Clinical Decision Matrix for Post-Cardiac Treatment Selection
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Solution Overview
Problem
Current clinical practices for selecting post-cardiac event treatment regimens for patients with coronary artery disease lack data on health status outcomes, excluding patient participation and failing to provide informed decision-making tools that consider individual patient characteristics and desired health goals.
Innovation Solution
A method and software system that creates a disease-specific database and decision matrix to assess and select the most appropriate revascularization procedure for patients, incorporating patient-specific health status parameters and demographic data to provide tailored treatment options with relative risk and success outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If treatment selection is based solely on technical considerations and procedural risks, then procedural safety is improved, but patient participation and informed decision-making are worsened
Solution Approach 1:
The decision-making process is segmented into multiple components: procedural risk assessment, health status outcome prediction, and patient preference evaluation. This allows each aspect to be addressed independently while integrating them into a comprehensive decision framework that includes patient participation.
Solution Approach 2:
A computer-based decision support system serves as an intermediary between medical professionals and patients. This system provides objective data on procedural risks and health status outcomes while facilitating patient participation through personalized presentation of treatment options and outcomes.
2Ease of operation
If treatment decisions are made without health status outcome data, then decision-making simplicity is maintained, but quality of life optimization is worsened
Solution Approach 1:
The system replaces manual calculation and judgment with automated computer-based analysis. The software processes patient data, retrieves health status outcome data from databases, and generates personalized treatment recommendations, making complex data processing accessible and simple for users while optimizing quality of life outcomes.
Solution Approach 2:
The system transforms raw health status data into clinically meaningful parameters and predictions. By processing multiple variables (age, comorbidities, procedural risks) and transforming them into predicted health status outcomes, the system provides precise quality of life optimization while maintaining ease of use through automated parameter transformation.
3Adaptability or versatility
If personalized treatment options are provided based on individual patient characteristics, then treatment customization is improved, but data requirements and system complexity are worsened
Solution Approach 1:
The decision support system is designed with universal functionality to handle multiple patient characteristics and treatment options. A single integrated system processes diverse data types (demographics, clinical parameters, procedural risks) and generates personalized recommendations, reducing overall system complexity through multi-functional design.
Solution Approach 2:
The system performs preliminary data processing and outcome prediction before the actual treatment decision-making process. By pre-calculating health status outcomes and preparing personalized treatment options in advance, the system reduces real-time computational complexity while maintaining high treatment customization levels.
4Reliability
If health status data from clinical trials is utilized, then evidence-based decision-making is improved, but data availability and applicability to individual patients are worsened
Solution Approach 1:
The system applies general health status outcome data from clinical trials to specific individual patients by localizing the information. It retrieves applicable outcome data from databases and customizes it to match the individual patient's characteristics, thereby maintaining evidence-based decision-making while improving data applicability to specific patients.
Solution Approach 2:
The system creates virtual copies of clinical trial data that can be matched to individual patient profiles. By copying and adapting trial outcome data to match specific patient characteristics, the system maintains the reliability of evidence-based decisions while improving the applicability of data to individual patients through data matching and adaptation.
Data Source
AI summary
The invention discloses a method by which the health care professional or patient may draw upon historical medical data concerning patients similarly situated in medical condition, to assist him/her in deciding on a clinical intervention procedure to select. This method is specifically tailored to the patient, as data is provided and evaluated from only similarly situated patients, and provides an expectation of potential outcome of the patient should one or the other of the options be selected. The invention further provides a database that may be used in order to provide this comparison based evaluation method. A computer based software system is further disclosed that implements the method. The invention more speiocifically provides a method by which a post-coronary event patient may make an informed decision of which post-coronary revascularization procedure to undergo in the future management of his disease. This method employs the patient's health status date (symptoms, function and quality of life), and provides projections of the patient's expected survival, risk, and 1-year health status outcome from the selection of revascularization procedure, such as Coronary Artery Bypass Grafting (CABG) or Percutaneous Coronary Intervention (PCI).