Department of Cross-sectoral Collaboration, Region of Southern Denmark, Damhaven 12, 7100, Vejle, Denmark.
Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, 5000, Odense, Denmark.
BMC Geriatr. 2021 Feb 27;21(1):146. doi: 10.1186/s12877-021-02092-2.
The challenges imposed by ageing populations will confront health care systems in the years to come. Hospital owners are concerned about the increasing number of acute admissions of older citizens and preventive measures such as integrated care models have been introduced in primary care. Yet, acute admission can be appropriate and lifesaving, but may also in itself lead to adverse health outcome, such as patient anxiety, functional loss and hospital-acquired infections. Timely identification of older citizens at increased risk of acute admission is therefore needed. We present the protocol for the PATINA study, which aims at assessing the effect of the 'PATINA algorithm and decision support tool', designed to alert community nurses of older citizens showing subtle signs of declining health and at increased risk of acute admission. This paper describes the methods, design and intervention of the study.
We use a stepped-wedge cluster randomized controlled trial (SW-RCT). The PATINA algorithm and decision support tool will be implemented in 20 individual area home care teams across three Danish municipalities (Kerteminde, Odense and Svendborg). The study population includes all home care receiving community-dwelling citizens aged 65 years and above (around 6500 citizens). An algorithm based on home care use triggers an alert based on relative increase in home care use. Community nurses will use the decision support tool to systematically assess health related changes for citizens with increased risk of acute hospital admission. The primary outcome is acute admission. Secondary outcomes are readmissions, preventable admissions, death, and costs of health care utilization. Barriers and facilitators for community nurse's acceptance and use of the algorithm will be explored too.
This 'PATINA algorithm and decision support tool' is expected to positively influence the care for older community-dwelling citizens, by improving nurses' awareness of citizens at increased risk, and by supporting their clinical decision-making. This may increase preventive measures in primary care and reduce use of secondary health care. Further, the study will increase our knowledge of barriers and facilitators to implementing algorithms and decision support in a community care setup.
ClinicalTrials.gov , identifier: NCT04398797 . Registered 13 May 2020.
人口老龄化带来的挑战将在未来几年给医疗保健系统带来挑战。医院所有者担心越来越多的老年公民急性入院，因此在初级保健中引入了综合护理模式等预防措施。然而，急性入院可能是合适的和救命的，但也可能本身导致不良的健康后果，如患者焦虑、功能丧失和医院获得性感染。因此，需要及时识别有急性入院风险增加的老年公民。我们介绍了 PATINA 研究的方案，该研究旨在评估“PATINA 算法和决策支持工具”的效果，该工具旨在提醒社区护士注意那些表现出健康状况逐渐恶化迹象且有较高急性入院风险的老年公民。本文描述了该研究的方法、设计和干预措施。
我们使用了一个阶梯式楔形集群随机对照试验（SW-RCT）。PATINA 算法和决策支持工具将在丹麦三个市（Kerteminde、Odense 和 Svendborg）的 20 个独立地区家庭护理团队中实施。研究人群包括所有接受家庭护理的 65 岁及以上的社区居住公民（约 6500 名公民）。基于家庭护理使用的算法会触发警报，基于家庭护理使用的相对增加。社区护士将使用决策支持工具系统地评估有急性住院风险的公民的健康相关变化。主要结果是急性入院。次要结果是再入院、可预防入院、死亡和卫生保健利用成本。还将探讨社区护士接受和使用该算法的障碍和促进因素。
这个“PATINA 算法和决策支持工具”有望通过提高护士对风险增加的公民的意识，并支持他们的临床决策，积极影响对老年社区居住公民的护理。这可能会增加初级保健中的预防措施，并减少对二级卫生保健的使用。此外，该研究将增加我们对在社区护理环境中实施算法和决策支持的障碍和促进因素的认识。
ClinicalTrials.gov，标识符：NCT04398797。2020 年 5 月 13 日注册。
