Objective:The level of discipline construction is an important indicator of a hospital′s comprehensive strength and core competitiveness.Establishing a scientific, reasonable, and effective performance evaluation system for the clinical medicine advantageous disciplines is of particular significance for university affiliated hospitals to strengthen discipline construction, improve governance systems, and promote high-quality development of hospitals.Methods:The Delphi method was used to determine the reliability of the indicator system by measuring the positivity coefficient, authority level, coefficient of variation, and coordination coefficient.Using Analytic Hierarchy Process, judgment matrix was established to determine the corresponding weights of elements, and calculate the total weight coefficients of each indicator, as well the consistency of the judgment matrix was checked.Results:The Cronbach′s coefficient of the questionnaire was 0.945, indicating high reliability.The effective recovery rates of the two rounds of expert consultation were 90.0%and 94.7%.Two rounds′ expert authority coefficients were 0.743 and 0.720.And the Kendall harmony coefficient was 0.196~0.731. The differences mentioned above were statistically significant(all P<0.05).Through two rounds consultation, a performance evaluation system for the clinical medicine advantageous disciplines was constructed, including 8 primary indicators and 24 secondary indicators.The weight coefficients of the primary indicators were 0.126, 0.208, 0.091, 0.162, 0.178, 0.118, 0.068, 0.049, and the total weight coefficients of each indicator were calculated, which showed good logical reliability. Conclusions:The performance evaluation system for the clinical medicine advantageous disciplines has strong scientific validity.It can provide evaluation tools for promoting the rational allocation and efficient utilization of hospital resources.

Objective:The level of discipline construction is an important indicator of a hospital′s comprehensive strength and core competitiveness.Establishing a scientific, reasonable, and effective performance evaluation system for the clinical medicine advantageous disciplines is of particular significance for university affiliated hospitals to strengthen discipline construction, improve governance systems, and promote high-quality development of hospitals.Methods:The Delphi method was used to determine the reliability of the indicator system by measuring the positivity coefficient, authority level, coefficient of variation, and coordination coefficient.Using Analytic Hierarchy Process, judgment matrix was established to determine the corresponding weights of elements, and calculate the total weight coefficients of each indicator, as well the consistency of the judgment matrix was checked.Results:The Cronbach′s coefficient of the questionnaire was 0.945, indicating high reliability.The effective recovery rates of the two rounds of expert consultation were 90.0%and 94.7%.Two rounds′ expert authority coefficients were 0.743 and 0.720.And the Kendall harmony coefficient was 0.196~0.731. The differences mentioned above were statistically significant(all P<0.05).Through two rounds consultation, a performance evaluation system for the clinical medicine advantageous disciplines was constructed, including 8 primary indicators and 24 secondary indicators.The weight coefficients of the primary indicators were 0.126, 0.208, 0.091, 0.162, 0.178, 0.118, 0.068, 0.049, and the total weight coefficients of each indicator were calculated, which showed good logical reliability. Conclusions:The performance evaluation system for the clinical medicine advantageous disciplines has strong scientific validity.It can provide evaluation tools for promoting the rational allocation and efficient utilization of hospital resources.

With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.
Pattern Recognition, Automated ; Medical Informatics