Data Mapping

Just as innovations in science are crucial to improving health care outcomes, so are the resource allocation decisions that determine the most cost-effective course of treatment. At the intersection of these ethical and economic questions, analysts have been focusing on improving ways to measure health care outcomes to influence better public and private policy decisions.

The basic problem facing the field revolves around how to value various care options, as well as how to structure pro-health incentives outside of care; policy makers must structure an insurance system that provides for broad coverage without creating moral hazard, or conditions which might lead to sub-optimal consumer behavior. As a result, the problems facing health care economists are quite difficult, especially in the context of the various interest groups in the public policy sector. Modern health care outcome models are based upon consumers as both implicit producers, and, indirect consumers, of health. In this sense, health is a type of human capital, which can be augmented based upon care, education and proper decisions, parallel to the role that education plays.

Where health analysts commonly disagree is how to find the “optimal” level of health care – while early models suggested the optimal health investment occurs where marginal benefit equals marginal costs, measuring these variables (objectively) has proved to be difficult. Additionally, the public-private nature of the industry has made it difficult for policy makers to separate ethical and normative considerations from more objective measures. One of the most difficult questions is how to pool risk without denying the benefits of private coverage – the costs of treating uninsured patients has increasingly fallen upon government and private institutions, which are forced to pass on these costs to “healthy” patients. By pooling large groups of individuals together, policy makers are creating models that reduce overall risk and incentivize preventative care.