Karen Way, Health Plan Analytics & Consulting Practice Lead, NTT DATA Services
A 2018 study published in the Journal of the American Medical Association suggests that the high cost of healthcare in the U.S. (which is double the per capita cost of other developed countries) is not a case of overuse, as many believe. Americans don’t go to the doctor or the hospital more often than those from other developed countries. The study showed there are other factors that drive the higher costs, including high administrative expenses and the high price tag of many treatments in the U.S.
Health plans routinely focus on medical cost management, because it’s important to their bottom line. The Affordable Care Act mandated that plans spend 80% of premium revenues on health services to their members. Plans strive to keep this figure, known as the medical loss ratio (MLR), as close to 80% as possible. If the ratio is too low they must issue rebates to their enrolled members, while if the ratio goes too high, their profit margin is reduced.
It becomes a delicate balance for a health plan to monitor and maintain the appropriate MLR to remain competitive in today’s healthcare market. Health plans need to be even more diligent in finding ways to manage medical costs; if costs can be reduced, plans can lower premiums to make their benefit offerings more attractive. This, in turn, increases competition and assists in driving down the cost of medical care in the U.S.
Using analytics to identify root cause, resulting in cost optimization
As noted earlier, administrative overhead costs are a key component of the rising cost of healthcare in the U.S. One way to help reduce administrative overhead is to collaborate with providers to prevent claims submission errors. Both health plans and providers spend significant time and effort addressing billing and reimbursement errors; resolution of these issues requires hours of work thus increasing administrative costs.
Recent studies found that as many as 80 percent of medical bills contain errors despite the extensive efforts employed to review documentation and prepare the claims. The difficulty in reducing errors lies in knowing exactly how and why the errors are occurring. To find the root causes of claims anomalies requires sophisticated analytics which, until recently, were expensive and could be time-consuming to implement.
With advancements in analytics, it has become much easier to develop and maintain the algorithms, or rules, necessary to detect potential claims issues. In addition, by utilizing cloud services, larger data sets can be analyzed instead of just a random sampling. This increases the probability of identifying the anomalies and quantifying the impact to the organization once root cause has been determined. It’s not just about having the right technologies and tools in place either. Working with expert consultants knowledgeable in claims processing as well as clinical quality can help, too. The return on investment can be millions of dollars, both from recovery of overpayments and reduction of administrative costs.
Creating transparency and trust
Another benefit to conducting ongoing medical cost management analysis is the opportunity to work collaboratively with provider networks and facilities to correct the identified issues. Since a health plan can review claims retrospectively for up to 24 months post-adjudication, this provides a robust data set in which to identify patterns. Health plans can provide targeted education to help the providers get it right the first time, saving everyone time and money. Analytics can also help you spot patterns of suspicious claims submission behavior, giving you the opportunity to monitor certain providers and prevent fraud. These are all key components of ensuring the concepts in value-based care: high quality at the lowest possible cost.
Health plans can also get a look at errors occurring within their own systems. If, for example, bundled payments somehow become unbundled during adjudication (we’ve seen this happen), the plan could end up paying thousands more on a claim than the contracted amount. Left unchecked, this process could end up significantly impacting the MLR, depending on the total number of claims that fall into this scenario.
Forecasting
Analytics can also help with predicting future trends. Forecasting claims liability, disbursement leakage, revenue, and other trends provides insight into the potential administrative costs associated with re-adjudication of claims, cost recovery and opportunities for cost optimization. This allows health plans to make more informed decisions about pricing, contracting and who to include in their provider networks.
While effective medial cost management can’t solve the nation’s high health costs on its own, it is a step in the right direction. Not only does it assist health plans in proactively tracking their MLR, it also serves to provide insights from a value-based care perspective. In today’s healthcare market, there is no doubt that utilizing analytics to lower costs can help health plans compete more effectively.