V-BID in Action: A Profile of Connecticut’s Health Enhancement Program

Value-Based Insurance Design (V-BID) - hailed as a "game changer" by the National Coalition on Health Care - refers to insurance designs that cary consumer cost-sharing to distinguish between high-value and low-value health care services and providers. V-BID entails (1) reducing financial barriers that deter use of evidence-based services and high-performing providers, and (2) imposing disincentives to discourage use of low-value care. Through the incoporation of greater clinical nuance in benefit design, payers, purchasers, taxpayers, and consumers can attain more health for every dollar spent. The University of Michigan Center for V-Bid leads in research development, and advocacy for innovative health benefits plans and payment reform initatives.

Connecticut Seeks to Improve Health and Contain Costs

The state of Connecticut faced a projected budget gap of $3.8 billion in fiscal year 2012, and state employees were asked to help address the shortfall. The Governor's Office and a coalition of unions representing state employees met throughout 2011 to discuss a wide range of topics, including the health plan covering active and retired state employees. The parties focused health care discussions on possibilities for improving health as a means to control long-term costs. Discussions involving unions, the Governor's representatives, and the Connecticut Office of the State Comptroller led to the October 2011 lunach of an uncommonly innovative initiative - the Health Enhancement Program (HEP).

Given the new and central role of beneficiary accountability in this novel plan design, preliminary versions were carefully scrutinized and modified. Shortly after union ratification, open enrollment took place in October 2011. As designed and implemented, HEP incorporates clinically-nuanced elemnts of V-BID eliminating barriers to specified evidence-based clinical services based on beneficiary demographics and medical history. This brief highlights some of HEP's key features as well as early results and lessons learned.