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

Hospital at home (HaH), also referred to as virtual wards in the UK, enable patients to get hospital-level care at home with the use of digital technology, multidisciplinary teams, and remote monitoring. Despite recent evidence and rapid implementation, many questions around safe implementation and wider implications remain unanswered. Developing a logic model and dark logic model aimed to illustrate the recent evidence base with input from key informants and conceptualise the research focus for further work.

Triangulation of three workstreams for comprehensiveness and credibility, involved (1) document analysis using publicly available documents, and non-published documents (including grey literature) provided by key stakeholders or virtual ward forums, (2) key informant interviews with a variety of expertise involved in the planning, implementation, or delivery of HaH, and (3) a focus group, to reach consensus on the final refined logic models. These were analysed using content analysis using an inductive and deductive approach to refine the logic models after each workstream.

A draft logic model was developed from document analysis describing key components of the logic model and dark logic models. Interviews with 12 participants helped refine the logic models with a subsequent focus group for consensus. The key themes for sustainability were securing clinical 'buy-in', effective communication, potential workforce re-modelling and optimising operational capabilities. Concerns and challenges were raised such as continuous funding, inadequate shared systems and duplication.

These logic models provide a clear visual representation of intended (logic) and unintended outcomes (dark logic) of HaH (virtual wards) in England, and factors contributing to them. They can support prioritising future research or program planning and evaluation. Future research should explore strategies to deliver this personalised holistic care safely and effectively whilst maximising potential of resources like digital technology and understanding it's impact on patients and equity.