Round-Robin Database for Rapid Physiological Data Retrieval
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Solution Overview
Problem
Current patient monitoring systems face challenges in efficiently storing and retrieving physiological trend data and analyzing medical events across multiple patient monitoring devices, leading to delays in clinical decision-making and suboptimal hospital performance.
Innovation Solution
A clinical network environment utilizing a round-robin database (RRDB) for storing physiological data and a journal database for recording medical events, allowing for rapid data acquisition and analysis of trends and events, with dynamic parameter descriptors and adjustable data storage based on patient acuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a traditional database system is used to store physiological data from multiple patient monitors, then data storage is achieved, but data retrieval speed and analysis efficiency are slowed down
Solution Approach 1:
The database is segmented into multiple specialized components: a round-robin database for time-series physiological data storage with fixed-size circular buffers for rapid retrieval, and an event database for discrete medical events. This segmentation allows each component to be optimized for its specific function, enabling fast data access without requiring complex full-database queries.
Solution Approach 2:
The system dynamically adjusts data retention policies and database buffer sizes based on patient acuity levels and clinical priorities. Critical physiological parameters maintain larger circular buffers for extended retention, while non-critical parameters use smaller buffers. This dynamic adaptation optimizes retrieval speed for important data without uniformly increasing database complexity across all parameters.
2Productivity
If physiological data from multiple devices is centralized for analysis, then comprehensive monitoring is achieved, but data processing time and system response delay increase
Solution Approach 1:
The round-robin database pre-organizes physiological data into fixed-size circular buffers with predetermined retention periods, allowing clinicians to quickly retrieve historical trends without complex processing. Event databases pre-log critical occurrences as they happen, enabling immediate alert generation and clinician response without delayed batch processing.
Solution Approach 2:
Different data processing and retrieval strategies are applied to different types of physiological data based on their clinical importance. Critical parameters (e.g., heart rate, blood pressure) use high-priority queues and shorter retention buffers for rapid access, while non-critical parameters use standard processing. This localized optimization reduces overall processing time by focusing computational resources on clinically essential data.
Data Source
AI summary
Physiological information can be stored in a dynamic round-robin database. Parameter descriptors may be used to identify parameter values in the records. The parameter values can be dynamically updated by changing the parameter descriptors. In addition, the size of files used in the database can be dynamically adjusted to account for patient condition. In certain implementations, the round-robin database can be adaptive, such that an amount of data stored in the database is adapted based on patient condition and/or signal condition. Additionally, medical data obtained from a clinical network of physiological monitors can be stored in a journal database. The medical data can include device events that occurred in response to clinician interactions with one or more medical devices and device-initiated events, such as alarms and the like. The journal database can be analyzed to derive statistics, which may be used to improve clinician and/or hospital performance.