Differential Diagnosis Apparatus for Optimal Test Sequencing
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Solution Overview
Problem
Current medical diagnosis support systems fail to provide an optimal sequence of diagnostic tests based on appropriateness criteria, leading to issues such as overuse or underuse of tests, which can result in increased costs and negative health effects for patients, while also neglecting factors like timeliness, safety, and patient centrality.
Innovation Solution
A medical diagnosis apparatus that determines an optimal sequence of diagnostic tests by processing patient data and appropriateness indexes, using an inferential engine to compute probabilities and recommend diagnostic paths that are cost-effective, efficient, and safe, integrating a knowledge base with epidemiological data and international guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the physician prescribes multiple diagnostic tests to ensure accurate diagnosis, then diagnostic accuracy is improved, but diagnostic cost and patient burden increase
Solution Approach 1:
The system performs preliminary evaluation of diagnostic hypotheses and test appropriateness before the physician finalizes the diagnostic path. By pre-calculating which tests are most appropriate for each hypothesis and evaluating their expected impact on diagnostic accuracy, the system enables the physician to make informed decisions about test selection, avoiding unnecessary tests while maintaining diagnostic accuracy.
Solution Approach 2:
The system provides feedback to the physician by presenting diagnostic hypotheses ranked by probability and indicating which tests would be most appropriate for each hypothesis. This feedback loop allows the physician to see the potential impact of each test on refining the differential diagnosis, enabling optimization of the diagnostic path to achieve accurate diagnosis with minimal necessary tests.
2Adaptability or versatility
If the physician selects from a broad offer of diagnostic tests, then diagnostic coverage is improved, but decision complexity increases
Solution Approach 1:
The system applies local quality by providing customized, context-specific recommendations for each diagnostic hypothesis rather than a generic list of all available tests. For each hypothesis, the system identifies and presents only the tests that are most appropriate and have the greatest expected impact, filtering out irrelevant options. This localized information presentation reduces decision complexity while maintaining comprehensive diagnostic coverage.
Solution Approach 2:
The system segments the broad offer of diagnostic tests into organized groups associated with each diagnostic hypothesis. By presenting tests in this structured manner rather than as a single undifferentiated list, the system makes the information more manageable and easier to process, reducing the cognitive burden on the physician while maintaining access to the full range of diagnostic options.
3Reliability
If the physician performs redundant diagnostic tests, then diagnostic thoroughness is improved, but resource efficiency deteriorates
Solution Approach 1:
The system applies partial action by recommending only the necessary subset of tests required to adequately evaluate each diagnostic hypothesis, rather than all possible tests. By calculating the expected impact of each test on diagnostic accuracy and selecting only those with sufficient impact, the system achieves thorough diagnostic evaluation with minimal necessary testing, avoiding redundant procedures while maintaining diagnostic reliability.
Data Source
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AI summary
A differential diagnosis apparatus is described, which is adapted for medical applications in order to determine an optimal sequence of diagnostic tests for identifying a pathology by adopting diagnostic appropriateness criteria, comprising: - a first updatable database containing patients' data; - a second relational database containing identification data of pathologies, symptoms, clinical signs, identification data of diagnostic tests, and data relating to the appropriateness parameters of said diagnostic tests for defining a list of diagnostic hypotheses (pathologies); - means adapted to determine said optimal sequence of diagnostic tests for identifying a pathology, said means comprising an inferential computation engine, which determine for each diagnostic hypothesis (pathology), based on data contained in said first and second databases, said optimal sequence of diagnostic tests with associated indices of appropriateness and probability that a patient is suffering from that pathology.