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DRG 지불제도 도입 후 제왕절개술에서의 의료의 질 변화
권영훈,홍두호,김창엽,김용익,신영수,임준,Kwon, Young-Hun,Hong, Du-Ho,Kim, Chang-Yup,Kim, Yong-Ik,Shin, Young-Soo,Yim, Jun 대한예방의학회 2001 예방의학회지 Vol.34 No.4
Objectives : To determine the impacts of Diagnosis-Related Groups/Prospective Payment System (DRG/PPS) on the quality of care in cases of Cesarean section and to describe the policy implications for the early stabilization of DRG/PPS in Korea. Methods : Data was collected from the medical records of 380 patients who had undergone Cesarean sections in 40 hospitals participating in the DRG/PPS Demonstration Program since 1999. Cesarean sections were peformed in 122 patients of the FFS(Fee-For-Service) group and 258 patients of the DRG/PPS group. Measurements of quality used included essential tests of pre- and post-operation, and the PPI(Physician Performance Index) score. The PPI was developed by two obstetricians. Results : Univariate analysis demonstrated significant differences in PPI scores according to the payment systems. With respect to the mean of PPI scores, a higher score was found in the DRG/PPS group than in the FFS group. However, the adjusted effect did not show significant differences between the FFS group and the DRG/PPS group. Conclusion : This study suggested that the problem of poor quality may not be related to the implementation of DRG/PPS in Cesarean section. However, this study did not consider the validity and reliability of the process measurement, and it did not exclude the possibility of data emission in medical records.
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의학전문대학원 학생들의 학업성취도 분석 - 개인적 특성 및 입학전형요소를 중심으로 -
박귀화(Kwi Hwa Park),홍두호(Du Ho Hong),오재환(Jae Hwan Oh),박이병(Yi Byoung Park),신동진(Dong Jin Shin),이영돈(Young Don Lee) 한국의학교육학회 2006 Korean journal of medical education Vol.18 No.3
Purpose: The purpose of this study was to examine the academic achievement of the first year medical students at Gachon University of Medicine and Science based on their characteristics (i.e. age, gender, and academic degree types (bachelors/masters), field of major (biology/ non-biology), and admission variables (i.e. MEET, GPA, attitude, interview, and English test). Methods: The admission scores and demographic information were obtained from 39 students who had entered the university in 2005. T-test was used to examine the differences in academic achievement according to the students characteristics. Correlations between admission variables and academic achievements were examined while multiple regression analysis was used to decipher the influence of students characteristics and admission variables on the academic achievements. Results: This study showed that the academic achievements differed significantly depending on degree level, age, and field of study, but not gender. It was also found that both the Nature Science Reasoning (NSR) of MEET subscale score and GPA score correlated significantly with academic achievement in the first year students. Attitude, interview, and English test scores did not correlate significantly. The result of the multiple regression analysis revealed that NSR 1 and 2, field of major, and age accounted for a substantial amount of the variance in academic achievement. Conclusion: Students with higher scores in NSR 1 and 2 who majored in biology-related fieldsand who are younger tend to do better academically in their first year of medical school. The results of this study may be helpful to graduate medical schools in designing effective admissions strategies for recruiting students, as well as, in planning the curriculum to improve the academic achievements of medical students.
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데이터마이닝 기법을 이용한 효율적인 DRG 확인심사대상건 검색방법
이중규,조민우,박기동,이무송,이상일,김창엽,김용익,홍두호,Lee, Jung-Kyu,Jo, Min-Woo,Park, Ki-Dong,Lee, Moo-Song,Lee, Sang-Il,Kim, Chang-Yup,Kim, Yong-Ik,Hong, Du-Ho 대한예방의학회 2003 Journal of Preventive Medicine and Public Health Vol.36 No.2
Objectives : To develop a Diagnosis-Related Group (DRG) fraud candidate detection method, using data mining techniques, and to examine the efficiency of the developed method. Methods ; The Study included 79,790 DRGs and their related claims of 8 disease groups (Lens procedures, with or without, vitrectomy, tonsillectomy and/or adenoidectomy only, appendectomy, Cesarean section, vaginal delivery, anal and/or perianal procedures, inguinal and/or femoral hernia procedures, uterine and/or adnexa procedures for nonmalignancy), which were examined manually during a 32 months period. To construct an optimal prediction model, 38 variables were applied, and the correction rate and lift value of 3 models (decision tree, logistic regression, neural network) compared. The analyses were peformed separately by disease group. Results : The correction rates of the developed method, using data mining techniques, were 15.4 to 81.9%, according to disease groups, with an overall correction rate of 60.7%. The lift values were 1.9 to 7.3 according to disease groups, with an overall lift value of 4.1. Conclusions : The above findings suggested that the applying of data mining techniques is necessary to improve the efficiency of DRG fraud candidate detection.
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