Objective:To evaluate and compare the inpatient medical services of secondary public and private general hospitals by using disease risk adjustment model, and to explore the application of disease risk adjustment model in medical service evaluation of different ownership hospitals.Methods:Based on 1 032 865 front pages of medical records in Chengdu in 2017 and 2018, a regression model with mortality, average length of stay, total hospitalization expenses, medical service fees, drug costs and surgical consumables costs as dependent variables and related influencing factors as independent variables was established by using disease management intelligent analytic and evaluation system. The risk adjusted case mix index(ACMI) was calculated. The mortality, average length of stay, hospitalization expenses and other indicators were predicted. The ratio of observed value to expected value(O/E value) of each index in public and private secondary general hospitals was obtained and compared.Results:The ACMI value of secondary public general hospital was 4.63, slightly higher than that of private hospitals(4.55). The technical difficulty and resource consumption of the public hospitals were slightly higher than that of the private hospitals.From the O/E value, the management of disease mortality, medical service fees and inpatient drug costs of secondary public hospitals was generally good, and the O/E values of hospitalization expenses of each secondary private general hospital were quite different, and there was a possibility that the costs were unreasonable. The O/E value of surgical consumables cost in secondary public general hospital was 1.54, and there was room for improvement in cost management.Conclusions:The disease risk adjustment model fully considers the characteristics of different types and severity of diseases in different institutions, which can not be simply compared. Based on individual cases, it realizes the comparability of different ownership hospitals, and provides a new means for the evaluation of medical service ability and quality.

Objective: To study new ways and tools for assessing the inpatient disease management and improving refined management of the hospital. Methods: By using homepages of medical records of the patients discharged from 21 tertiary general hospitals in a city in 2016, we completed the modeling and predicted value calculation within each DRGs with the Disease Management Intelligent Analytic & Evaluation System (DMIAES System). Results: 2 192 predication models were built, to compute the theoretic values of the mortality rate, length of stay, medical fee, medical service fee, and drug cost of each inpatient. Such values were compared with the observed results to gain the O/E index. If O/E is less than 1, it indicates that the inpatient′s disease management is good and better than expected. On the other hand, O/E index greater than 1 indicates poorer disease management than expected and rooms of further improvement. With the help of O/E index, we made multidimensional comparisons assessment and analysis of different hospitals, clinical disciplines, diseases and doctors. Conclusions The DMIAES System can take risk factors of inpatients′ outcomes into account, assessing the major indicators of inpatient outcomes by means of big data and modelling. This approach proves effective in enabling administrators and doctors to rapidly analyze problems for identifying solutions and enhancing management, thus having great potential in hospital management, supervision and assessment.