Foresight is a healthcare testbed that can help policy-makers better understand the many challenges facing the American healthcare system and model the potential equity impacts of policy options on individual and community health outcomes.
The main objective of this model is to inform the design of healthcare policy for the purposes of improving measures of health. Foresight can help assess the impact of policies on costs, health outcomes, and health disparities across different populations and identify where inequitable outcomes may arise.
Foresight helps decisionmakers understand how policy changes can unintentionally increase disparities among different ethnic and income groups in the U.S. The model considers community members’ lived experiences and can provide insights about unintended consequences that may otherwise go unnoticed in other modeling approaches.
This simulation illustrates how changes in healthcare policy might lead to disparate impacts across different communities. Specially, it shows how a decrease in the average level of controlled diabetes--a viable proxy for overall population health--does not necessarily equate to an increase in population health across all communities. In fact, a reduction in the diabetes metrics may hide the fact that there are growing disparities between different populations (White vs. Black Americans, low vs. high income, low vs. high education) and that some groups are actually in worse health. This simulation also highlights the hidden nature of key parts of the healthcare system, such as the availability and cost of health insurance.
By simulating the heterogeneous actors in the health system – patients, providers and payers – this model shows how changes in health policy may impact the health of individuals in a variety of communities within the US. This enables decisionmakers to compare how communities may be impacted by a given policy. Further, the tool can create insights into how a given policy could be strengthened to achieve its intended outcome for all communities. Here is a sample list of questions that Foresight can help address: What will a health policy cost? How can it be implemented to maxime benefits and minimze costs? How many people will be impacted by the policy? Does ‘coverage’ differ by population? Which communities benefit from the policy and which end up worse off?
Foresight makes it easy for users to alter different variables associated with policy alternatives. For example, one can give consumers different choices and adjust the scope of benefits up or down, correlate benefits across multiple social services, and model trade-offs across multiple dimensions of cost and benefit at the population level.
