  A statistical description and model of individual healthcare expenditures in
the US has been developed for measuring value in healthcare. We find evidence
that healthcare expenditures are quantifiable as an infusion-diffusion process,
which can be thought of intuitively as a steady change in the intensity of
treatment superimposed on a random process reflecting variations in the
efficiency and effectiveness of treatment. The arithmetic mean represents the
net average annual cost of healthcare; and when multiplied by the arithmetic
standard deviation, which represents the effective risk, the result is a
measure of healthcare cost control. Policymakers, providers, payors, or
patients that decrease these parameters are generating value in healthcare. The
model has an average absolute prediction error of approximately 10-12% across
the range of expenditures which spans 6 orders of magnitude over a nearly
10-year period. For the top 1% of the population with the largest expenditures,
representing 20%-30% of total spending on healthcare, a power-law relationship
emerges. This relationship also applies to the most expensive medical
conditions in the US. A fundamental connection between healthcare expenditures
and mathematical finance is found by showing that the process healthcare
expenditures follow is similar to a widely used model for managing financial
assets, leading to the conclusion that a combination of these two fields may
yield useful results.
