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实施新生儿和儿科患者的结构化随访:使用功能共振分析方法对三所大学附属医院案例的评估。

Implementing structured follow-up of neonatal and paediatric patients: an evaluation of three university hospital case studies using the functional resonance analysis method.

机构信息

Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, Netherlands.

Department of Pediatric Surgery, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam and Vrije Universiteit, Amsterdam Reproduction and Development, Amsterdam, Netherlands.

出版信息

BMC Health Serv Res. 2022 Feb 14;22(1):191. doi: 10.1186/s12913-022-07537-x.

Abstract

BACKGROUND

In complex critical neonatal and paediatric clinical practice, little is known about long-term patient outcomes and what follow-up care is most valuable for patients. Emma Children's Hospital, Amsterdam UMC (Netherlands), implemented a follow-up programme called Follow Me for neonatal and paediatric patient groups, to gain more insight into long-term outcomes and to use such outcomes to implement a learning cycle for clinical practice, improve follow-up care and facilitate research. Three departments initiated re-engineering and change processes. Each introduced multidisciplinary approaches to long-term follow-up, including regular standardised check-ups for defined age groups, based on medical indicators, developmental progress, and psychosocial outcomes in patients and their families. This research evaluates the implementation of the three follow-up programmes, comparing predefined procedures (work-as-imagined) with how the programmes were implemented in practice (work-as-done).

METHODS

This study was conducted in 2019-2020 in the outpatient settings of the neonatal intensive care, paediatric intensive care and paediatric surgery departments of Emma Children's Hospital. It focused on the organisational structure of the follow-up care. The functional resonance analysis method (FRAM) was applied, using documentary analysis, semi-structured interviews, observations and feedback sessions.

RESULTS

One work-as-imagined model and four work-as-done models were described. The results showed vast data collection on medical, developmental and psychosocial indicators in all work-as-done models; however, process indicators for programme effectiveness and performance were missing. In practice there was a diverse allocation of roles and responsibilities and their interrelations to create a multidisciplinary team; there was no one-size-fits-all across the different departments. Although control and feedback loops for long-term outcomes were specified with respect to the follow-up groups within the programmes, they were found to overlap and misalign with other internal and external long-term outcome monitoring practices.

CONCLUSION

Implementing structured long-term follow-up may provide insights for improving daily practice and follow-up care, with the precondition of standardised measurements. Lessons learned from practice are (1) to address fragmentation in data collection and storage, (2) to incorporate the diverse ways to create a multidisciplinary team in practice, and (3) to include timely actionable indicators on programme effectiveness and performance, alongside medical, developmental and psychosocial indicators.

摘要

背景

在复杂的新生儿和儿科重症临床实践中,对于患者的长期预后以及何种随访护理最有价值,人们知之甚少。阿姆斯特丹大学医学中心(荷兰)艾玛儿童医院实施了一项名为“Follow Me”的新生儿和儿科患者群体随访计划,以更深入地了解长期预后,并利用这些结果为临床实践实施学习循环,改善随访护理并促进研究。三个科室启动了再工程和变革流程。每个科室都引入了多学科方法进行长期随访,包括根据医疗指标、患者及其家庭的发育进展和心理社会结果,为特定年龄组定期进行标准化检查。本研究评估了三个随访项目的实施情况,将预定程序(想象中的工作)与项目在实践中的实施情况(实际中的工作)进行了比较。

方法

本研究于 2019 年至 2020 年在艾玛儿童医院新生儿重症监护、儿科重症监护和小儿外科部门的门诊环境中进行,重点关注随访护理的组织结构。应用功能共振分析方法(FRAM),采用文献分析、半结构化访谈、观察和反馈会议。

结果

描述了一个想象中的工作模型和四个实际中的工作模型。结果表明,所有实际中的工作模型都广泛收集了医疗、发育和心理社会指标的数据,但缺乏用于评估项目效果和绩效的过程指标。在实践中,存在着多样化的角色和职责分配及其相互关系,以创建多学科团队;不同科室之间没有一刀切的模式。尽管在项目内针对随访群体指定了长期结果的控制和反馈循环,但发现这些循环与其他内部和外部长期结果监测实践重叠且不匹配。

结论

实施结构化的长期随访可能为改善日常实践和随访护理提供思路,但前提是要进行标准化测量。从实践中吸取的教训是:(1)解决数据收集和存储碎片化问题;(2)将实践中创建多学科团队的各种方式纳入其中;(3)将关于项目效果和绩效的及时可行的指标纳入医疗、发育和心理社会指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/183a/8842913/807476f86a1a/12913_2022_7537_Fig1_HTML.jpg

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