In the U.S., the Federal government is the largest single payer for health expenditures (36% according to this cms document (PDF)) from 2020.
It has long been a goal to reign in the growth rate in Federal spending on healthcare, as growth has been a multiple of the inflation rate.
Regardless of the merit of the premise, healthcare finance regulators operate under the assumption that fee for service incentivizes this excessive cost growth, and have been pushing a move to so-called “value based” healthcare, which really means shifting cost risk onto health service organizations for individual clinical decisions. The idea is to use “market incentives” to foster effective, cost efficient care, control “waste” and expand coverage. I alluded to the convergence of actuarial models with clinical prediction models in this thread:
My personal experience where the fusion of actuarial thinking and clinical thinking has great potential is in the accountable care model known as PACE (Programs for All-inclusive Care of the Elderly). Economically, a PACE program might be thought of as a managed care organization “owned” by the participants (a regulatory term for eligible individuals who choose to have health care provided by program) but paid for by a fixed fee per member by the government (a capitated managed care plan). There is no “profit” per se as these organizations are operated as non-profit charitable entities.
The organization must figure out how all participants individual needs can be met with the resources provided to it via the monthly fee it receives per member. There is some risk adjustment done by CMS that considers age, medical history, etc. in determining what the monthly premium should be.
These PACE programs service the most medically complex and at-risk populations by keeping those who might ordinarily need nursing home care, living in the community.
The front line clinicians in these settings are doing the best they can with very limited decision tools. In other settings (where cost reduction is directly related to profit) I worry these “AI” models might be used in a way to simply cut costs rigging the guidelines for care provision that might adversely effect those who are simply expensive to care for.
My attitude is that clinical prediction models should be explicit, and assessed like insurance ratemaking models. Of particular interest is the dialogue that goes on between regulators and the insurance companies regarding the inputs to a model. The NAIC (National Association of Insurance Commissioners) has a few free publications that describe how insurance regulation balances the need for economic efficiency across state borders with autonomy at the local level.
Here is a recent document on the principles behind the calculation of insurance rates from the Casualty Actuarial Society on predictive models (pdf). This is the rigorous way of examining predictive models by entities and experts who have what Taleb refers to as “skin in the game.”
A more detailed discussion is found here (pdf) Insurance Rating Variables: What They Are and Why They Matter.
An actuarial ratemaking model has both a probability (predictive) component and a cost (utility) component. We might evaluate clinical prediction models as a subset of a full actuarial model, based upon how accurate the probability estimate is, without full consideration of the economics of the situation.