The Affordable Care Act and NetLogo: An Agent-Based Framework for Modeling the Effects of Healthcare Policy

The goal of this project is to model the Affordable Care Act (ACA) from 2010 onwards, and design a model that would replicate consumer behavior in the insurance market. The goal of the ACA was to provide subsidies, public insurance, and penalizations to encourage U.S. citizens to enroll in insurance plans and increase the total amount of insured people in the U.S. We have data today that shows the ACA accomplished its goal, but this model allows to users to change the key policy metrics to see what aspects of the ACA could be improved on to further increase the number of sustainably insured people in the United States. The secondary goal of this model is to add further hypothetical policies to the modeled ACA and test whether those policy strategies might be productive in improving healthcare outcomes for individuals. 

The project methodology has three parts:

• Defining a rule set that replicates the economic behavior we have observed in reality 

• Parametrizing the key metrics of the model allows Policy Makers to test different policy designs and hypotheses

  • For example, what if you change the maximum age a person can legally stay with their parent's health insurance how will that impact the total number of insured and uninsured?

• Then create a second model which implements additional policies

  • For example, one aspect of the new model changes citizen behavior to compare the prices of procedures at different hospitals. After only choosing the hospital offering the lowest price the price transparency over time increases the cost efficiency of health outcomes significantly.

On the left-hand side, a user can adjust the parameters of the model. In the middle, there are graphs that show metrics like the amount of debt taken on by the citizens, the age distribution of the citizens, the profitability and assets under the management of the insurance companies, or the number of people using hospitals at any point in time. To the right is a full visualization of all the agents interacting in the environment. The white buildings are the hospitals, the grey tall buildings are the insurance companies, and the greek looking building is the government. The small specs at the bottom represent people and the color of the people represents what type of insurance they have. For example, dark blue signifies someone is covered through medicare while red signifies that the person is completely uninsured. Citizens go to hospitals under a probability defined by real scientific data, and the cost of going to the hospital is also calculated by the average costs of U.S. medical procedures. 

The research paper below explains what the model can be used for in detail, and what insights about health economics can we find regarding the U.S. healthcare policy. In conclusion, the most important findings are that the U.S.'s population growth is unsustainable and eventually will collapse the healthcare debt the country will need to take on. In other words, the number of working people in the U.S. will not be able to fund the amount of people that will age into needing Medicare so the U.S. will not be able to afford to give free healthcare to millions of elderly people. The second insight is that increased transparency of the prices for different procedures saves U.S. citizens a significant amount of money (billions) that compounds over the decades (compounding into trillions of dollars). The current system where people go to the first hospital they find and receive care without asking the price is broken.

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