Objective This study examined the research status and development process of FU Qingzhu’s Obstetrics and Gynecology(Fu Qing Zhu Nv Ke,《傅青主女科》,FQZNK)in the past 40 years with bibliometrics and visual analysis.M...Objective This study examined the research status and development process of FU Qingzhu’s Obstetrics and Gynecology(Fu Qing Zhu Nv Ke,《傅青主女科》,FQZNK)in the past 40 years with bibliometrics and visual analysis.Methods Retrieved all related literature in the research field of FQZNK from the domestic and foreign databases:China National Knowledge Infrastructure(CNKI),China Science and Technology Journal Database(VIP),Wanfang Database,and Web of Science(WOS)core database,including Science Citation Index Expanded(SCIE),Social Sciences Citation Index(SSCI),and Arts&Humanities Citation Index(A&HCI).The search range was from January 1,1980 to March 10,2021.In addition,bibliometrics and CiteSpace 5.7.R2 software were used to analyze literature types,published journals,cited literature,the number of author publica-tions,co-author networks,co-institution networks,keyword co-occurrence networks,keyword clusters,and keyword bursts.Results A total of 678 valid records were included in the final dataset.Literature types,high publication journals,highly cited literature,high-yield institutions,high-yield research teams,and high-productivity scholars in this research field were found through bibliometrics.Liter-ature types can be divided into four categories,among which 451 are theoretical studies on academic thoughts of FQZNK,accounting for 66.5%of the included journals.The Journal of Shanxi Traditional Chinese Medicine had the largest volume of published articles(61),ac-counting for 9.0%of the total number of the included journals.The most cited literature was ZHOU Mingxin’s article“Using the quantitative method to discuss author’s authenticity and formula characteristics of FU Qingzhu’s Obstetrics and Gynecology”,which was cited 94 times.Hunan University of Chinese Medicine,the institution with the most publications,published 45 articles,and YOU Zhaoling,the most published author,published 33 articles.Moreover,it was found that most high-yield researchers came from high-yield institutions and that Hun-an University of Chinese Medicine had the most research on FQZNK.Keyword co-occur-rence analysis revealed that the keyword“FQZNK”had the highest frequency(597 times)and the highest centrality(1.00).Keyword cluster analysis used the Log-Likelihood Ratio(LLR)al-gorithm to form eleven important clusters:#0 treatment aiming at its root causes,#1 gynecopathy,#2 Siwu Decoction(四物汤),#3 FU Qingzhu,#4 post-partum,#5 infertility,#6 dysmenorrhea,#7 sterility,#8 coordinate the heart and kidney,#9 Danggui Buxue Decoction(当归补血汤),and#10 treatment.It was found that the prescriptions of FQZNK were studied mainly before 2000,the theoretical studies were mainly conducted before 2010,and its clinic-al application was mainly explored from 2010 until now.Diseases such as dysmenorrhea,morbid vaginal discharge,infertility,metrorrhagia,and polycystic ovarian syndrome(PCOS)have recently become popular topics in this field.Conclusion The current study provides more scientific,accurate,and comprehensive sci-entific support for further research and development of traditional Chinese medicine(TCM)in FQZNK.With this foundation,people can use burst detection to ascertain the current hot-spots in research,get their development trends,and forecast future research directions.In ad-dition,infertility,morbid vaginal discharge,flooding,and PCOS treatments based on TCM syndrome differentiation are currently popular research topics for FQZNK.展开更多
OBJECTIVE: The ambulatory clinic was an important departmental problem. Providers hated working there and patients complained about the wait times there. It seemed there were equal numbers of patients and provider com...OBJECTIVE: The ambulatory clinic was an important departmental problem. Providers hated working there and patients complained about the wait times there. It seemed there were equal numbers of patients and provider complaints. In the spirit of solving the problem, data was gathered, a LEAN intervention was planned, and data was collected. METHODS: We defined the service families in the clinic as registration, vital signs, provider or ultrasound visit, nursing visit, and registration for the return visit. We walked the Gemba engaging all the staff in the process. Many observations pointed to long waits between and among the five stations. In order to study the current state, time data was collected by attaching a sheet of paper to a folder that the patient would carry themselves to all the clinical steps. On the sheet of paper each station logged the time that patient appeared and the time the patient left their sight. Data was gathered each day and every day from October 2016 to the summer of 2017. The data was analyzed. Leadership met and identified value and waste in the process. A Kaizen event was scheduled after the first set of measurements engaging all the staff. After the data was thoroughly analyzed and digested, brainstorming occurred. Together we determined our future state. We created a vision and strategic goals to reach our future state. RESULTS: The data pre-Kaizen event showed that the process of arrival to leaving took 124 minutes. We discovered that not every patient passed through each station. We learned the patients were on time or early for their visit most of the time. The providers were late most of the time by 1 - 1.5 hours. We learned how long each station took from the patient’s point of view. There were no statistically significant differences between ultrasound and provider visits;there were no statistically significant differences between midwife and physician visits. Each day of the week was similar. The arrival rate was higher in the morning because of the template. After the event, the total time in clinic did not change however the variability in time between and among each station decreased in variance. We informed the staff of these findings so that they could take responsibility for their part in the process. The atmosphere in clinic changed dramatically and the complaints from both providers and patients stopped. CONCLUSION: LEAN management was used to improve the clinic. It yielded important results, got the staff engaged in the process, and provided a way for the patients to see the efforts made by staff to improve.展开更多
基金National Key R&D Program of China-Science and Technology Innovation 2030-"New Generation of Artificial Intelligence"Major Project(2018AAA0102100)Postgraduate Research Innovation Project of Hunan Province(CX2018B465)2011 Digital Chinese Medicine Innovation Research Platform of Hunan Digital Chinese Medicine Collaborative Innovation Center。
文摘Objective This study examined the research status and development process of FU Qingzhu’s Obstetrics and Gynecology(Fu Qing Zhu Nv Ke,《傅青主女科》,FQZNK)in the past 40 years with bibliometrics and visual analysis.Methods Retrieved all related literature in the research field of FQZNK from the domestic and foreign databases:China National Knowledge Infrastructure(CNKI),China Science and Technology Journal Database(VIP),Wanfang Database,and Web of Science(WOS)core database,including Science Citation Index Expanded(SCIE),Social Sciences Citation Index(SSCI),and Arts&Humanities Citation Index(A&HCI).The search range was from January 1,1980 to March 10,2021.In addition,bibliometrics and CiteSpace 5.7.R2 software were used to analyze literature types,published journals,cited literature,the number of author publica-tions,co-author networks,co-institution networks,keyword co-occurrence networks,keyword clusters,and keyword bursts.Results A total of 678 valid records were included in the final dataset.Literature types,high publication journals,highly cited literature,high-yield institutions,high-yield research teams,and high-productivity scholars in this research field were found through bibliometrics.Liter-ature types can be divided into four categories,among which 451 are theoretical studies on academic thoughts of FQZNK,accounting for 66.5%of the included journals.The Journal of Shanxi Traditional Chinese Medicine had the largest volume of published articles(61),ac-counting for 9.0%of the total number of the included journals.The most cited literature was ZHOU Mingxin’s article“Using the quantitative method to discuss author’s authenticity and formula characteristics of FU Qingzhu’s Obstetrics and Gynecology”,which was cited 94 times.Hunan University of Chinese Medicine,the institution with the most publications,published 45 articles,and YOU Zhaoling,the most published author,published 33 articles.Moreover,it was found that most high-yield researchers came from high-yield institutions and that Hun-an University of Chinese Medicine had the most research on FQZNK.Keyword co-occur-rence analysis revealed that the keyword“FQZNK”had the highest frequency(597 times)and the highest centrality(1.00).Keyword cluster analysis used the Log-Likelihood Ratio(LLR)al-gorithm to form eleven important clusters:#0 treatment aiming at its root causes,#1 gynecopathy,#2 Siwu Decoction(四物汤),#3 FU Qingzhu,#4 post-partum,#5 infertility,#6 dysmenorrhea,#7 sterility,#8 coordinate the heart and kidney,#9 Danggui Buxue Decoction(当归补血汤),and#10 treatment.It was found that the prescriptions of FQZNK were studied mainly before 2000,the theoretical studies were mainly conducted before 2010,and its clinic-al application was mainly explored from 2010 until now.Diseases such as dysmenorrhea,morbid vaginal discharge,infertility,metrorrhagia,and polycystic ovarian syndrome(PCOS)have recently become popular topics in this field.Conclusion The current study provides more scientific,accurate,and comprehensive sci-entific support for further research and development of traditional Chinese medicine(TCM)in FQZNK.With this foundation,people can use burst detection to ascertain the current hot-spots in research,get their development trends,and forecast future research directions.In ad-dition,infertility,morbid vaginal discharge,flooding,and PCOS treatments based on TCM syndrome differentiation are currently popular research topics for FQZNK.
文摘OBJECTIVE: The ambulatory clinic was an important departmental problem. Providers hated working there and patients complained about the wait times there. It seemed there were equal numbers of patients and provider complaints. In the spirit of solving the problem, data was gathered, a LEAN intervention was planned, and data was collected. METHODS: We defined the service families in the clinic as registration, vital signs, provider or ultrasound visit, nursing visit, and registration for the return visit. We walked the Gemba engaging all the staff in the process. Many observations pointed to long waits between and among the five stations. In order to study the current state, time data was collected by attaching a sheet of paper to a folder that the patient would carry themselves to all the clinical steps. On the sheet of paper each station logged the time that patient appeared and the time the patient left their sight. Data was gathered each day and every day from October 2016 to the summer of 2017. The data was analyzed. Leadership met and identified value and waste in the process. A Kaizen event was scheduled after the first set of measurements engaging all the staff. After the data was thoroughly analyzed and digested, brainstorming occurred. Together we determined our future state. We created a vision and strategic goals to reach our future state. RESULTS: The data pre-Kaizen event showed that the process of arrival to leaving took 124 minutes. We discovered that not every patient passed through each station. We learned the patients were on time or early for their visit most of the time. The providers were late most of the time by 1 - 1.5 hours. We learned how long each station took from the patient’s point of view. There were no statistically significant differences between ultrasound and provider visits;there were no statistically significant differences between midwife and physician visits. Each day of the week was similar. The arrival rate was higher in the morning because of the template. After the event, the total time in clinic did not change however the variability in time between and among each station decreased in variance. We informed the staff of these findings so that they could take responsibility for their part in the process. The atmosphere in clinic changed dramatically and the complaints from both providers and patients stopped. CONCLUSION: LEAN management was used to improve the clinic. It yielded important results, got the staff engaged in the process, and provided a way for the patients to see the efforts made by staff to improve.
