Healthcare Event Data Correlation for Adverse Outcome Disruption
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Solution Overview
Problem
In healthcare settings, existing systems lack the capability to effectively identify and disrupt undesirable outcomes that can harm patients, such as adverse events, by correlating event and action data to inform interventions.
Innovation Solution
A system comprising computer-readable media, electronic input and output devices, and a processor that automatically stores and analyzes event data, determines critical events, and displays interventions to prevent future adverse occurrences, with the ability to iteratively refine interventions based on outcome data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems are used in healthcare settings, then basic patient monitoring is maintained, but the capability to identify and disrupt undesirable outcomes is insufficient
Solution Approach 1:
The system segments healthcare data into distinct types (event data, action data, outcome data) and processes each separately before integrating them for correlation analysis. This segmentation allows for specialized handling of each data type while maintaining their relationships for identifying undesirable outcomes.
Solution Approach 2:
The system implements nested data structures where event data, action data, and outcome data are organized in hierarchical relationships. Outcome data contains references to related event data, creating a nested structure that preserves the causal relationships while enabling efficient querying and analysis.
2Reliability
If comprehensive data analysis is performed to identify critical events, then patient safety is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-defining criteria for what constitutes a critical event and pre-establishing correlation rules between different data types. This preliminary configuration reduces the complexity of real-time analysis by having decision logic prepared in advance.
Solution Approach 2:
The system introduces an intermediary correlation engine that acts as a mediator between raw data collection and critical event identification. This intermediary layer processes and correlates data according to predefined rules, simplifying the overall system architecture by separating data collection from analysis.
3Object-affected harmful factors
If interventions are implemented based on correlated data, then harmful outcomes are reduced, but the need for continuous monitoring and refinement increases
Solution Approach 1:
The system implements feedback loops where outcomes of interventions are monitored and fed back into the correlation analysis. This feedback mechanism allows the system to learn from past interventions and refine future recommendations, reducing harmful outcomes over time while maintaining continuous improvement.
Solution Approach 2:
The system provides self-service capabilities by automatically generating intervention recommendations based on correlated data without requiring constant manual analysis. The automated nature of the system reduces the burden of continuous monitoring while maintaining effectiveness in preventing harmful outcomes.
Data Source
AI summary
Systems and methods set forth herein may aid in identifying and disrupting undesirable outcomes. The system may include computer readable media; an electronic input device; an output device; a processor in data communication with the input device and the output device; and electronic instructions. The electronic instructions, when executed by the at least one processor, perform steps for: automatically storing event data from the electronic input device in the computer readable media; actuating the output device to graphically display the event data; determining if the event data qualifies as a critical event; storing the critical event data as outcome data in the computer readable media; accessing the event data and outcome data; determining at least one correlation between the event data and the outcome data; actuating the output device to display an intervention to aid in disrupting future critical events; and storing the intervention in the computer readable media.