目的：基于我国国情构建科学、简便且高效的眼科急诊预检分诊标准，为眼科医护人员提供高效的眼科急诊预检分诊工具。方法：基于文献查询法、半结构访谈法、德尔菲法及层次分析法确定眼科急诊预检分诊标准内容。通过提取2023年8月1日-2023年8月10日急诊分诊系统及HIMSS电子病历系统数据，分析初次分诊的级别与接诊医生最终诊断的所属级别符合率，对眼科急诊预检分诊标准体系的应用效果进行初步验证。结果：对18名专家进行２轮专家咨询，有效问卷回收率均为100%，专家权威系数均为0.95，肯德尔和谐系数分别为0.5640.117(均P＜0.05)。最终构建的眼科急诊预检分诊标准体系包括3个一级指标、11个二级指标。初步验证显示，该预检分诊标准体系具有92.7%的分诊准确率。结论：本研究构建的眼科急诊预检分诊标准体系结构合理、内容全面，具有科学性及实用性，可为眼科临床急诊预检分诊工作提供准确、有效的分诊工具，有助于提高临床工作效率及预检分诊质量。
Objective: To establish a scientific, simple, and efficient ophthalmic emergency pre-examination triage standard, and provide efficient ophthalmic emergency pre-examination triage tools for ophthalmic staffs, based on national conditions. Methods: With literature search, semi-structured interview, Delphi Method, and Analytic Hierarchy Process, the content of ophthalmic emergency pre-examination and triage standard are confirmed. By extracting data from the emergency triage system and HIMSS electronic medical record system from August 1st, 2023 to August 10th, 2023, the consistency rate between the initial triage level and the final diagnosis level of the attending doctor was analyzed, and the application effect of the ophthalmic emergency pre-examination and triage standard system was preliminarily verified. Results: Two rounds of expert consultation were conducted among 18 experts, all with a 100% effective questionnaire response rate. The expert authority coefficients were 0.95, and the Kendall harmony coefficients were 0.564 and 0.117, respectively (all P<0.05). The final constructed ophthalmic emergency pre-examination triage standard system includes 3 primary indicators and 11 secondary indicators. Through verification, the pre screening triage standard system has a good triage accuracy rate of up to 92.7%. Conclusions: The structure of the ophthalmic emergency pre-examination triage standard system constructed in this study is reasonable, comprehensive, scientific, and practical. It can provide accurate and effective triage tools for ophthalmic clinical emergency pre-examination triage work efficiency, and preexamination triage quality.
目的：检索并总结开角型青光眼患者眼部用药规范化护理管理的最佳证据，为临床实践提供参考。方法：通过护理循证方法提出实践问题，按照循证证据检索数据库的“6S”分类模型，检索有关开角型青光眼患者眼部用药管理的所有证据资源类型，包括临床指南、最佳实践信息册、证据总结、系统评价和专家共识等。由2名循证护理研究员对纳入文献的质量进行独立评价并进行证据的归纳总结。结果：最终纳入12篇文献，包括4篇指南、3篇证据总结和5篇系统评价；汇总了19条有关开角型青光眼眼部用药管理的最佳证据，包括滴眼技术指导、依从性管理和信息提供3个方面。结论：总结开角型青光眼眼部用药管理的最佳证据，可为临床医务人员管理患者提供参考和借鉴，以达到患者规范用药、控制眼压和延缓疾病进展的目的。
Objective: To retrieve and summarize the best evidence on standardized ocular medication management among open-angle glaucoma patients. Methods: With evidence-based nursing method, practical problemswere identified. According to the “6S” pyramid model of evidence resource, studies on standardized ocular medication management among open-angle glaucoma patients were retrieved, including clinical guidelines, best practice information booklet, systematic reviews, and expert consensus. Two evidence-based nursing researchers independently evaluated the quality of the included literature and summarized the evidence. Results: A total of 12 articles were ultimately enrolled, including 4 clinical guidelines, 3 evidence summaries and 5 systematic reviews. Finally, 3 aspects including 19 pieces of best evidence were summarized, which were Eye drop technical instruction, medication adherence management and related information provision. Conclusion: The best evidence for the medication management of open-angle glaucoma patients were summarized, which provide reference for clinical medical staffs to manage patients, so as to achieve the purpose of standardizing medication,controlling intraocular pressure and preventing disease progression.
目的：分析眼科护理对人工智能技术应用的内在需求，为眼科医院临床的人工智能技术开发及应用提供导向与依据。方法：采用整群抽样和单纯随机抽样相结合的方法，于2019年7月至2019年8月，对抽取的中山大学中山眼科中心，中山大学附属第一医院、珠海市人民医院、无锡人民医院、新疆维吾尔族自治区人民医院等目标医院其中的眼科护理人员进行问卷调查，内容包括一般资料及人工智能需求等。结果：调查对象绝大部分来自三级甲等医院(89.2%)，以华南地区为主(87.2%)，人工智能在眼科临床护理应用的需求多种多样，其中以健康教育、接诊与分诊、患者回访领域需求最强烈，分别占比95.7%、93.5%、93.2%。结论：人工智能在眼科临床护理应用有较强及多样化的需求，应结合实际需求为导向，重点推进人工智能在眼科患者健康教育等相关应用的研发。
Objective: To analyze the internal demands of the application of artificial intelligence technology to ophthalmic care, and provide guidance and basis for the development and application of artificial intelligence technology in ophthalmic hospitals. Methods: Using the method of combining cluster sampling with simple random sampling, a questionnaire survey was conducted on the ophthalmic nursing staff in the selected target hospitals from July to August 2019, which included general information and artificial intelligence needs. Results: Most of the respondents came from the third-class hospitals (89.2%), and hospitals in South China account for 87.2% of them. There are diverse demands of artificial intelligence in ophthalmology clinical nursing applications, including health education, clinical reception and triage, patients return visits, which have the strongest demand for the artificial intelligence, accounting for 95.7%, 93.5%, and 93.2%, respectively. Conclusion: The application of artificial intelligence in ophthalmic clinical nursing has strong and diversified demands, and the research and development of artificial intelligence in the health education of ophthalmic patients and other related applications should be promoted according to the actual demands.
目的：探讨品管圈在缩短眼底外科门诊患者就诊时长中的应用效果。方法：成立品管圈小组，确立缩短眼底外科门诊患者就诊时长活动主题，选择2020年9月份眼底外专科门诊就诊的484例患者为活动前研究对象。2020年12月份眼底外科门诊就诊的976例患者为活动后研究对象，分析干预前眼底外患者就诊时长，患者就医体验差的原因，针对原因拟定对策并组织实施。结果：开展品管圈活动后，眼底外科门诊患者的平均就诊时长显著缩短(P<0.05)。借助信息系统优化就诊流程，提高了患者满意度，圈员的团队凝聚力、积极性、沟通协调能力显著提高。结论：品管圈活动能缩短眼底外科患者就诊时长，提高患者就医体验，提升护理团队综合能力，且改善效果可持续保持。
Objective: To explore the application effect of quality control circle in shortening the length of outpatient visit in fundus surgery. Methods: A quality control circle group was established to set up the activity theme of shortening the duration of treatment for outpatient patients of fundus surgery, and 484 patients who visited outpatient clinics outside fundus in September 2020 were selected as the pre-activity research objects. In December 2020, 976 patients who visited fundus surgery outpatient department were the subjects of the post-activity study. We analyzed the duration of treatment and the reasons for poor medical experience of patients before the intervention, formulated countermeasures for the reasons and organized and implemented them. Results: After the quality control circle activity was carried out, the mean duration of outpatient visits in fundus surgery was significantly shortened (P<0.05). With the help of the information system, the medical treatment process was optimized to improve the satisfaction of patients, and the team cohesion, enthusiasm, communication and coordination ability of the circle members were significantly improved. Conclusion: Quality control circle activities can shorten the duration of treatment for fundus surgery patients, improve patients' medical experience, enhance the comprehensive ability of the nursing team, and the improvement effect can be maintained sustainably.
目的：分析眼科护理对人工智能技术应用的内在需求，为眼科医院临床的人工智能技术开发及应用提供导向与依据。方法：采用整群抽样和单纯随机抽样相结合的方法，于2019年7月至2019年8月，对抽取的中山大学中山眼科中心，中山大学附属第一医院、珠海市人民医院、无锡人民医院、新疆维吾尔族自治区人民医院等目标医院其中的眼科护理人员进行问卷调查，内容包括一般资料及人工智能需求等。结果：调查对象绝大部分来自三级甲等医院(89.2%)，以华南地区为主(87.2%)，人工智能在眼科临床护理应用的需求多种多样，其中以健康教育、接诊与分诊、患者回访领域需求最强烈，分别占比95.7%、93.5%、93.2%。结论：人工智能在眼科临床护理应用有较强及多样化的需求，应结合实际需求为导向，重点推进人工智能在眼科患者健康教育等相关应用的研发。
Objective: To analyze the internal demands of the application of artificial intelligence technology to ophthalmic care, and provide guidance and basis for the development and application of artificial intelligence technology in ophthalmic hospitals. Methods: Using the method of combining cluster sampling with simple random sampling, a questionnaire survey was conducted on the ophthalmic nursing staff in the selected target hospitals from July to August 2019, which included general information and artificial intelligence needs. Results: Most of the respondents came from the third-class hospitals (89.2%), and hospitals in South China account for 87.2% of them. There are diverse demands of artificial intelligence in ophthalmology clinical nursing applications, including health education, clinical reception and triage, patients return visits, which have the strongest demand for the artificial intelligence, accounting for 95.7%, 93.5%, and 93.2%, respectively. Conclusion: The application of artificial intelligence in ophthalmic clinical nursing has strong and diversified demands, and the research and development of artificial intelligence in the health education of ophthalmic patients and other related applications should be promoted according to the actual demands.
