Medical claims adjudication refers to the determination of the payer’s responsibility with respect to the member’s benefits and provider payment arrangement. The insurance company has a few actions it can take – they either pay the full amount of the claim, deny the claim, or reduce the amount that is paid to the provider per contractual rates. Improving auto-adjudication can drastically improve how quickly and precisely claims can be processed.
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What Makes Auto-Adjudication Better Than Manual?
Auto-adjudication is the process of paying or denying insurance and public benefits claims quickly without reviewing each claim manually. Auto-adjudication uses advanced AI software to scan for errors then match key details to make the decision of approval, denial, or a change to the claim automatically.
Auto-adjudication isn’t just a tongue twister, it’s changing the entire way claims processing is done. It creates a seamless channel that is both paperless and humanless. We’ve talked about how long it can take to process a medical claim from the day of the appointment, to finally getting paid by the insurance company.
What Could Be Limiting Auto-Adjudication? Internal & External Influences
Internally there are multiple reasons which could prevent auto adjudication. Some adjudication platforms have limitations regarding accepting certain loops or segments carried in the EDI. In these cases, things such as primary payer adjustments, and other contractual PPO or bill review adjustments may cause claims to pending for review. Additionally, many procedures could be flagged by the payment system to ensure that medical necessity or prior approval was provided for the services.
Externally claims adjudication can be subject to even more causes such as billing errors, and mapping anomalies from downstream data sources. Billing errors can generally be detected upstream through standardized SNIP edits but each payer is unique regarding their provider relations, error management, and validation rules. Additionally, factors such as name mismatches can also cause pends for many platforms. If a provider bills the claim as Jenny but the patient is on file is Jennifer, how does your platform handle that?
How Do We Improve Auto-Adjudication?
A number of upfront validation checks such as member matching, provider matching, and business rules and edits can help improve auto-adjudication to handle those discrepancies.
Pre-adjudication member matching can help reduce pends by normalizing disparities between proper names and nicknames of your members. Additionally, this type of data validation and cleanup can resolve additional inconsistencies such as members being billed under their Social Security Number instead of their correct member ID. Even for cases where all of the information is correct, the claim may be for an individual who truly is not a member of your plan or perhaps a member who had coverage at one point but not during the dates of service for the claim. For those cases, rejection kick-outs can remove those claims from your upfront workflow allowing only clean normalized data to pass into your adjudication system thus improving auto adjudication rates.
Provider matching works similarly to member matching and can help ensure only clean normalized claim data is presented to your system. Provider name variations, ID numbers, tax ids, and other billing identifiers can be normalized through upfront validation processes done at the clearinghouse level or other pre-adjudication processes. This validation can also identify new providers flagging them for entry in your system allowing your team to examine the new provider information and make sure that it can be added to the system exactly as it should be. Provider matching also can have additional benefits beyond auto adjudication in that it reduces the number of duplicate provider records which could be created due to minor variations in name or address listings.
Applying Business Rules and Edits
Other pre-adjudication edits can be used to screen for other business cases preventing auto adjudication. This can include EDI SNIP edits and can go further such as ensuring that all diagnosis codes used are specific enough for payment. Custom or proprietary business rules can be enforced such as remapping provider contract information from notes fields to other segments of the EDI. Smart Data Solutions’ customized services ensure business rules are applied and mapping requirements are executed prior to adjudication. Many of our clients also prefer the simplicity of using a single vendor gateway, rather than managing multiple vendors.
SNIP Level Validation
SNIP validation includes seven guidelines for industry-standard levels of verification for electronic data compliance. SNIP is an acronym for the Strategic National Implementation Process, developed by the Workgroup for Electronic Data Interchange. The seven tests for data compliance are integrity, requirements, balancing, situational, code set, line of business, and trading partner.
The seven levels of testing play a significant role in the development and implementation of auto-adjudication by creating a guideline for maintaining compliance across all levels of your workflow.
These SNIP level edits and testing ensure that your business is capable of auto-adjudication. Using SNIP level validation early on in the claims process helps to avoid common issues like billing errors, and mismatched claims.
Improving processes and increasing auto-adjudication rates is a top priority for us at Smart Data Solutions. Using SNIP level edits, machine learning, and advanced AI solutions develop streamlined claims processing free of errors and eliminate much of the risky manual processes.
Increasing auto-adjudication rates is a top priority for Smart Data Solutions. We strive to constantly improve by utilizing machine learning and advanced AI solutions to provide error-free, paper-free, human-free claims processing. Take a look at what we are doing to automate and eliminate manual processes.