Keeping an Eye on the Watch List – Epic Tips & Tricks

Customization in Epic is a blessing and a curse.  If you can get it right, it can drive reporting and workflows that far exceed functionality of other EMR platforms.  When it goes wrong, it can result in information overload and cumbersome workflows that kill productivity and put accounts at risk.  One underutilized reporting feature that is highly customizable within Epic is the Watch List. In this month’s blog, we’ll discuss the flexibility of the Watch List, how it can best be used, and common pitfalls of Watch List development.  

Watch List – Overview and flexibility

The Watch List is a component that can be plugged into Radar Dashboards within Epic that highlights key metrics within Patient Financial Services (PFS).  While the Watch List has improved over time, it remains one of the most powerful, yet underutilized tools within Epic. The Watch List is comprised of custom rules that can be catered to various owning areas (billing, follow-up, denials, variance, self-pay, department specific etc.) within the revenue cycle.  Strategic metric design can help identify high opportunity/risk account populations to drive back end collections efforts while reducing bad debt.

Watch List – Use cases

There are three different types of metrics that should be monitored within the Watch List.

  • High Risk – Populations of accounts that if not addressed, may result in the provider being exposed to heightened financial risk.  Examples include accounts not qualifying for workqueues, accounts on delinquent workqueues, and timely filing/appeal.
  • High Opportunity – Account populations that represent significant financial opportunity once resolved.  Examples include high dollar visits with a single billing edit, aged uncoded visits, high dollar underpayments or revenue guardian edits.
  • System Clean-up – Populations of accounts that are cluttering worklists, but do not require immediate action.  Examples may include aged low dollar open denials, open denials with a $0 balance, populations that should qualify for the next responsible payer, accounts on numerous workqueues, and vendor populations that have not been returned to the provider.

Watch List – Common pitfalls

The Watch List is a powerful tool when utilized and managed correctly.  However, despite the best of intentions, many providers fail to adequately build out functionality and manage populations.  The following are common pitfalls that are encountered when developing the Watch List.

  • Lack of clear ownership – The best metric build is worthless if we don’t have clear ownership and accountability. Many providers fail to delegate a single person to manage Watch List metrics or assign various owners to specific metrics.
  • Limited testing/validation – Building out rules within Epic always requires extensive testing and validation.  IT and operations need to partner to validate metrics prior to roll-out.
  • Overbuild – Too many metrics can be burdensome and take away from the key areas of focus.  Metric build should be focused on those highest priority populations that can be effectively managed.
  • Failure to monitor over time – Workflows and functionality evolve over time.  Watch List metrics need to be maintained over time to ensure they continue to provide value.

Parting Thoughts

  1. The Watch List is a highly powerful, yet under utilized tool within the Epic reporting platform
  2. Strategic metric design can drive a powerful daily management tool that can expose issues surrounding system build, as well as high opportunity/risk account populations
  3. Strategic build/testing/validation is critical to a successful Watch List implementation
  4. Clear ownership and accountability must be in place to drive long term performance
  5. Overdevelopment of the Watch List can take away from the ability to quickly identify areas of opportunity

For more information related to Epic optimization or revenue cycle management, please contact Kevin Blanchard at

3 Factors that Reduce the Accuracy of Your Hospital’s Cost Estimation

hospital cost estimations

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