Healthcare Bill Validation via OCR and Claims Standardization
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Solution Overview
Problem
The healthcare industry faces challenges in timely and accurate validation of medical provider bills due to synchronization issues between provider statements and explanation of benefits (EOB) forms, leading to confusion for patients and inefficiencies in payment processes, exacerbated by non-standardized formatting and untimely distribution of information across different insurance providers and locations.
Innovation Solution
A mobile app that uses OCR technology to standardize and synchronize adjudicated claims data, allowing patients to validate healthcare provider bills by matching key information such as provider name, amount due, and service dates, and facilitating digital payments through authorized payment methods, while also providing geolocation services to confirm visits and access to average procedure costs and provider ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If EOB forms are sent 2-4 weeks before or after provider bills, then insurance companies can process claims in batches, but the timing creates synchronization issues and confusion for patients
Solution Approach 1:
The system performs preliminary actions by pre-processing and standardizing EOB data before it arrives at the patient's location. The server proactively retrieves, parses, and stores adjudicated claims data in a standardized format, so when the provider bill arrives, the matching EOB data is already ready and synchronized, eliminating timing synchronization issues for the patient.
Solution Approach 2:
The server acts as an intermediary between the insurance company and the patient. It receives non-standardized EOB data from insurance companies, standardizes it according to a common format, stores it in a database, and makes it available for matching with provider bills. This intermediary layer eliminates the timing and format synchronization problems directly facing the patient.
2Quantity of substance
If EOB forms are grouped by patient and sent monthly, then insurance companies can reduce communication overhead, but patients must search through substantial unrelated information to find relevant claims
Solution Approach 1:
The system segments the EOB data extraction and processing into discrete, manageable components. It identifies and extracts only the relevant fields (provider name, amount due, service dates, patient responsibility) from the bulk EOB data, separating useful information from unrelated content. This segmented approach allows patients to access only the critical billing information without wading through substantial unrelated data.
Solution Approach 2:
The server extracts and isolates the essential billing information from the comprehensive EOB forms. It pulls out key data elements (provider name, amount due, service dates, patient responsibility) and stores them in a standardized, easily queryable format. This extraction process removes the burden of patients searching through large volumes of unrelated information while preserving all necessary billing details.
3Adaptability or versatility
If provider bills are stored locally in platform-dependent formats, then providers can maintain data in their existing systems, but information sharing becomes difficult due to non-standardized formatting
Solution Approach 1:
The system changes the parameter of data format by converting various platform-dependent and insurance-company-specific formats into a single standardized format. The server receives EOB data in multiple formats from different insurance companies and transforms all of them into a common standardized structure, enabling seamless information sharing and matching with provider bills regardless of the original format.
Solution Approach 2:
The server creates a universal data representation that can handle EOB data from multiple insurance companies and providers. By establishing a common standardized format, the system enables one data structure to serve multiple purposes: receiving data from various insurance companies, storing it consistently, matching it with provider bills, and presenting it to patients. This universal approach eliminates format incompatibility issues.
4Reliability
If patients manually verify and decipher EOB information against provider bills, then patients can ensure accuracy, but the process is time-consuming and complex
Solution Approach 1:
The system performs self-service validation by automatically matching provider bills with corresponding EOB data using key fields (patient name, provider name, service dates, amounts). The server autonomously verifies billing accuracy, identifies patient responsibility amounts, and prepares payment information without requiring manual patient intervention. This automation maintains high reliability while dramatically reducing validation time.
Solution Approach 2:
The system provides feedback to patients by automatically generating and presenting validated billing information, including the correct patient responsibility amount and payment details. The server compares provider bill data against EOB data, identifies discrepancies, and presents resolved information to patients, eliminating the need for manual verification while ensuring accuracy through systematic comparison.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables patients to confidently and timely pay the correct amount, reducing interaction with the billing process, ensuring healthcare providers receive payments faster, and providing transparency into medical costs, thus improving the overall billing and payment process.
Implementation Method 1
A user of the mobile device (also referred to as 'the patient') receives a healthcare provider bill and obtains a photographic image of the bill
Implementation Method 2
The instructions then cause the mobile device to execute an optical character recognition (OCR) engine to convert the visual text in the bill image into machine readable text data
Data Source
AI summary
A system for validation of healthcare provider bills includes obtaining an image of the bill on a mobile device which uses optical character recognition to resolve the patient identity, provider identity and amount due as alphanumeric characters. The system also receives adjudicated claims data from insurance companies in a non-standardized format. A claims conversion server converts the adjudicated claims data to a standardized machine-readable format. The standardized adjudicated claims data and provider bill are validated against each other in real-time so the patient can make payment in an accurate amount without having to first receive and decipher an EOB form.