Assigning Electronic Remittance Advice to the correct clinics

Efficiently Aligning Masticable ERA Files with Their Respective Clinics

Project Overview

This module helps Dental Office Coordinators and Retrace Customer Supports to streamline the process of splitting ERA (Electronic Remittance Advice) documents and ensure they are routed to the correct clinics.

Time

8 Weeks

My Role

User Research | Interaction Design | UI Design

Product Designer

Team

Kevin Yang

Head of Product

Stephen Chan

Sr. VP of Engineering

Abdurrahman Avci

Front-End Engineering

Problem

AI matching ERA (Electronic Remittance Advice) with clinic’s error rate of up to 4%, which may lost 1 million dollars, which is not acceptable, the necessity of manual intervention, and difficulty in identifying redundant ERA files.

Outcome

Reduced ERA error rate to zero

Increased 80%

matching efficiency

Minimized clinic production loss by missing claim payments

Final Design Quick Look

By introducing this new workflow supported by the expandable table design, we've created a system that significantly reduces the time and complexity of assigning ERAs. The result is a huge efficiency gain - up to an 80% improvement. This frees up more time for customer support and dental office coordinators to focus on other important tasks, providing better service to patients and clinics alike.

Without Retrace

Retrace is an AI-powered platform that revolutionizes dental healthcare by streamlining billing and office solutions for dental providers. Our product, an end-to-end platform, automates the claim submission and payment process, ensures accurate, instant payments, and simplifies claim submission.

With Retrace

We handle the confusion of navigating multiple disjointed systems by seamlessly connecting providers, patients, and payers with an end-to-end solution that’s tailored to practice workflows.

Understand the claim life cycle

Providers

Claim Creation and Transmission

An automated function that generates dental claims once the treatments are completed. These claims contain all the essential information about the treatments and are created on behalf of the providers. Once the dental claims are created, they are automatically sent to a clearinghouse via our Retrace system.

Clearing House

Claim Check

The clearinghouse serves as an intermediary, checking if the claims meet all the required standards and criteria before forwarding them to the insurance payer.

Payers

Claim Processing

The insurance payer receives the claims from the clearinghouse and then initiates their internal claim processing mechanism. If the claim passes through all their checks, it successfully follows what we call the 'happy path'. After the claim has been processed, the payer generates an ERA, which they send back to the provider.

Retrace AI

96%

Clinics

AI-Driven ERA Navigation

Retrace receives these ERAs in the system. Each ERA may contain multiple claims and can sometimes be unclear as to which specific clinic it belongs to, especially when a practice operates multiple clinics. To resolve the issue of ERA attribution, we employ an AI system that navigates these ERAs to match them with their respective clinics. This AI system effectively manages to match 96% of the ERAs we receive.

Manual ERA Matching

The remaining 4% of ERAs that the AI cannot match requires human intervention. Initially, the Retrace Customer Support team steps in to handle these unmatched ERAs. However, if the team is unable to resolve the ERA attribution, the responsibility is then passed on to the Dental Office Coordinator from the respective practice.

Project Goal

Improve the accuracy of ERA-claim matching by leveraging AI

Minimize redundant ERA and Claim data

Proposed Solutions

Highlight key identifying factors to quickly identify target claims

To streamline the process of ERA matching, we propose the implementation of a system that highlights key identifying factors within each claim. This system enables quicker and more precise identification of target claims, significantly reducing the time spent on manual matching.

ERA and Claim Comparison Facilitation

Building on the claim identification system, we introduce a feature that facilitates the direct comparison of ERA and claim information. This feature provides a side-by-side view of the relevant data, allowing for a more intuitive and efficient review and matching process.

Confidence Score-Based Recommendations

To further optimize the matching process, we propose a recommendation system that provides match suggestions based on confidence scores assigned to each location. These confidence scores, calculated through a sophisticated AI algorithm, indicate the likelihood of a match. This system aims to enhance decision-making, prioritizing accuracy and efficiency in the ERA matching process.

Understand ERA matching workflows

Research Goal

Understand the workflow and main informations retrace customer support and dental office coordinator use for matching ERA with claim.

User Interview

Conducted user interviews with dental office coordinators and Retrace's customer support to understand the workflow and main information required for ERA-claim matching. The insights guided the prioritization of patient name, provider name/NPI, billing NPI, billed amount, and service in our design solutions.

Patient Name

Provider Name / NPI

Billing NPI

Billed Amount

Service

Pain Points

Cross-Clinic Patient Matching

AI match patient name in different clinic Internal admin need use as much as possible helpful information to investigate the ERA.

Multi-Clinic ERA

Patient and Provider may exist in multiple clinics.

Claim ID Errors

Claim ID may not accurate during matching due to data errors.

Redundant ERA

It's difficult for Internal admin to identify the redundant ERA file and remove it from headquarter.

Design Explorations

Bridging Proposed Solutions with Effective Interactions

Option 1

Drawer

Provides a centralized location for unmatched ERAs, streamlining the matching process for internal admin users.

Build Drawer
‍This solution centralizes unmatched ERAs, providing a focused view. However, it may suffer from the Serial-position Effect, a cognitive bias where users are more likely to remember information at the beginning and end of a list, but forget those in the middle. This could result in overlooked ERAs, particularly in large unmatched sets. Furthermore, it could also be subject to the Clustering Illusion, where users might perceive patterns in data where none exist, possibly leading to incorrect matching.

Option 2

Side by side ERA

Allows users to visually examine the ERA files for faster identification and matching.

Side by Side ERA Image
This solution is good for visual comparison. However, it is susceptible to the Aesthetic-usability Effect, where users perceive more aesthetic designs as more intuitive. If the visual presentation of ERAs isn't aesthetically pleasing or the quality of images is low, it might negatively impact the user's perception of usability. Moreover, this approach demands significant cognitive load, which might not be efficient when dealing with a large number of ERAs.

Option 3

Expandable Table

Presents all relevant information in an organized and easily accessible format for efficient matching.

Optimized Data Display for Streamlined Workflow

With a large monthly volume of 5,000 ERAs to manage, it's vital to have an efficient, organized method of displaying and interacting with data. Our expandable table was designed to meet this challenge.

200

Providers

5,000+

Monthly ERA Volume

Average Number of Claims per Clinic

Expandable Table
‍This solution organizes all relevant information efficiently, enabling users to view more details as needed. It reduces cognitive load and supports the Comparison Effect, where users make decisions based on side-by-side comparison. It also leverages the Law of Proximity, grouping related content together to facilitate user understanding and action. The expandable nature allows for more data to be presented without overwhelming the user initially, which adheres to the Progressive Disclosure principle. However, it is essential to ensure that users understand how to interact with the table to access additional information.

Expandable Table

Flow 1

Bulk Assign - For high confident clinics

For high confident score ERAs, the process is even more streamlined. These ERAs can be bulk assigned to clinics without the need to open each ERA's expandable table. This saves precious time and reduces cognitive load, improving efficiency significantly.

Flow 2

Investigate - For low confident clinics

Once they've located the ERAs that require investigation, they can simply click on an ERA record. This action expands the table, revealing a wealth of information split into two sections: ERA details and Matching Practices.

Pain Point Resolutions

AI match patient name in different clinics

The solution leverages key claim identifiers and an expandable table. These features help users swiftly and accurately match an ERA to a patient, even across multiple clinics.

Patient and Provider may exist in multiple clinics

The Confidence Score-Based Recommendation system in your design handles this complexity. It smartly suggests matches, swiftly assigning high-confidence ERAs and providing a detailed view for lower-confidence ERAs, ensuring correct assignments.

Claim ID may not be accurate due to data errors

The claim identification system, along with ERA and claim comparison features, helps validate the Claim ID. It provides a plethora of claim information, thus improving the accuracy of the matching process even in case of data errors.

Difficulty in identifying redundant ERA files

The system's advanced filtering options and the expandable table's organized display make redundancy detection simpler. Users can filter ERAs by various criteria and use the bulk selection feature to efficiently manage and remove redundant files.

User Testing & Validation

As the project's Lead Designer, I collaborated with a diverse team of stakeholders, including product managers, engineers, and customer-facing teams, to revolutionize the ERA matching process. We collectively identified the Expandable Table as the optimal design solution. Product managers provided crucial insight into our product goals, shaping the initial design. Engineers assessed the technical feasibility, recognizing the Expandable Table as a more adaptable, albeit challenging, framework capable of handling complex ERA matching tasks. Feedback from customer support, sales teams, and direct user input underscored real-world challenges and reaffirmed the necessity for a comprehensive solution. Thus, the Expandable Table, despite its initial development demands, emerged as a robust solution addressing user needs and product objectives.

Iterations

Matching practice score for high score ERAs

Based on user feedback, the matching practice and their score were brought to the forefront. A bulk assign feature was also introduced to increase matching efficiency.

Customer feedback

“This product has been an absolute lifesaver for our clinic, as it effortlessly solves the ERA matching challenge and simplifies the process of navigating through ERA to correct clinics.”

Success Metrics

Reduced workload for Retrace Customer Support.

Improved dental partner satisfaction

Future improvements

Matching practice score for high score ERAs

In the future, we plan to consider the Matching Practice Score Gap for further efficiency. We aim to show the matching practice and the score on the table and add another filter “ Score Gap ” for adjusting the filtering result." This can Prevent 2 ERA confident score too small and make the data density adjustable

Precision By setting a minimum score gap, users can ensure they're only viewing ERAs with a significant difference in confidence scores. This can help to avoid cases where two ERAs have very similar scores, making it hard to determine which is the correct match.

Efficiency If there are many ERAs to review, users can set a larger score gap to filter out less confident matches. This allows them to focus on the most promising matches first, improving efficiency.

Customization Different users might have different tolerance levels for uncertainty. Some might prefer to only see very confident matches, while others might be willing to consider less confident ones. The "Score Gap" feature allows each user to customize the view according to their preference.

Data Density Adjustment By manipulating the score gap, users can control the density of the data they are viewing. A smaller gap would result in more data, while a larger gap would result in less data. This can help users manage large amounts of data more effectively.

Key Takeaways

Innovating the ERA Matching Process for Enhanced Accuracy and Efficiency

User-centered design

Empathizing with our users and understanding their pain points were crucial in designing a solution that met their needs.

Collaboration

Working closely with the engineering team helped ensure the final product matched the design and met user needs.

Iteration

Continuous testing, feedback, and iteration were vital for improving the accuracy and efficiency of ERA-claim matching.