Automated Clinical Knowledge Integration in Patient Records
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Solution Overview
Problem
Clinicians face challenges in uniformly disseminating and integrating new clinical knowledge into their practice habits, leading to potential gaps in patient care due to time constraints and human error in reviewing and relating healthcare information to individual patient plans.
Innovation Solution
A computerized system generates executable code based on clinical knowledge, which analyzes patient records to identify relevant matches, facilitating automated task lists and ensuring timely dissemination of standard care guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clinicians manually review clinical knowledge sources and relate information to patient records, then they can integrate clinical knowledge into practice, but it is time-consuming and subject to human error
Solution Approach 1:
The patent replaces manual mechanical review processes with automated computer systems. The system automatically retrieves clinical knowledge from sources, analyzes patient records using algorithms, and generates alerts without human intervention, thereby eliminating time consumption and human error while maintaining reliability
Solution Approach 2:
The system performs self-service by automatically monitoring clinical knowledge sources, self-updating its knowledge base, and autonomously analyzing patient records to identify relevant information. This eliminates the need for clinicians to manually review sources and reduces reliance on human judgment
2Productivity
If clinicians use automated email updates to receive clinical knowledge, then dissemination is efficient, but clinicians cannot reliably review and retain all information
Solution Approach 1:
The patent introduces an intermediary system between clinical knowledge sources and clinicians. This automated intermediary retrieves knowledge, analyzes patient records, and presents only relevant information to clinicians through structured alerts, thereby maintaining high dissemination speed while improving retention through targeted delivery
Solution Approach 2:
The system extracts only the most relevant clinical knowledge information from large volumes of available data. By using algorithms to filter and select pertinent information based on patient characteristics and clinical guidelines, the system delivers condensed, actionable insights rather than overwhelming clinicians with all available information
3Quantity of substance
If an assistant manually pulls patient records to review clinical knowledge, then comprehensive coverage is achieved, but it is very time-consuming and open to human error
Solution Approach 1:
The patent replaces manual record retrieval and analysis with automated computer systems that electronically access patient records, apply clinical knowledge algorithms, and generate alerts instantly. This substitution eliminates the time-consuming manual process while maintaining comprehensive coverage of all patient records
Solution Approach 2:
The system provides continuous automated monitoring of patient records against clinical knowledge bases. Rather than periodic manual reviews, the system continuously analyzes records in real-time, ensuring no information is missed and eliminating downtime associated with manual review cycles
Data Source
AI summary
A system and associated methods provide healthcare entities with distributed analysis capabilities for records of a patient population. The analysis seeks to find matches between a piece of clinical knowledge introduced to a healthcare entity and data contained in the patient population records. According to one method, a service generates executable code based on the piece of clinical knowledge. Through a communication with the service, the healthcare entity makes a determination as to what extent the clinical knowledge has relevance to a patient population of the healthcare entity. Based on this determination, the healthcare entity downloads at least a portion of the executable code. Then, the records of the patient population may be analyzed by the executable code to register matches of the clinical knowledge with data contained within the records. System activity may then be initiated based on the matches registered.