Automated EHR Document Indexing via Classification Engine
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Solution Overview
Problem
Conventional Electronic Health Record (EHR) systems lack efficient interfaces for importing and indexing physical and electronic documents, leading to inaccuracies and inefficiencies due to manual processing by healthcare employees, which can result in incorrect document matching and classification.
Innovation Solution
A web-based system that uses a client computing device to receive documents, determine their type, and transmit them to a server for automatic classification and indexing, matching them with patient data in an EHR system, thereby streamlining the document importation process and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual document processing is used by healthcare employees, then flexibility and adaptability are maintained, but accuracy decreases and errors increase
Solution Approach 1:
The patent introduces an automated document processing system that acts as an intermediary between document import and EHR system integration. This system uses machine learning algorithms and natural language processing to automatically classify, index, and match documents with patient records, eliminating manual processing errors while maintaining accuracy through automated intelligence.
Solution Approach 2:
The patent replaces the mechanical manual processing system with an automated computational system. Machine learning models and algorithms substitute human employees in the document classification, indexing, and matching tasks, thereby improving accuracy while reducing human error and increasing efficiency.
2Productivity
If manual document processing is used, then system complexity remains low, but productivity decreases and time consumption increases
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models with extensive healthcare document datasets before deployment. The system performs preliminary document classification, indexing, and patient matching automatically upon import, eliminating the need for sequential manual processing and significantly increasing productivity.
Solution Approach 2:
The automated system performs self-service by independently classifying, indexing, and matching documents without human intervention. The machine learning algorithms automatically learn from data patterns and make decisions, enabling the system to serve itself in processing documents at high speed while managing its own complexity through automated architecture.
3Reliability
If automated processing is implemented, then productivity increases, but reliability may decrease due to system errors
Solution Approach 1:
The patent incorporates feedback mechanisms where the automated system continuously learns from processing outcomes and corrects its own errors. Machine learning models are retrained using feedback from classified documents and matching results, improving classification accuracy over time while maintaining high processing throughput through iterative optimization.
Data Source
AI summary
A system includes one or more processors to receive a representation of a document from a client computing device, the document comprising one of a scanned document, a faxed document, and an electronic document, determine a document type of the document based at least on the representation of the document, index the document using a classification and index processing engine based on the document type, the document type comprising at least one of a plurality of document types used by an electronic health record (EHR) system, extract index data from the document based on the document type using the classification and index processing engine, and match the document with a patient from a database of the EHR system using the index data when the classification and index processing engine successfully indexes the document and extracts index data from the document.