Clinical Decision Support System for Therapy Ranking
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Solution Overview
Problem
Clinicians face difficulties in selecting appropriate therapies for patients, especially in cancer treatment, due to the vast number of clinical trials and varying therapeutic options, with existing clinical guidelines offering limited assistance when standard care paths are unclear.
Innovation Solution
A clinical decision support system that establishes a patient profile, identifies relevant clinical trials based on inclusion criteria, ranks therapies according to clinical benefits, and outputs a list of suitable options, incorporating weighting scores for satisfaction, outcome, and side effects, while grouping therapies by biological similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If clinical guidelines and pathways are used to select therapies, then decision-making is aided, but they offer little assistance when standard care paths are unclear or non-existent
Solution Approach 1:
The patent introduces a computer system as an intermediary between clinical guidelines and clinicians. This system automatically searches, filters, and ranks clinical trials based on patient characteristics, bridging the gap between rigid guidelines and complex real-world cases where standard paths don't exist.
Solution Approach 2:
The patent replaces the manual mechanical process of clinicians searching through numerous clinical trials with an automated computer-based system. The computer systematically queries databases, applies inclusion/exclusion criteria, and ranks therapies, eliminating the time-consuming manual search process.
2Adaptability or versatility
If clinicians rely on their own knowledge or scientific literature to select therapies, then flexibility is maintained, but the process is time-consuming and may miss available options
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing clinical trial data into searchable databases with defined inclusion and exclusion criteria. When a clinician inputs patient characteristics, the system has already prepared the data for rapid retrieval and ranking, eliminating the need for clinicians to manually search literature.
Solution Approach 2:
The system provides feedback to clinicians by ranking therapies based on how well they match patient characteristics and trial criteria. This automated feedback loop helps clinicians quickly identify the most suitable options without manually evaluating each trial's applicability.
3Adaptability or versatility
If multiple clinical trials with various therapy combinations are conducted, then therapeutic options increase, but it becomes difficult for clinicians to remain aware of suitable options
Solution Approach 1:
The system extracts relevant information from numerous clinical trials by automatically applying inclusion and exclusion criteria to filter out inappropriate options. It extracts only the therapies that match specific patient characteristics, presenting a manageable subset rather than the complete universe of available trials.
Solution Approach 2:
The system changes parameters by allowing clinicians to adjust patient characteristics and trial criteria dynamically. By modifying input parameters (patient age, condition, previous treatments), the system automatically re-ranks therapies to reflect changed conditions, keeping clinicians informed of suitable options as circumstances evolve.
Data Source
AI summary
A clinical decision support system (100) is disclosed. The clinical decision support system (100) comprises a processor (102) configured to: establish a subject profile for defining a subject according to a plurality of features; identify a set of clinical trials that are relevant to the subject, wherein each clinical trial in the set of relevant clinical trials corresponds to at least one therapy has inclusion criteria that are satisfied by features of the plurality of features of the subject profile; rank the therapies corresponding to the relevant clinical trials; and output the ranking of the therapies for the subject. A method and a computer program product are also disclosed.