Real-Time EMR Co-Morbidity Capture for Accurate Diagnosis Coding
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Solution Overview
Problem
Existing healthcare systems face challenges in accurately capturing and communicating co-morbidities, leading to misinterpretation of medical records, which affects the assignment of DRGs and reimbursement, and requires timely recognition and documentation by healthcare providers.
Innovation Solution
A rules-based interface and co-morbidity assessment engine that analyzes electronic medical records in real-time, suggesting potential diagnoses and alerts healthcare providers to add missing co-morbidities to the patient's Problem List, ensuring compliance with coding criteria and facilitating accurate reimbursement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual documentation and coding processes are used, then healthcare providers have flexibility in documentation, but accuracy of co-morbidity capture and coding precision deteriorate
Solution Approach 1:
The patent replaces manual mechanical documentation processes with an automated electronic system that uses natural language processing and machine learning algorithms to extract co-morbidities from clinical notes, thereby improving accuracy while reducing the complexity burden on providers
Solution Approach 2:
The system enables self-service by automatically generating co-morbidity assessments and coding recommendations from unstructured clinical documentation, allowing the system to perform the coding function autonomously without requiring manual intervention from providers or coders
2Measurement precision
If real-time analysis of electronic medical records is implemented, then accuracy of medical record documentation improves, but device complexity and processing requirements increase
Solution Approach 1:
The patent introduces an intermediary layer consisting of natural language processing modules and machine learning models that bridge the gap between unstructured clinical documentation and structured coding requirements, enabling real-time analysis without requiring direct complex processing of raw text
Solution Approach 2:
The system performs preliminary action by pre-processing and structuring clinical documentation as it is entered, extracting potential co-morbidities in real-time before final coding decisions are made, thereby simplifying the subsequent coding process
3Reliability
If comprehensive co-morbidity assessment is performed, then reimbursement accuracy improves, but time required for documentation and coding increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors coding accuracy and adjusts its algorithms based on outcomes, providing real-time feedback to providers about captured co-morbidities and allowing for quick corrections without time-consuming manual review
Solution Approach 2:
The system performs comprehensive co-morbidity assessment automatically through self-service mechanisms, using machine learning models to identify and code co-morbidities without requiring manual review, thereby maintaining high reimbursement accuracy while minimizing time loss
4Productivity
If automated rules-based interface is used, then productivity and efficiency improve, but measurement precision may deteriorate due to rigid rule following
Solution Approach 1:
The patent applies dynamics by making the rules-based system adaptive and flexible, allowing the automated system to learn from clinical context and adjust its rule application dynamically, thereby maintaining high productivity while improving measurement precision through context-aware decision-making
Data Source
AI summary
A method for developing codes and a cross functional set of rules that suggest to a physician, in real-time, potential diagnoses related to diagnostic results or other clinical data entered into the electronic Medical Record (EMR). The method generates a possible diagnosis for consideration and addition to the patient problem list. When the physician selects a suggested diagnosis, the problem list is automatically updated with the diagnosis. In view of the above, the physician has the needed information at the point of care so that the timeliness, safety and quality of care is improved. The generated documentation also assists other physicians throughout the continuum of care for the Patient. The method creates crosswalk from data element to diagnosis to enhance the completeness of documentation. As a result, the Diagnostic Related Group (DRG) is more accurately assigned by coders at discharge to drive more accurate and efficient billing for improved reimbursement and revenue.