The complexity of American healthcare payments results in an elaborate, bureaucratic process for doctors to get paid. When a patient comes in for a consultation, the provider must first verify the patient’s insurance coverage, deliver care accordingly, document it, code it and fill out claims to be submitted to payers. On the payer’s end, the claim is analyzed, and if everything is alright, the provider is reimbursed.
However, analyzing claims is a highly tedious and manual process. It involves going over tons of paperwork and examining appointments, bills, and doctor-patient communication to see if everything about each patient’s health plan and treatment adds up. Needless to say, the process is bound to be highly inefficient and payers may end up paying more where nothing should have been paid in the first place.
That’s where insurance claims analytics can help boost the efficiency and productivity of payers.
Medical claim data would hold important information such as patient encounters, treatments, diagnosis, billed and paid amounts, and so forth. Assessing them helps payers optimize their operations and even identify fraudulent claims. Research estimates that between 3% and 10% of health care expenditures (approximately $60 billion) dished out by payers are based on fraudulent claims. Unsurprisingly, this eats into the revenues of payers and puts an administrative burden on the staff, not to mention some patients who must go through unnecessary medical procedures.
Health insurance claims data enables payers to leverage the power of data assessment and implement predictive analytics to bolster their own efforts. As mentioned earlier, claims processing makes up the biggest administrative expense at any insurance payer’s organization. It involves a series of processes that are prone to errors. However, insurance claim software introduces digitization and automation to many of the processes that make up the overall workflow of insurance claims. Automation coupled with digitization results in many of the menial, repetitive tasks being carried out by the software.
Additionally, when labor-intensive activities in claims management move from physical to digital, the team of professionals working could complete things with just a few clicks. Needless to elaborate, all of this eventually culminates into superior claims analytics in healthcare.
Perhaps the most useful aspect of insurance claims analytics is the ability to use the data and take a proactive approach. As mentioned earlier, predictive analytics can identify claims that are more likely to incur high costs of defending them. It’s won’t be a stretch to say that defending disputed claims takes up a sizeable portion of an insurance company’s expenses. Solutions for insurance claims analytics, or rather predictive analytics, enable payers to find out which of the claims have a higher likelihood to result in litigation.
On one hand, health insurance analytics solutions help to know which claims may end up being contested, in addition to highlighting those that might result in higher costs of defense. Both of these advantages eventually go a long way in helping payers streamline their operations and minimize losses. To sum it up, using insurance claims analytics helps payers optimize their operations, accelerates claims assessment, and prevents unnecessary expenditure. The result is a win-win for providers, payers, and most of all, the patients.
