Abstract
Gout is a chronic systemic disease characterized by the deposition of monosodium urate crystals in various tissues and inflammation. In Russia, time to diagnosis may be as long as 8 years. This leads to serious complications, such as urate nephropathy, and disability. Effective strategies are needed to improve the quality of medical care for gout patients. One of such strategies is creation of an expert system to aid the clinician in establishing the diagnosis and selecting adequate therapy. The cornerstone of an expert system is a knowledge base. The aim of this paper was to develop a medical nomenclature and algorithms for the diagnosis and treatment of gout that will be used to create an expert system in the future. A total of 1,174 entities were selected that laid the basis for 40 diagnostic and 50 treatment algorithms for gout patients. All informational models were verified by the expert panel.
Highlights
Gout is a chronic systemic disease characterized by the deposition of monosodium urate crystals in various tissues and inflammation
Federal clinical guidelines approved by the Russian Ministry of Health [1] were used as the main source of data for creating a medical nomenclature and diagnostic/treatment algorithms for gout
Eligibility criteria for experts participating in the development of an expert system or a similar product are not explicitly specified in Russian normative documents, so the selection was based on the formal indicators of expertise, including academic credentials, position held, and over 8 years of experience in rheumatology
Summary
Gout is a chronic systemic disease characterized by the deposition of monosodium urate crystals in various tissues and inflammation. In Russia, time to diagnosis may be as long as 8 years. This leads to serious complications, such as urate nephropathy, and disability. Effective strategies are needed to improve the quality of medical care for gout patients. One of such strategies is creation of an expert system to aid the clinician in establishing the diagnosis and selecting adequate therapy. The aim of this paper was to develop a medical nomenclature and algorithms for the diagnosis and treatment of gout that will be used to create an expert system in the future.
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