infrastructure to allow for the pooling of that data will give us a resource to try to arrive at this alignment about outcomes. Simultaneously, we now have better understood and more efficient machine learning capabilities able to determine and decode meaningful information from this data and that helps model future outcomes based on today’s information.