Tuesday, September 1, 2026

A Simple Model of the US Healthcare System

 



In Pasdirtz (2007) I published a State-Space "covering model" for the US Healthcare system and generated Policy Wedges for areas of the system that needed control (see Blog Roll: Healthcare for more information). In this post, I'll present a simpler model (six variables based on the Kaya Identity, see below) that might prove useful for further policy analysis.


Starting with the basic Kaya Identity (above) where N=Population, L=Labor, Q=Production, P=Prices and K=Capital.




Adding in the Healthcare Sector (terms defined below) we have a simplified Healthcare Systems model.

Kaya Identity models are easy to understand but incomplete because all the possible feedback loops in the system are never clear. The State-Space Measurement model (computed with Principal Components--see below) has three components: 


Notes


Readings



USL20HC Measurement Model





The State Space of the simplified Healthcare model has three components that explain 99.8% of the variation in the indicators. HC1 = (Growth - Hospitals, HC2 = (Population Insured + Labor in HealthCare + Hospitals) and HC3 = (Growth - Prices). What the three component state variables show is that the major feedback loops in the system center around Hospitals. Another way to say that is that Hospital growth controls the system and are essentially dependent on the Population of Insured patients.

Notice, in HC2, that Hospitals peaked in 1975 and hospital consolidation increased centralization. And, HC3 peaked in 1990. Although there have been structural changes in US Healthcare, this simplified system is still unstable (as are the other models with input from either the World System, WL20, or the US, USL20. 




USL20HC AIC Statistics



Notice that all the models are unstable and would require stabilize to control the system. The best model is alignment with the World System, the WL20 model.

USL20HC BAU Model
















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