Denecke Kerstin
Institute Patient-Centered Digital Health, Bern University of Applied Sciences, Quellgasse 21, Biel, 2502, Switzerland.
BMC Health Serv Res. 2025 Jan 15;25(1):84. doi: 10.1186/s12913-025-12251-5.
Hospital at home (HaH) care models have gained significant attention due to their potential to reduce healthcare costs, improve patient satisfaction, and lower readmission rates. However, the lack of a standardized classification system has hindered systematic evaluation and comparison of these models. Taxonomies serve as classification systems that simplify complexity and enhance understanding within a specific domain.
This paper introduces a comprehensive taxonomy of HaH care models, aiming to categorize and compare the various ways HaH services are delivered as an alternative to traditional hospital care.
We developed a taxonomy of characteristics for HaH care models based on scientific literature and by applying a taxonomy development framework. To validate the taxonomy, and to analyze the current landscape of HaH models we matched the taxonomy to HaH care models described in literature. Finally, to identify types of HaH care implementations, we applied the k-means clustering method to care models represented using the taxonomy.
Our taxonomy consists of 12 unique dimensions structured into 5 perspectives following the progression from triaging, through care delivery, operational processes, and metrics for success: Persons and roles (2 dimensions), Target population (1 dimension), Service delivery and care model (6 dimensions), outcomes and quality metrics (2 dimensions), and training and education (1 dimension). Cluster analysis of 34 HaH care models revealed three distinct types: One cluster (50%, 17/34) focuses on patient eligibility and home environment suitability, a care model to be chosen for clinically complex patients. A second cluster (29.4%, 10/34) aggregates technology-enabled models using telemedicine and remote monitoring that are adaptable across settings. This type could be chosen for generalizable care. The third cluster (20.6%, 7/34) includes complex interventions involving informal caregivers and advanced medical devices, requiring caregiver training, supportive policies, and user-friendly technology to reduce caregiver burden and improve safety.
The clusters identified highlight practical considerations for adapting HaH care approaches to patient and contextual needs. These findings can guide policymakers in developing guidelines and assist practitioners in tailoring HaH care models to specific patient populations. The challenges encountered in collecting information on different characteristics of the taxonomy underscore the urgent need for more comprehensive and standardized reporting in scientific papers on HaH interventions.
居家医院（HaH）护理模式因其在降低医疗成本、提高患者满意度和降低再入院率方面的潜力而备受关注。然而，缺乏标准化的分类系统阻碍了对这些模式的系统评估和比较。分类法作为一种分类系统，能够简化复杂性并增进对特定领域的理解。
本文介绍了一种全面的居家医院护理模式分类法，旨在对作为传统医院护理替代方案的居家医院服务的各种提供方式进行分类和比较。
我们基于科学文献并应用分类法开发框架，为居家医院护理模式制定了特征分类法。为了验证该分类法，并分析居家医院模式的当前状况，我们将该分类法与文献中描述的居家医院护理模式进行了匹配。最后，为了确定居家医院护理实施的类型，我们将k均值聚类方法应用于使用该分类法表示的护理模式。
我们的分类法由12个独特维度组成，分为5个视角，按照从分诊到护理提供、运营流程以及成功指标的顺序排列：人员和角色（2个维度）、目标人群（1个维度）、服务提供和护理模式（6个维度）、结果和质量指标（2个维度）以及培训和教育（1个维度）。对34种居家医院护理模式的聚类分析揭示了三种不同类型：一类（50%，17/34）侧重于患者资格和家庭环境适宜性，这是一种为临床复杂患者选择的护理模式。第二类（29.4%，10/34）汇总了使用远程医疗和远程监测的技术支持型模式，这些模式可在不同环境中适用。这种类型可用于通用护理。第三类（20.6%，7/34）包括涉及非正式护理人员和先进医疗设备的复杂干预措施，需要护理人员培训、支持性政策和用户友好型技术，以减轻护理人员负担并提高安全性。
所确定的聚类突出了使居家医院护理方法适应患者和具体情况需求的实际考虑因素。这些发现可为政策制定者制定指南提供指导，并帮助从业者为特定患者群体量身定制居家医院护理模式。在收集有关分类法不同特征的信息时遇到的挑战凸显了在关于居家医院干预措施的科学论文中迫切需要更全面和标准化的报告。
