Hospital at home (virtual wards): developing a logic model and dark logic model.

Area of Science:

• Healthcare Management
• Health Services Research
• Digital Health

Background:

• Hospital at Home (HaH), or virtual wards, deliver hospital-level care at home using digital technology and multidisciplinary teams.
• Rapid implementation has outpaced understanding of safe practices and broader implications.
• Logic and dark logic models are needed to map evidence and guide future research.

Purpose of the Study:

• To develop logic and dark logic models for Hospital at Home (virtual wards) in England.
• To visually represent intended and unintended outcomes and contributing factors.
• To inform future research prioritization and program planning.

Main Methods:

• Triangulation of document analysis (public, grey literature), key informant interviews (n=12), and a consensus focus group.
• Content analysis using inductive and deductive approaches to refine logic models.
• Development of both logic (intended outcomes) and dark logic (unintended outcomes) models.

Main Results:

• Logic and dark logic models were iteratively developed and refined.
• Key sustainability themes: clinical buy-in, communication, workforce adaptation, operational optimization.
• Identified challenges: consistent funding, fragmented systems, service duplication.

Conclusions:

• The developed logic models visually map intended and unintended outcomes of HaH (virtual wards).
• Models can guide research, program planning, and evaluation for HaH services.
• Future research should focus on safe, effective, equitable care delivery and optimizing digital technology's impact.