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Goal-driven management of interacting clinical guidelines for multimorbidity patients
Alexandra Kogan1, Samson W Tu2, Mor Peleg1
1University of Haifa, Haifa, Israel.
Insights
This study introduces a goal-based method for integrating multiple computer-interpretable guidelines (CIGs) to manage patients with multiple chronic conditions. It effectively detects and resolves therapeutic inconsistencies for improved clinical decision support.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Medicine
- Clinical Decision Support Systems
Background:
- Clinical practice guidelines (CPGs) typically address single morbidities, posing challenges for managing aging populations with multiple chronic conditions.
- Existing computer-interpretable guidelines (CIGs) lack effective mechanisms for integrating recommendations from multiple sources.
- Managing polypharmacy and comorbidities requires advanced decision support that considers complex drug-disease and drug-drug interactions.
Purpose of the Study:
- To develop and demonstrate a novel goal-based method for integrating multiple CIGs.
- To create a system capable of detecting and resolving therapeutic inconsistencies arising from combined guideline recommendations.
- To leverage knowledge of drug effects and therapeutic usage for automated clinical decision support.
Main Methods:
- Utilized a goal-based approach incorporating drug physiological effects and therapeutic knowledge.
- Developed an algorithm employing pattern matching for consistency checking and event response.
- Integrated existing standards: Fast Healthcare Interoperability Resources (FHIR), SNOMED-CT, and PROforma CIG formalism.
- Employed Alium knowledge-engineering environment and PROforma enactment engine for guideline execution.
Main Results:
- Demonstrated automatic detection and resolution of therapeutic inconsistencies between two CIGs.
- Successfully managed a case study involving a new goal and a conflicting medication request.
- Validated the system's ability to plan non-contradicting therapies based on integrated CIG knowledge.
Conclusions:
- The proposed goal-based method offers an effective mechanism for integrating multiple CIGs.
- This approach enhances clinical decision support for patients with multiple morbidities.
- The system provides a foundation for automated, safe, and consistent therapeutic planning in complex patient cases.
Abstract:
Computer-interpretable guidelines (CIGs) are based on clinical practice guidelines, which typically address a single morbidity. However, most of the aging population suffers from multiple morbidities. Currently, there is no demonstrated effective mechanism that integrates recommendations from multiple CIGs. We are developing a goal-based method that utilizes knowledge of drugs' physiological effects and therapeutic usage to combine knowledge from CIGs. It incrementally detects interactions and plans non-contradicting therapies. Our algorithm uses patterns to check consistency and respond to events, including data enquiries, diagnoses, adverse events, recommended medications, tests, and goals. Our method utilizes existing standards and CIG tools, including the Fast Healthcare Interoperability Resources (FHIR) patient data model, SNOMED-CT, and the PROforma CIG formalism with its Alium knowledge-engineering environment and PROforma enactment engine. We demonstrate our approach using a case study involving two clinical guidelines with templates for responding to a new goal and to a medication request that causes an inconsistency which can be automatically detected and resolved based on the knowledge of the two CIGs.
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