Dynamic Clinical Workflow Automation for Healthcare Data Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Extracting information from a wide variety of data sources in healthcare workflows is difficult, time-consuming, and prone to errors due to the diverse forms of data, including structured and unstructured sources, which require complex processing to generate bills and perform other workflows.
Innovation Solution
A computer system implements dynamic, data-driven workflows by defining data extraction and processing steps from various sources, using trigger conditions to automate the extraction and processing of data, and applying Natural Language Processing (NLP) for unstructured data, enabling the generation of structured data for use in workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual extraction methods are used for data from multiple sources, then flexibility in handling diverse data formats is maintained, but time consumption and error rates increase significantly
Solution Approach 1:
The system enables self-service automation where the workflow engine automatically extracts data from multiple sources using predefined templates and triggers, eliminating the need for manual intervention while maintaining high accuracy through structured extraction rules and validation mechanisms
Solution Approach 2:
The system changes the parameters of data extraction by transforming unstructured and semi-structured data into standardized structured formats using configurable templates, allowing automated processing while maintaining data quality through parameter mapping and validation
2Productivity
If automated data extraction is implemented across diverse data sources, then productivity and accuracy improve, but system complexity increases due to handling structured and unstructured data
Solution Approach 1:
The system segments the complex data extraction process into modular components including trigger condition evaluation, data source identification, template-based extraction, and result validation, allowing each component to be independently configured and managed, thus reducing overall system complexity
Solution Approach 2:
The workflow engine provides universal functionality by handling multiple data sources (EHR, billing systems, insurance databases) and various data formats (structured, semi-structured, unstructured) through a single unified platform with configurable templates, eliminating the need for separate specialized systems
3Loss of information
If comprehensive data is extracted from all available sources, then completeness of workflow information is improved, but the time and resources required for processing increase
Solution Approach 1:
The system performs preliminary action by pre-defining extraction templates and trigger conditions that identify only the necessary data elements required for specific workflows, allowing the system to extract only relevant information rather than processing all available data, thus maintaining completeness while reducing processing time
Data Source
AI summary
Embodiments of the present invention are directed to computer systems for implementing dynamic, data-driven workflows within healthcare and other environments. Such a system may include a computer-processable definition of one or more workflows. Each workflow definition may define various aspects of the corresponding workflow, such as the data required by the workflow, a process for extracting such data from a variety of structured and/or unstructured data sources, a set of process steps to be performed within the workflow, and a condition for triggering the workflow. The system may use the workflow definition to extract the data required by the workflow and to perform the workflow's process steps on the extracted data in response to determining that the workflow's trigger condition has been satisfied. The workflow may change in response to changes in data extracted by the workflow.