Medical Coding System for ICD-10 Precision and Workflow Efficiency
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Solution Overview
Problem
The transition from ICD-9 to ICD-10 coding in medical billing requires more detailed and specific codes, leading to challenges in accurately determining diagnosis codes, with existing solutions like natural language processing and keyword searches being unreliable and inefficient.
Innovation Solution
A system and method that assists in gathering and documenting relevant data during a doctor-patient encounter by correlating selectable data items with standardized ICD-10 codes, dynamically tracking information, and providing indications for missing data to ensure proper code selection, including error checking and notifications for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ICD-10 codes are used for billing purposes, then coding precision and detail are improved, but device complexity and difficulty of operation increase due to the large number of codes and required detailed information
Solution Approach 1:
The system performs preliminary actions by automatically generating a list of candidate ICD-10 codes based on the physician's initial diagnosis documentation, before the final code selection is made. This preliminary code generation reduces the complexity burden on the user by pre-processing the coding task and presenting only relevant options.
Solution Approach 2:
The system acts as an intermediary between the physician's documentation and the final ICD-10 code selection. It processes the documentation, generates candidate codes, and presents them to the physician for confirmation, thereby mediating the complex translation process between clinical documentation and standardized coding.
2Measurement precision
If ICD-10 codes are used for billing purposes, then coding precision is improved, but loss of time increases due to the need for additional data collection and documentation
Solution Approach 1:
The system performs self-service by automatically analyzing the physician's documentation and generating candidate ICD-10 codes without requiring manual code lookup or extensive additional data collection. The system serves itself by processing the available documentation to produce code suggestions, reducing the time burden on users.
Solution Approach 2:
The system performs preliminary code generation based on the documentation that is already available during the patient encounter, rather than requiring additional time for data collection after the encounter. This preliminary processing reduces the time loss by working with existing information.
3Ease of operation
If natural language processing is used to locate diagnosis codes, then ease of operation is improved, but reliability deteriorates due to unreliable and inaccurate technology
Solution Approach 1:
The system implements feedback by presenting multiple candidate codes to the physician and allowing selection or correction. The system receives feedback from the user's choice and can learn from corrections to improve future suggestions. This feedback mechanism maintains ease of operation while improving reliability through user verification.
Solution Approach 2:
The system is dynamic in that it adapts to user input and corrections. Rather than relying solely on static natural language processing accuracy, the system dynamically adjusts based on physician selection and can incorporate user preferences and corrections to improve reliability over time while maintaining ease of use.
4Ease of operation
If keyword search is used to find diagnosis codes, then ease of operation is improved, but productivity deteriorates when 500 or more results are returned requiring manual review
Solution Approach 1:
The system applies local quality by providing different levels of code detail and information in different parts of the interface. It presents candidate codes with varying levels of specificity and allows the user to drill down into relevant details only for the codes of interest, rather than requiring review of all 500+ results uniformly.
Solution Approach 2:
The system segments the large set of 500+ keyword search results into smaller, more manageable groups or candidate lists. By dividing the results into segmented categories or prioritized groups, the system maintains ease of operation while improving productivity by reducing the manual review burden.
Data Source
AI summary
In certain arrangements, systems and methods assist in gathering relevant data in a doctor-patient encounter for obtaining a properly specified diagnosis code. In one embodiment, selectable data items which are provided as part of a medical charting program may be correlated with one or more standardized diagnosis codes (e.g. ICD-10 codes). Upon selection of the appropriate data items when charting a patient encounter, one or more diagnosis codes which are correlated with the selected data items may be flagged and/or generated for later use, such as for filing a claim submission as part of a billing process or to further enhance the clinical workflow of patient encounter documentation.