A variety of factors influenced whether states incurred net administrative costs or savings as a result of ELE implementation. Per capita operational savings are realized to the extent that automation and data-sharing arrangements substitute for manual procedures, thereby reducing the resources required to process a single application or renewal. The extent of such savings depends on the number of cases that are processed more efficiently. Simply put, should a state gain operational savings through ELE, the more children it can process through the policy, the quicker it will recoup its up-front administrative investments.
For example, Louisiana’s highly automated ELE program was the most expensive to implement among all the states we examined, involving both programming costs and staff effort to define new policies. However, because the capture is so high—15,000 automated renewals per month—Louisiana offset the implementation costs we have been able to quantify within the first year ELE was in effect for renewals. At the same time, Iowa’s Medicaid ELE program, which is also partly automated and leads to an estimated 28 minutes in savings over processing an application through traditional means, is essentially cost-neutral to the state. This is because the savings in processing are offset by new mailing costs but more so because of low capture—only about 1,300 children are enrolled annually through Iowa Medicaid’s ELE methods. Automation did not result in any staffing reductions in the states we examined. However, staff were freed up to work on other projects or to process applications and renewals that came in through regular channels. More broadly, ELE helped states use their existing staff to handle an increased caseload.
For states considering automated ELE processes, the up-front cost of automation was lower in states with newer information technology systems in place, as expected. Louisiana and Iowa Medicaid were both using legacy information systems that date to the 1970s, resulting in significant programming costs to implement the automation needed for their respective ELE designs (State of Louisiana 2011; Iowa Department of Human Services 2012). In contrast, Oregon implemented a new MMIS in December 2008, and its programming costs of $1,600 to implement ELE reflect the greater simplicity of making changes to such a modern system. Moreover, states with newer information systems are likely to be able to execute the needed programming for ELE much faster, letting them operationalize ELE more rapidly. States with legacy systems should consider these factors when evaluating potential automation for ELE; however, as noted above, Louisiana realized considerable savings, despite its older system.
States also should carefully consider who will bear the administrative costs of ELE implementation. States may be able to find different funding sources to support the implementation of ELE—Louisiana’s implementation was costly, but most of the funds to support it came from a foundation grant. However, states looking to automate processes through ELE in the future may be able to take advantage of the Affordable Care Act’s 90 percent matching rate for eligibility and enrollment systems to help support the investment in automation (76 FR 21590). One source that none of the ELE states evaluated used was amending their third party administrator contracts to extract savings accruing from the new ELE processes, such as faster processing time for applications under ELE, although this could be considered in the future or by other states.
Finally, in determining the total cost impact of ELE, one must consider the cost of providing health care to newly enrolled or renewed children through Medicaid or CHIP along with potential administrative savings. This balance will vary based on each state’s circumstances and approach to ELE; for example, ELE renewals may involve a greater balance of administrative savings relative to health care costs. states should likewise consider the opportunity costs of implementing ELE: Louisiana and Iowa Medicaid, the states that invested the most in ELE programming and implemented the most automated ELE programs among the study states, both deferred work on other projects because of ELE, including work on other simplifications that might also have led to increased enrollment. Another factor to consider is that ELE can, in conjunction with other simplifications, qualify states for CHIPRA performance bonuses through federal fiscal year 2013 to help support the costs of new enrollment. Moreover, state administrators uniformly viewed ELE as a worthwhile strategy to support coverage expansion goals despite expenses (expected or not) they incurred.