Automated Medical Documentation Query System
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Solution Overview
Problem
Current medical documentation systems face inefficiencies in extracting information from physicians, leading to incomplete and inaccurate patient records, as they rely on manual processes that are time-consuming and prone to errors, especially in updating problem lists and billing codes.
Innovation Solution
A computer-implemented system that automatically identifies missing or ambiguous information in medical documentation, generates queries to solicit user input, and updates documentation accordingly, using natural language processing to analyze and map patient data to standardized codes, thereby reducing the need for manual intervention and improving data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to extract information from physicians and update documentation, then flexibility and adaptability in handling diverse medical scenarios are maintained, but time consumption and error rates increase significantly
Solution Approach 1:
The patent replaces manual mechanical processes (physicians typing or dictating encounters, specialists reading documentation, coders reviewing records) with an automated computer-implemented system that uses natural language processing and pattern recognition to extract information, identify undocumented items, generate codes, and create queries automatically
Solution Approach 2:
The system enables self-service by automatically performing documentation review, code generation, and query creation without requiring manual intervention from specialists or coders, allowing the system to serve itself in identifying issues and soliciting necessary information from physicians
2Productivity
If manual review processes are used by specialists and coders, then accuracy in identifying documentation issues can be maintained through human judgment, but productivity and throughput are significantly reduced
Solution Approach 1:
The system incorporates feedback mechanisms where generated queries are sent to physicians for confirmation, and the system learns from physician responses to improve future automated coding accuracy, creating a closed-loop system that continuously refines its reliability
Solution Approach 2:
The system acts as an intermediary between the raw medical documentation and the final coded output, using natural language processing and pattern recognition as intermediate steps to bridge the gap between unstructured physician notes and standardized medical codes, thereby improving both speed and accuracy
3Loss of time
If automated code generation is implemented without human review, then productivity and speed are improved, but errors and inaccuracies in coding increase
Solution Approach 1:
The system performs preliminary automated code generation based on analyzed documentation, preparing draft codes and identifying undocumented items before physician review, so that when physicians do review, they are confirming or correcting pre-prepared options rather than creating codes from scratch, thereby maintaining accuracy while improving speed
Data Source
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AI summary
This disclosure describes systems, devices, and techniques for automatically identifying missing or ambiguous information in documentation associated with a patient. In one example, a computerized system for updating medical documentation may include one or more computing devices configured to receive a code representative of one or more undocumented items determined from a plurality of documented items related to the patient. The one or more computing devices may be configured to generate, based on the code, query that solicits user input addressing the one or more undocumented items and output, for display, the query.