Mapping the landscape of Hospital at home (HaH) care: a validated taxonomy for HaH care model classification.

backgroundHospital 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.

objectiveThis 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.

methodsWe 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.

resultsOur 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.

conclusionsThe 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.