Dynamic Data Analysis for Multivariate Diagnostic Interpretation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In modern medicine, diagnosing a patient's condition often relies on integrating multiple laboratory results, where abnormal results that don't fit a standard pattern pose a challenge, leading to unnecessary and expensive testing cycles, especially in genetic and multifactorial conditions.
Innovation Solution
A computer-implemented system that provides an integrated interpretation of multiple laboratory results by comparing a patient's data to a database of other patients with established conditions, using multivariate pattern recognition techniques to generate scores and plots, allowing physicians to rank patients relative to known conditions and improve diagnostic efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard cut-off values are used to interpret laboratory results, then diagnostic simplicity is maintained, but diagnostic accuracy deteriorates when results do not fit standard patterns
Solution Approach 1:
The system dynamically adjusts reference ranges based on patient-specific factors such as genetic makeup, family history, and clinical presentation. Instead of using fixed cut-off values, the system continuously adapts the interpretation criteria to match the individual patient's profile, thereby improving diagnostic accuracy without requiring physicians to manually adjust complex parameters.
Solution Approach 2:
The system changes the interpretation parameters from static cut-off values to dynamic, multi-dimensional parameters that incorporate genetic data, family history, and clinical presentation. This allows the system to accurately interpret atypical results by comparing them against customized reference ranges specific to each patient's genetic and clinical profile.
2Reliability
If additional tests are ordered to verify abnormal results, then diagnostic reliability is improved, but loss of time and increased cost occur
Solution Approach 1:
The system performs preliminary analysis by integrating multiple data sources (genetic data, family history, clinical presentation) before interpreting laboratory results. This preliminary action creates a customized diagnostic framework that reduces the need for additional verification tests, as the system has already contextualized the abnormal result within the patient's overall profile.
Solution Approach 2:
The system provides immediate feedback by comparing abnormal results against customized reference ranges and providing an integrated interpretation. This feedback mechanism reduces diagnostic uncertainty without requiring additional testing cycles, as physicians receive contextualized information that directly addresses the clinical question.
3Loss of information
If multiple laboratory results are integrated using traditional methods, then comprehensive diagnosis is achieved, but information overload occurs making interpretation difficult
Solution Approach 1:
The system merges multiple data sources (genetic data, family history, clinical presentation, and laboratory results) into a unified interpretation framework. By combining these elements and comparing them against integrated reference ranges, the system provides a consolidated diagnostic assessment that reduces information overload while maintaining comprehensive analysis.
Solution Approach 2:
The system acts as an intermediary between raw laboratory data and clinical interpretation. It processes multiple laboratory results and patient-specific factors through algorithmic analysis, transforming complex multi-dimensional data into a simplified integrated interpretation that guides clinical decision-making without overwhelming the physician.
Data Source
AI summary
A computer-implemented method comprises storing data that aggregates statuses, uploaded by healthcare providers, of a plurality of patients for a plurality of medical conditions; receiving via a remote upload from a healthcare provider data that characterizes the status of the particular patient with respect to at least some of the plurality of medical conditions; providing for review by the healthcare provider data that graphs a plotted location that is indicative of the particular patient's values in a common graph with locations that are indicative of the other patients' values, and that highlights the particular patient's values relative to the other patients' values; and adding data for conditions of the particular patient to a database that aggregates the statuses of the plurality of patients.