Behavioral Health Workflow Engine for Automated Test Ordering
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Solution Overview
Problem
Current mental health testing methods are time-consuming and inefficient, leading to delayed diagnoses and inadequate resource allocation, particularly in healthcare facilities, due to incomplete patient data organization and a lack of automated decision-making systems that consider multiple factors for behavioral health conditions.
Innovation Solution
A computer-implemented method utilizing a layered decision-making system with a logic-based workflow engine, EHR interface, machine learning model, and patient communication interface to automate test ordering and analysis, leveraging machine learning to determine condition probabilities and improve diagnostic efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual testing methods are used for behavioral health assessment, then providers can conduct comprehensive evaluations, but the process becomes time-consuming and inefficient leading to delayed diagnoses
Solution Approach 1:
The system performs preliminary data organization and analysis by automatically collecting patient data from multiple sources (EHR, demographics, test history) and pre-processing it before the actual diagnostic decision is needed. This preliminary action includes structuring unorganized patient data and preparing it for rapid analysis, thereby reducing the time required for actual diagnosis without compromising comprehensiveness
Solution Approach 2:
The patent replaces manual mechanical processes of data collection, organization, and analysis with an automated computer-based system. The system automatically retrieves patient data from multiple sources, organizes it according to predefined structures, and presents it for diagnostic evaluation, eliminating the time-consuming manual work while maintaining diagnostic quality
2Measurement precision
If comprehensive patient data is collected from multiple sources to improve diagnostic accuracy, then more factors can be considered for testing decisions, but data organization becomes incomplete and difficult to manage
Solution Approach 1:
The system segments patient data into distinct categories and sources (demographics, EHR data, test history, family history) and processes each segment separately through dedicated data collection modules. This segmentation allows comprehensive data gathering while maintaining organizational simplicity, as each data type is handled by specialized routines that structure and validate their specific portions before integration
Solution Approach 2:
The patent introduces an intermediary data structure and processing layer that sits between raw data sources and diagnostic decision-making. This intermediary automatically standardizes, validates, and organizes data from multiple sources into a unified format, reducing the complexity of managing diverse data while ensuring complete and accurate information is available for diagnosis
3Productivity
If automated systems are implemented to improve efficiency, then resource allocation can be optimized, but the system may lack the ability to interpret context and make informed decisions
Solution Approach 1:
The system incorporates feedback loops where diagnostic outcomes and test results are fed back into the data repository, allowing the system to learn from past decisions and improve future recommendations. This feedback mechanism enables the automated system to adapt to contextual nuances by continuously refining its decision-making algorithms based on accumulated experience and patterns in the data
Solution Approach 2:
The patent implements parameter changes by allowing the system to adjust testing recommendations based on varying patient parameters and risk factors. The automated system can modify test selection, frequency, and type based on dynamic parameters such as patient age, symptom severity, family history, and previous test results, thereby achieving both efficiency and contextual adaptability
Data Source
AI summary
Disclosed herein is a computer implemented method for managing healthcare diagnosis and treatment. The method includes the steps of monitoring at least one data source for a workflow trigger comprising at least one of an order, a test result, an appointment, a patient demographic, a patient status, a patient history, a patient communication, or a condition; and triggering a workflow upon the detection of a workflow trigger. The workflow comprises a first decision-making layer configured to manage at least one of a rule, a patient test, and a patient communication; a second decision-making layer configured to manage at least one workflow, wherein the workflow comprises at least one rule; and a third decision-making layer configured to manage at least one machine learning model, wherein the machine learning model is configured to process data relevant to the workflow and to determine a probability of a condition to be tested.