Improved medical claim management has been a necessity in the healthcare industry for some time. According to an article in Becker’s Hospital Review, a study showed that 62% of the healthcare executives interviewed noted declining reimbursements to be the most pressing challenge within their health systems.
The healthcare industry sees an average claim denial rate of 9%, totaling about $262 billion annually among hospitals and health systems. Each hospital, on average, loses 3% of its revenue to denied medical claims.
When a claim is denied, a provider can rework and resubmit a claim for approval. It costs, on average, $118 to rework a claim. This adds up to about $8.6 billion annually in administrative costs, nationwide, on the recovery of denied claims.
If a claim is not paid by the payer, the burden of the payment falls on the patient. Many patients have a difficult time paying for basic medical expenses. The Kaiser Family Foundation/New York Times Medical Bills Survey reported that 46.7% of patients interviewed in the survey had problems paying their household medical bills. Patients may not be able to pay for the additional cost of medical treatment due to denied claims.
Keeping denial rates as low as possible is the ultimate goal for providers. A study by Change Healthcare shows 54.2% of denials can be attributed to complications with registration/eligibility, service not covered, pre-authorization/pre-certification, and medical necessity.
A claim is processed differently depending on which payer a provider is engaging with. Similarly, the processing of a claim for the same payer differs by state. This complicates the claim cycle and the nature of denials.
Although providers may never see a denial-free situation, the ineffective management of claims results in an abundance of missed revenue. It is obvious that there is a need for technical solutions to combat these challenges. Researchers are looking at artificial intelligence and data analytics for potential help to address these issues.