Medical Event Contextualization and Error Detection
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Solution Overview
Problem
Patients struggle to comprehend medical billing errors due to complex Explanation of Benefits (EOBs) and service codes, making it difficult for them to verify the authenticity of medical events and detect errors individually.
Innovation Solution
A computer-implemented method and system that normalizes and groups medical event data by provider and event, generating a user interface with interactive graphical elements for patients to provide feedback and determine the accuracy of claim data, allowing for contextualization and error detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical billing information is presented using standard codes and terminology, then accuracy and completeness of data are improved, but patient understanding and ease of verification deteriorate
Solution Approach 1:
The system introduces an intermediary layer between the raw medical billing data and the patient. This intermediary translates complex coding terminology into plain language descriptions while maintaining accurate reference to the original data, enabling patients to understand their bills without sacrificing data precision
Solution Approach 2:
The system changes the presentation parameters of the data by transforming technical coding parameters into human-readable formats. It maintains the underlying accurate data while changing how it is displayed, explained, and interacted with by patients through simplified interfaces
2Reliability
If detailed medical event data is provided to patients, then verification capability is improved, but complexity of the information deteriorates
Solution Approach 1:
The system segments the detailed medical event data into organized categories and groups related information together. It presents verification-capable details in a structured manner that reduces cognitive load, breaking down complex information into manageable sections while maintaining complete verification capability
Solution Approach 2:
The system incorporates feedback mechanisms that allow patients to interact with the data at their own pace. It provides adaptive guidance and explanations based on user interactions, delivering detailed verification information only when and where needed, thereby reducing overall information complexity
3Productivity
If automated data processing is used, then processing efficiency is improved, but error detection capability deteriorates
Solution Approach 1:
The system enables patients to perform self-verification of their medical billing data through an interactive interface. This self-service approach maintains high processing efficiency through automated data collection while simultaneously improving error detection by leveraging patient knowledge about their own medical events
Solution Approach 2:
The system implements feedback loops where patient responses to verification questions feed back into the error detection process. This feedback mechanism enhances automated processing by providing human validation that identifies errors automated systems might miss, thereby improving overall reliability
Data Source
AI summary
A computer-implemented method is described for contextualizing medical events and detecting errors, for example, in claims data. The method may include receiving medical event data from one or more reporting entities, and converting the received data by normalizing and grouping by provider and by medical event. The converted data is presented to the subject of the medical event(s), via a user interface (UI). The UI includes one or more interactive graphical elements (e.g., virtual buttons) each configured to provide feedback on specific aspects of the converted data for the medical event. Based on input from the subject via the UI, it is determined whether the subject believes the received data is correct. If the data is indicated to be incorrect, further investigation may be triggered, and the payor and/or reporting entity may be alerted.