Automated Patient Testing Workflow for Context-Aware Diagnosis
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Solution Overview
Problem
Current healthcare diagnostic methods, particularly for mental health, are time-consuming and inefficient, often leading to delayed diagnoses due to incomplete data organization and lack of automated, context-aware decision-making, especially in managing patient testing regimens across diverse healthcare facilities.
Innovation Solution
A system utilizing a logic-based workflow engine, machine learning engine, and interfaces for EHR/EMR systems to automate test ordering and execution, leveraging user-defined rules and machine learning models to determine appropriate testing based on patient data and demographics, thereby enhancing decision-making and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used for patient testing and diagnosis, then providers can exercise judgment and flexibility in testing decisions, but the process becomes time-consuming and inefficient with delayed diagnoses
Solution Approach 1:
The system performs preliminary actions by automatically organizing patient data, identifying relevant factors, and generating testing recommendations before providers make final decisions. This includes pre-processing demographic information, family history, past test results, and appointment history to prepare comprehensive patient profiles that guide testing decisions in advance.
Solution Approach 2:
An automated testing recommendation system acts as an intermediary between raw patient data and provider decision-making. This intermediary component processes incomplete or disorganized data, applies clinical guidelines and risk factors, and presents structured testing recommendations to providers, thereby bridging the gap between data availability and diagnostic efficiency.
2Measurement precision
If comprehensive patient data is collected to improve diagnostic accuracy, then more informed decisions can be made, but data organization and processing becomes more complex and resource-intensive
Solution Approach 1:
The system segments comprehensive patient data into distinct categories including demographic information, family history, past test results, appointment history, and risk factors. Each segment is processed and analyzed separately using appropriate methods, then integrated to form complete patient profiles. This segmentation reduces processing complexity while maintaining diagnostic accuracy by allowing specialized handling of each data type.
Solution Approach 2:
The automated testing recommendation system performs multiple functions within a single integrated platform: data collection from various sources, data organization and standardization, risk factor identification, testing recommendation generation, and result tracking. This multi-functional approach consolidates complex data processing tasks into a unified system that handles diverse data types through common processing frameworks.
3Ease of operation
If automated systems are implemented to improve testing consistency, then resource burden on staff is reduced, but the system requires sophisticated integration with EHR/EMR systems and multiple data sources
Solution Approach 1:
The automated testing recommendation system serves as an intermediary layer between existing EHR/EMR systems and testing workflows. It connects to multiple data sources through standardized interfaces, processes information centrally, and delivers recommendations back to providers through familiar interfaces. This intermediary architecture enables automation benefits while working with existing infrastructure rather than requiring complete system replacement.
Solution Approach 2:
The system implements feedback mechanisms where testing recommendations and results are continuously fed back into the patient record and used to refine future recommendations. This feedback loop allows the system to learn from actual testing outcomes and provider decisions, improving accuracy over time while maintaining simple operation for staff who observe continuous improvement in diagnostic consistency.
Data Source
AI summary
A system and method for managing healthcare diagnosis and treatment may comprise interfaces configured to communicate with internal and external data sources, a workflow engine configured to execute rules on the data, and a machine learning engine configured to process the data and provide the workflow engine with a probability of a test condition. The system and method for managing healthcare diagnosis and treatment may determine, order, send, and execute an appropriate test for a patient. The system and method for managing healthcare diagnosis and treatment may process, record, and communicate the test results.