Glycemic Care Reporting With Real-Time Patient Alert Filtering
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Solution Overview
Problem
Conventional glycemic management in hospitals is hindered by discarded initial glucose measurements, improper insulin administration, retrospective analysis, and delayed protocol changes, leading to uncontrolled blood sugar issues with significant clinical and financial impacts.
Innovation Solution
A system comprising an input module for collecting patient data, a comparison module for filtering relevant data, a report module for generating actionable reports, and an alert module for timely intervention, enabling real-time analysis and prompt healthcare provider alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If initial glucose measurements are discarded, then the analysis of organizational glycemic management performance is improved, but loss of information occurs
Solution Approach 1:
The patent segments the glucose measurement data into different time periods (pre-admission, post-admission) and applies different analysis methods to each segment. Initial measurements are separated from ongoing measurements, allowing the organization to exclude biased pre-admission data while still capturing relevant post-admission glycemic management performance.
Solution Approach 2:
The patent establishes preliminary filtering criteria and classification rules before analyzing the data. By pre-defining which measurements to include or exclude based on timing and patient characteristics, the system automatically removes biased initial measurements while preserving relevant data, eliminating the need to discard all initial measurements.
2Measurement precision
If retrospective analysis is used with at least one month of data collection, then analysis accuracy is improved, but loss of time occurs
Solution Approach 1:
The patent implements periodic analysis at multiple time intervals (e.g., daily, weekly, monthly) rather than waiting for a full month of data accumulation. This allows the organization to perform retrospective analysis on shorter timeframes while still maintaining statistical validity, enabling faster detection and response to glycemic management issues.
Solution Approach 2:
The patent makes the analysis timeframe dynamic rather than fixed at one month. The system can adjust the data collection period based on patient acuity, clinical needs, and organizational goals, allowing for accelerated analysis when rapid response is needed while maintaining accuracy when using longer periods.
3Manufacturing precision
If at least one month delay is used to implement protocol changes, then thorough analysis is improved, but productivity deteriorates
Solution Approach 1:
The patent establishes preliminary protocols and decision rules in advance that trigger automatic or semi-automatic protocol changes when specific glycemic management thresholds are exceeded. By pre-defining action thresholds and response protocols, the system eliminates the need for lengthy deliberation periods while maintaining thorough, evidence-based decision-making.
Solution Approach 2:
The patent implements continuous feedback loops where glycemic management data is continuously monitored, analyzed, and used to trigger protocol adjustments in near-real-time. This closed-loop system allows for rapid protocol changes based on current performance data, eliminating the one-month delay while ensuring changes are based on thorough analysis of trends and patterns.
Data Source
AI summary
Described is a system comprising an input module receiving a first data set indicative of at least one patient condition for each of a plurality of patients obtained during a predetermined time period, a comparison module comparing each of the at least one patient condition to at least one filter criteria, a filter module selecting a patient to include in a second data set if the at least one patient condition of the patient satisfies the at least one filter criteria, a report module generating a report based on the second data set, wherein the report includes at least one patient identifier for each patient in the second data set and at least one descriptor of the at least one patient condition for each patient in the second data set, and a classification module storing at least one classification value for the at least one patient condition.