Over the past several years, price transparency has become a hot topic in healthcare.  Providing an accurate estimate of patient liability prior to service is one area many organizations are focusing on to address price transparency.  With that in mind, there are a variety of products in the revenue cycle space that providers can use to automatically calculate an estimate of a patient’s out of pocket liability.  Armed with a cost estimate, revenue cycle staff are equipped to have a meaningful conversation about the patient’s cost for the service. This conversation with the patient increases transparency and drives cash collections.  The time savings that comes with automation is a hard benefit to dismiss, but what about the accuracy? Like most products with numerous inputs and data sources, the initial configuration and the ongoing maintenance is critical to ensuring an accurate tool.  

 

First let’s take a look at the 4 main data elements that go into a cost estimation:

  1. Hospital Charge Description Master (CDM) – Your organization’s specific charges for services provided.  Utilized to estimate the gross charges associated with straightforward services (e.g., CTs, MRIs, etc.)
  2. 837 Claims Data – Actual claims history your organization has submitted electronically to payers that is aggregated to estimate gross charges associated with more complex services (e.g., surgeries)
  3. Payer Contracts – Your organization’s contractual terms with each contracted payer.  Utilized to estimate the allowed reimbursement for that payer. The allowed amount can be equated to what the insurance company will pay the hospital for that service.  This value is critical in the calculation of a patient’s coinsurance (% of the contracted amount the patient has to pay). Contracts can range from straightforward “% of charge” to significantly more complex reimbursement methodologies.
  4. Electronic Eligibility and Benefit Inquiry Responses (EDI 271) – Electronic benefit information directly from the payer via 271 response that outlines patient cost expectations (copay, deductible, coinsurance, and max out of pocket information)

 

Utilizing the above data, the tool estimates the gross charges associated with the service, the payer specific allowed amount for that payer based on the contract, and finally the patient’s copay, coinsurance, and deductible, if applicable.  Sounds pretty straight forward right? To have the best probability of an accurate cost estimation tool, each of the above items needs to not only be accurate, but refreshed periodically with the most current updates. Now let’s take a closer look at the 4 benefit components that go into the calculation:

  1. Copay – What is the copay associated with that specific service?  This comes directly from the benefit information received in the 271 response
  2. Deductible – What is the patient’s yearly deductible?  This also comes directly from the benefit information received in the 271 response.  For this to be an accurate part of the calculation, you need to know what portion of the deductible is remaining.
  3. Coinsurance -What is the patient’s cost share % of the contracted allowed amount?  This is where the 837 claims data and contracts come into play. For this to be accurate, you need to know the coinsurance for that specific type of service AND the contracted allowed amount for that particular payer.  
  4. Max out of pocket – What is the maximum yearly amount the patient can pay in a given year?  You need to know what portion is remaining.

 

With all the moving parts above, 3 specific items stand out as the top culprits for an inaccurate estimate:

  1. Payer contracts are not built correctly into the tool resulting in an inaccurate allowable amount/contracted rate and therefore an inaccurate coinsurance amount
  2. 837 data and/or CDM data is not refreshed frequently enough (or at all), resulting in an inaccurate coinsurance amount
  3. Specific benefits from the 271 response are either not available in the 271 response provided by the payer, or are not mapped correctly in the tool to tie the right benefit components to that particular type of service.  For example, a patient may have specific copay/coinsurance benefits for diagnostic imaging services, but the payer either doesn’t provide those in the 271 response, or the mapping in the tool is pulling more generic outpatient benefits instead.

 

While cost estimates are a great tool that allow for increased transparency, improved cash collections, and reduced bad debt, inaccurate estimates can lead to undesirable outcomes such as excessive patient credits and patient billing complaints.  For more information on improving the accuracy of your cost estimates, please contact Andrew at [email protected].