Clinical Decision Support System Authoring Environment
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Solution Overview
Problem
Medical professionals face challenges in making timely and accurate decisions regarding patient treatment due to the complexity of diseases and the volume of information they need to process, often lacking the time to review all relevant information during a patient encounter.
Innovation Solution
A clinical decision support system (CDS) with an authoring environment that allows for the development of functions using clinical quality language (CQL) programming code, enabling the creation of customized reports and automating lab test ordering based on dynamic values assigned by the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional methods of computer programming are used to design, edit, and update CDS computing systems, then the system can be customized and updated, but the process becomes time consuming and labor intensive
Solution Approach 1:
The patent uses templates as pre-defined copies of CDS logic structures that can be reused across multiple scenarios. Instead of programming each CDS rule from scratch, developers can replicate and adapt existing templates, significantly reducing programming time while maintaining customization capability through template parameterization.
Solution Approach 2:
The system performs preliminary actions by pre-configuring CDS logic templates with common clinical decision-making patterns, data transformation rules, and report generation frameworks during the development phase. This allows rapid deployment and customization during patient encounters without requiring time-consuming programming at the point of care.
2Reliability
If medical providers review all relevant information regarding patient treatment, then decision accuracy improves, but the time required exceeds the limited window of time during patient encounters
Solution Approach 1:
The CDS computing system acts as an intermediary between the voluminous patient information and the medical provider. It automatically processes, analyzes, and synthesizes clinical data using pre-configured logic templates, then presents distilled actionable insights to the provider. This intermediary function maintains decision accuracy by systematically evaluating all relevant information while reducing the time burden on providers through automated processing.
Solution Approach 2:
The system replaces the manual mechanical process of information review with automated computational processing. Instead of providers manually reviewing all patient data, the CDS system uses algorithms and predefined logic to automatically evaluate clinical information, transform data into meaningful metrics, and generate recommendations, thereby maintaining accuracy while dramatically reducing review time.
3Reliability
If the CDS system processes voluminous patient information automatically, then decision support quality improves, but system complexity increases
Solution Approach 1:
The CDS system is segmented into modular components: data ingestion modules, transformation modules using templates, analysis engines, and output generation modules. Each template represents a self-contained functional unit that can be independently configured and maintained. This segmentation manages system complexity by breaking down complex decision support tasks into manageable, reusable components while maintaining high decision support quality through coordinated module execution.
Data Source
AI summary
A method can involve receiving input data relevant to a patient encounter such that the input data may be compliant with a standard for exchanging healthcare information. The method may involve determining a subset of functions from a plurality of functions based on parameters associated with the patient encounter, conditions met by the input data, or a consuming system that received a customized report and for each function in the subset of functions, the method may perform at least one transformation on the input data such that performing the at least one transforming comprises producing an assigned dynamic value from among the plurality of dynamic values associated with the function. The method may involve automatically generating the customized report for the patient encounter based on the assigned dynamic values from each function in the subset of functions and transmitting an order request to order a lab test.