PDPM System Extracting Patient Diagnostics from Disparate EHR Data
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Solution Overview
Problem
The fragmentation of healthcare documentation across multiple Electronic Health Record systems leads to inaccurate and inefficient coding of patient records, particularly when patients transfer between healthcare settings, due to lack of integration and interoperability, resulting in time-consuming and error-prone review processes.
Innovation Solution
A Patient Driven Payment Model (PDPM) system that extracts actionable details from hospital discharge documentation, therapy records, and nursing notes using data mining and expert system capabilities to facilitate accurate and consistent coding, integrating unstructured data from various sources and providing a user interface for MDS coding decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If healthcare documentation is fragmented across multiple Electronic Health Record systems, then each system can maintain its specialized functionality, but the coding accuracy and efficiency deteriorate due to lack of integration
Solution Approach 1:
The patent merges data from multiple disparate Electronic Health Record systems into a unified analysis platform. The system consolidates documentation from different sources (nursing notes, therapy records, physician orders) and integrates them through natural language processing and data normalization techniques, enabling accurate coding decisions while preserving the specialized functionality of individual systems.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between multiple EHR systems and the coding process. This intermediary system performs data extraction, normalization, and integration, translating data from various formats and structures into a unified representation that maintains coding accuracy without requiring direct integration between specialized systems.
2Adaptability or versatility
If healthcare documentation is fragmented across multiple systems, then system independence is maintained, but the review process becomes time-consuming
Solution Approach 1:
The patent performs preliminary data processing, extraction, and normalization automatically before the coding review process. The system pre-processes documentation from multiple independent EHR systems, organizing and structuring the data in advance, which significantly reduces the time required for subsequent coding reviews while maintaining system independence.
Solution Approach 2:
The patent implements self-service automation where the system automatically extracts relevant information, identifies coding opportunities, and prepares draft codes without human intervention. This automated self-service approach reduces the time-consuming manual review process while allowing clinicians to maintain independence in their respective systems.
3Reliability
If manual review of patient records is performed across multiple systems, then comprehensive review is possible, but error rates increase due to human fatigue and inconsistency
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously learns from coding decisions and outcomes. The system provides feedback loops that refine its natural language processing and data extraction algorithms over time, improving coding accuracy while maintaining comprehensive review capabilities. This automated feedback reduces human fatigue and inconsistency.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated computational systems. Natural language processing algorithms, machine learning models, and automated data extraction techniques substitute for human reviewers, eliminating fatigue and inconsistency while maintaining comprehensive review of patient records across multiple systems.
4Loss of information
If data is extracted from unstructured documentation, then comprehensive information capture is achieved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex task of extracting data from unstructured documentation into multiple manageable components. The system divides the process into distinct stages: data ingestion, preprocessing, entity recognition, relationship extraction, and code generation. This segmentation reduces processing complexity while achieving comprehensive information capture from unstructured sources.
Solution Approach 2:
The patent transforms unstructured documentation into structured data by changing the parameters and format of the information. The system applies natural language processing to convert free-text narratives into standardized data elements with defined parameters, making the data more manageable and reducing processing complexity while capturing comprehensive information.
Data Source
AI summary
A method is described herein that comprises receiving scanned documents, wherein the scanned documents comprise unstructured data. The method includes performing optical character recognition of the scanned documents to produce text data for each page of the scanned documents, wherein the text data for each page comprises a sequence of words stored together with their location. The method includes dividing each page of the scanned documents into subsections. The method includes using the text data to identify a structure type of each subsection of a page, wherein the structure type includes at least one of a table and text paragraph. The method includes using the text data to label each subsection of a page with a semantic type, wherein the semantic type defines a context surrounding collection of information in a subsection. The method includes using the text data for each subsection of a page to identify medical concepts.