Mapping the Landscape of Hospital at Home (HaH) Care: A Validated Taxonomy for HaH Care Model Classification

Abstract Background: 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. Objective: 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. Methods: 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 archetypes of care models, we applied the k-means clustering method to the care models described using the taxonomy. Results: 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). Each dimension aggregates between 1 and 20 characteristics. We identified three clusters within a dataset of 34 HaH care approaches. Conclusions: The developed taxonomy offers a valuable framework for healthcare providers, policymakers, and researchers to systematically evaluate and implement HaH care models. The challenges encountered in collecting information on different characteristics of the taxonomy underscore the urgent need for more comprehensive and standardised reporting in scientific papers on HaH interventions.