Computer-based Decision Support for Management of Comorbid Diseases

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A clinical practice guideline (CPG) is a knowledge-based tool for disease-specific patient management that encapsulates best practices in deriving at possible diagnoses, and prescribing the most appropriate treatment plan. However, the unmitigated concurrent application of disease-specific guidelines for comorbid patients often has undesired consequences due to the inconsistencies in the guidelines associated with adverse drug-drug or drug-disease interactions. Despite the clinical and social importance of managing patients with comorbid diseases, there is little research on the automatic mitigation of adverse interactions that might be present in concurrently used CPGs. The proposed research program is designed to fill the void. The main goal of the proposed research is to develop methodologies that help to improve the quality of care for patients with comorbid diseases by supporting evidence-based decision-making at the point of care. This translates into research on creating a computable CPG model that: - allows for modeling concurrent application of the guidelines for a patient with comorbid diseases, - is automatically mitigated to address adverse interactions, - is executed at the point of care in an unobtrusive manner. Proposed research has applied and methodological axis. The applied axis is about supporting evidence-based management of patients with comorbid diseases, while the methodological axis is about the methodologies for automatic detection and mitigation of the adverse interactions. Specific aims of the research are defined as: - Automatic construction of computable logical guideline models from the CPG formalisms, - Building constraint logic programming (CLP) model for the multiple CPGs, - Identification of adverse interactions, - Mitigating the adverse interactions, - Proof of concept decision support using clinical case studies.