How Analytics Is The Answer To Compliant Coverage Identification for Medical Billing?

How Analytics Is The Answer To Compliant Coverage Identification for Medical Billing

Today with the healthcare industry undergoing a huge change; adding up a lot of confusion and challenges that physicians practices, hospitals, labs, clinics and durable medical equipment companies are finding it hard to cope up. The liability of the self-pay accounts is a growing problem with the providers which is now incurring billions of dollars losses every year.

Health care providers require an efficient compliant method to identify the source of reimbursement in timely manner before they file the deadlines.

Knowing the root cause of the problem

Capturing complete patient information during the registration process is not fool-proof, and in fact, is fraught with pitfalls. Many providers’ processes do not capture all available data, or patients may inadvertently or willfully withhold information. Additionally, human errors made in the collection of basic demographics such as name, address, Social Security number, insurance, employment can also mask a patient’s eligibility for insurance coverage or financial assistance. And even when this information is captured and accurate, there is no guarantee that all insurance sources have been identified.

Advance analytics combined with rich data resources can help providers to tap the new resources of reimbursement when maintaining the compliance.

Analytics is the key answer

Implementing an innovative combination of advanced analytics combined with the rich data enables providers to trace and undisclosed the coverage furthermore; determining the eligibility for the financial assistance without compromising on the compliance.

Analytic driven screening process can increase compliance with state regulations and CMS along with improving providers’ reimbursements, decreasing operational costs.

How Analytics can help

The reliability of analytics can help healthcare providers to gain insights that patients are unable to provide. Below mention 4 usual scenarios where data reveals more about patient’s conversation:

  1. During registration, a patient shares that he/she doesn’t have insurance coverage. Using his demographic profile, advanced analytics, and data-source searches, it is revealed the patient has undocumented Medicare coverage.
  2. An intake conversation doesn’t reveal whether the patient qualifies for assistance. Data and patient modeling confirm the patient’s residency, estimated income, family size, and placement on the federal poverty scale. The patient is eligible for Medicaid and is automatically assigned financial assistance.
  3. An emergency room patient shares that he/she has Medicaid coverage. Advanced data-mining capabilities reveal the patient’s injury resulted from a car accident, and the patient has motor vehicle accident insurance that will yield a higher reimbursement.
  4. A patient shares that he/she doesn’t have insurance coverage, but expresses an ability and likelihood to pay the balance owed. An analysis of multiple private and public data sources uncovers the patient’s full demographic and financial profile, qualification of eligibility for assistance programs, and propensity to pay. Staff can further share the charitable resources with the patient, and offer to assist with applications.

Providers that use advanced analytics to detect undisclosed coverage can reap numerous benefits, including improved revenue performance, decreased cost-to-collect on self-pay accounts, and increased patient satisfaction that results from identifying sources of financial assistance. What’s more, some solutions likewise as coverage insight are pay-for-performance; meaning customers are only paying a percent of the net gain.

This entry was posted in Medical Billing, Medical Billing Company, Medical Billing Services, Revenue Cycle Management (RCM) and tagged , , , , , , , , , , , . Bookmark the permalink.

CONTACT FORM

What are you looking for

Leave a Reply

Your email address will not be published. Required fields are marked *