Demographic Data Standardization and Verification System
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Solution Overview
Problem
Service providers face challenges in ensuring accurate patient or client record data for billing, leading to incorrect address information, resulting in unopened billing statements, costly reprocessing, and operational inefficiencies.
Innovation Solution
Implementing an automatic information standardization and verification system that standardizes demographic data into a standard format, determines the level of verification needed, and cross-matches it with third-party data sources in real-time to correct inconsistencies, thereby improving billing delivery and reducing operational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If address verification is performed after billing statements are returned to the service provider, then the service provider can identify and correct incorrect addresses, but the process of researching and identifying correct addresses, reprinting statements, and resending billing statements becomes costly and time-consuming
Solution Approach 1:
The system performs address verification and standardization before billing statements are generated and sent to patients. By validating address information upfront using third-party data sources and automated standardization algorithms, the system prevents incorrect addresses from entering the billing process, thereby avoiding subsequent reprocessing costs and time losses.
Solution Approach 2:
The system provides real-time feedback to service providers about address accuracy by comparing entered demographic information against third-party data sources. This feedback mechanism allows providers to correct incorrect addresses before billing statements are sent, improving reliability while minimizing time loss by preventing rework.
2Reliability
If address verification is performed on the front end before billing statements are sent to patients and clients, then incorrect addresses can be corrected before billing, but searching third party data sources becomes expensive and/or time-consuming
Solution Approach 1:
The system applies different verification strategies to different patients based on risk assessment. Instead of uniformly verifying all patients against all third-party data sources, the system prioritizes verification for high-risk cases (e.g., new patients, patients with incomplete information) while using faster, less expensive verification methods for low-risk cases, thereby reducing overall cost and time consumption while maintaining high reliability where needed.
Solution Approach 2:
The system performs partial verification by checking only the most critical address fields (street address, city, state, zip code) against the most relevant third-party sources rather than conducting comprehensive verification of all demographic data. This partial action approach reduces the cost and time of verification while still achieving sufficient reliability for billing purposes.
3Ease of operation
If demographic information is entered manually into the information system, then service providers can capture patient data, but input errors and wrong information lead to inaccurate demographic data
Solution Approach 1:
The system automatically standardizes and verifies demographic information by comparing it against third-party data sources. Instead of relying solely on manual data entry accuracy, the system enables self-verification where entered data is automatically checked for consistency with external records, correcting errors without requiring additional manual input from service providers and maintaining ease of operation while improving data accuracy.
Data Source
AI summary
Ensuring that a user's demographic data is correct so that billing statements may be sent to a correct address for collecting payment(s) from the user is a challenge many service providers face. Automatic information standardization and verification is provided. When a user's demographic information is inputted into an information system, address information is standardized into a standard format, a breadth of a verification to perform for the user is determined, and cross-matching the user's demographic information with information provided by one or more third party data sources is performed. Results may be provided in near real-time so that any inconsistencies may be corrected on the front-end and thus avoid unnecessary operation costs and improving billing and statement delivery.