Implementation determinants, strategies and outcomes for scale-up of hospital-at-home: a systematic review

Abstract

Background

Hospital-at-Home (HaH) has been shown to be clinically and cost effective, but many programs struggle to scale. We conducted a systematic review of HaH studies reporting implementation contexts, strategies and outcomes with the primary aim to synthesise the implementation evidence on HaH. Our secondary aim was to develop a framework of HaH implementation outcome indicators for future studies to apply.

Methods

We searched the literature PubMed, Embase and Scopus, for publications from 2012 to 2022. We included studies related to HaH that included at least one implementation context and at least one implementation outcome variable, excluding clinical outcomes measures. At least two reviewers independently selected studies, abstracted data and assessed quality. We coded the implementation determinants to the Consolidated Framework for Implementation Research (CFIR); implementation strategies to the Expert Recommendations for Implementing Change (ERIC); and implementation outcomes to the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) frameworks.

Results

We included 27 studies in the final review, which reported 24 CFIR contextual factors, 25 ERIC strategies, and all domains of the RE-AIM evaluation framework. The most commonly reported implementation determinant, identified in six studies, was information technology infrastructure, which included streamlining documentation processes and improving access to patient records. Five studies described the following implementation factors: (i) work infrastructure, focusing on organising manpower effectively; (ii) capability of innovation deliverers, referring to staff posessing the necessary skills to perform their roles; and (iii) local conditions, such as the geographical challenges associated with HaH. The most frequently referenced implementation strategies were conducting ongoing training (nine studies), creating new clinical teams (eight studies), and promoting adaptability (seven studies). All assessed implementation outcomes were aligned to the RE-AIM framework, with volume of admissions, patient-reported experiences or outcome measures, and home visit frequency being most commonly reported (four studies each). The majority of implementation measures lacked a common denominator, and use of validated tools across studies was lacking.

Conclusions

We identified a list of implementation determinants, strategies and outcomes that can be used to inform scale-up of HaH, and have developed a framework for consistent reporting of HaH implementation studies grounded onto RE-AIM.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s43058-026-01033-7.

Contributions to the literature

• Hospital-at-Home (HaH) is a mature clinical intervention, yet how to scale HaH remains fragmented and inconsistently reported across studies.

• This review is the first to systematically map HaH implementation determinants, strategies, and outcomes using established frameworks (CFIR, ERIC, and RE-AIM), enabling cross-study comparison.

• We identify recurring, cross-context implementation determinants, strategies and outcomes across published HaH implementation studies.

• By highlighting gaps in standardised and validated implementation outcome measurement, this review provides a framework aligned with RE-AIM for more rigorous, comparable evaluation to support HaH scale-up globally.

Background

Hospital-at-home (HaH) is a home-based inpatient substitutive care model, which has been expanding worldwide to augment hospital bed capacity as populations age [1]. It features multi-disciplinary healthcare teams that care for patients in their homes, who would otherwise have required a hospital bed. Several randomised controlled trials and systematic reviews [2, 3] to-date have shown that for suitable acutely-ill patients who require hospital-level treatment, HaH is associated with similar or better outcomes than an in-hospital stay, at generally lower cost and improved patient experience [4–7]. Consequently, various health systems worldwide, including United Kingdom (UK) [8], United States of America (USA) [9] and Asia [10] have invested heavily in developing HaH programs with the intention to attenuate future hospitalisation demand and the need to build costly new hospitals.

Despite these major national financial levers, the majority of existing HaH programs remain small. A recent account of HaH in the USA showed an average of 89 beds across over 300 HaH programs spanning 37 states [11]. While clinical and cost-effectiveness evidence exists, a critical gap remains in understanding how to scale these services optimally so that the impact demonstrated in research translates to real-world improvements in bed capacity and patient outcomes. A 2024 review by Cochrane [12] provided a narrative synthesis of perspectives on HaH implementation; however, it is still unclear which implementation strategies are most effective especially those needed to scale and sustain services beyond a pilot phase.

We conducted a systematic review of the global evidence base on HaH implementation at scale. Our primary aim was to extract and synthesise evidence on the context, strategies (if reported) and outcomes reported across studies, and map these to established implementation theories to identify what works and in which healthcare settings. Our secondary aim was to develop a framework for the consistent reporting of HaH-related outcomes, to enable cumulative implementation evidence across the emerging evaluative studies of HaH care models globally.

Methods

Implementation frameworks

For the primary aim of the review, we applied well-established implementation science frameworks to guide both the selection and analysis of studies. These frameworks were selected because together they address different dimensions of implementation: the phase of implementation activity, the contextual factors shaping it, the strategies used to support it, and the outcomes achieved. Each is briefly described below.

First, we used the EPIS (Exploration, Preparation, Implementation, Sustainment) framework [13] to situate studies within the implementation lifecycle. EPIS is a phased framework that characterises implementation as a process unfolding across four sequential stages, from initial exploration of an innovation through to its sustained integration into routine practice. We applied EPIS to determine which phases of implementation the included studies addressed, enabling us to identify where the evidence base is concentrated and where gaps remain.

Second, we used the Consolidated Framework for Implementation Research (CFIR) [14] to examine the context in which HaH programs are implemented. CFIR organises determinants of implementation across five domains – the innovation itself, the outer setting, the inner setting, the individuals involved, and the implementation process – and has been widely applied to identify barriers and facilitators to healthcare implementation across diverse settings. We used CFIR to map factors reported across studies, including resources, leadership engagement, and patient-level considerations, that acted as barriers or facilitators to implementation and scale-up.

Third, for studies which reported on implementation strategies, we applied the Expert Recommendations for Implementing Change (ERIC) [15] framework to systematically categorize all intentionally applied strategies reported by programs to support implementation. ERIC provides a taxonomy of 73 discrete, operationally defined implementation strategies organised into nine conceptual clusters, developed through expert consensus to enable precise specification and comparison of implementation strategies across studies. This allowed us to characterise not only whether strategies were reported, but what types of strategies were employed.

Finally, to assess how well these programs were implemented, we used the RE-AIM framework [16]. RE-AIM evaluates five dimensions: Reach, Effectiveness, Adoption, Implementation fidelity, and Maintenance. It was developed specifically to address the gap between intervention efficacy and real-world scalability. We applied RE-AIM to provide a structured assessment of implementation success across the included studies.

These frameworks were used consistently across study selection and data extraction.

For the secondary aim of the review, drawing on the implementation knowledge base curated through this review, we developed an integrative outcome framework (HAH RE-AIM) to support the consistent evaluation of HaH implementation. The framework was informed by domains and definitions extracted from the included studies, and refined to enable comparability across HaH programs (e.g. adding a denominator). Each measure within the framework was assigned a standard definition, providing a universal reference that can be applied across diverse HaH programs.

This study was funded by PHRG Grant PHRGOC23Jan-0002. The review protocol was registered onto the Open Science Framework (https://osf.io/mbxpy/).

Data sources and searches

A search was performed on December 14, 2022 using PubMed, Embase and Scopus. Search terms included concepts of hospital at home and implementation outcomes (including concepts of scale, sustainability, maintenance, reach, acceptability, appropriateness, penetration, and related terminology). We intentionally did not restrict studies by implementation context or strategies in the initial search, to broaden the inclusion of studies that were eventually analysed. These were agreed by all authors a priori and are available in eAppendix Table 1. Test searches were carried out and results were refined by S.K. before finalizing the search strategy. The search was restricted to studies published from 2012 to 2022. The search was restricted to studies published within the last ten years to ensure the relevance and currency of the evidence base. HaH services have evolved considerably over this period, with advances in medical technology, remote monitoring and care delivery models shaping how these services are designed and implemented. Older studies may not accurately reflect current clinical practices, workforce configurations or patient expectations, and therefore may be less applicable to current implementation efforts. While a formal systematic grey literature search was not performed, grey literature inclusion was limited to targeted identification of relevant reports and programme descriptions known to the study team through domain expertise and familiarity with the HaH landscape. One additional study identified through this process met inclusion criteria.

Study selection

The title and abstract of each study were each reviewed for inclusion by two independent reviewers (S.K., R.C., L.Y.L, L.S.Y. or A.P.) for consensus. Discordant opinions were reviewed by a third reviewer (S.K.). Duplicate studies were removed during the initial stage of reviewing titles and abstracts. Then, a full text review was conducted by two independent reviewers (S.K., R.C., or L.Y.L), who were familiar with HaH, for inclusion. If the reviewers did not agree on a study, a fourth independent reviewer (N.S.) made a final decision.

Studies needed to be about HaH. In this study, we followed the definition of HaH as presented in the World Hospital at Home Congress 2023, as an acute clinical service that takes staff, equipment, technologies, medication and skills usually provided in hospitals and delivers that hospital care to selected people in their homes or in nursing homes. It substitutes for acute inpatient hospital care. Included HaH programs in this study had to fit this definition, interpreted as (i) defined inclusion/exclusion criteria, (ii) vitals monitoring, (iii) daily medical team reviews.

Studies also had to include at least one implementation context parameter as per CFIR (such as changes in funding, organizational characteristics or processes), and one implementation outcome as per RE-AIM, excluding studies solely reporting clinical outcome measures. Implementation strategies were not mandated as an inclusion criterion, as the primary objective was to examine links between implementation context and outcomes. Nonetheless, many included studies described strategies used to address contextual barriers and facilitators, which were subsequently mapped to implementation science frameworks during data synthesis. We chose these frameworks as they were deemed to be broad enough to capture the respective constructs (i.e. contextual determinants and outcomes, for CFIR and RE-AIM, respectively), enabling systematic cross-study comparison and identification of patterns in factors associated with successful HaH implementation at scale.

Included studies could be observational or interventional of any design (i.e., quantitative, qualitative or mixed methods), with primary data published in the English language. Trial protocols, systematic or scoping reviews, editorials and abstracts were excluded as they did not report primary data, but reference lists were reviewed to look for potential relevant studies. Studies on programs specifically targeted at COVID-19, post-hospital discharge programs or individual components of home hospitalization (e.g., remote patient monitoring), were excluded.

Data extraction

From the selected studies, authors extracted study type, country, implementation stage (as per EPIS), diagnoses of patients managed under the respective HaH programs, and participant characteristics. For HaH programs, early supported discharge (ESD) models referred to acceptance of patients referred after a period of ward hospitalisation; admission avoidance (AA) models referred to acceptance of patients referred without prior ward hospitalisation, e.g. from the emergency department or primary care. Programs could do either or both models.

We coded implementation context, as per CFIR domains and subdomains and implementation outcomes as per the RE-AIM framework. Following this, authors examined whether any implementation strategies had been applied in response to identified contextual factors – that is, whether a barrier or facilitator to scale had promted a specific strategy that resulted in a measurable outcome, and coded these to the ERIC framework. For example, in one study [27] a lack of access to medical records and hospital protocols in the community setting was identified as a barrier (context: information technology infrastructure), which promoted information sharing via a shared computer medical file (strategy: change record systems) and a local helpline for General Practitioners (strategy: provide local technical assistance), resulting in increased acceptability (outcome). A detailed list of implementation context, strategies and outcome contextualized to HaH are provided in the supporting tables (eTable 2–4).

Risk of bias

Two reviewers (S.K., R.C.) independently assessed the risk of bias (ROB) of included studies using the mixed methods appraisal tool (MMAT), version 2018 [17]. Disagreements were resolved through discussion and consensus. The MMAT was selected as it is designed to appraise multiple study designs within a single review, including qualitative and mixed methods studies – reflecting the heterogenous nature of the HaH implementation literature. All included studies were first assessed against two screening questions, confirming the presence of clear research questions and that the collected data were sufficient to address them. Studies were then appraised according to their design category using the corresponding five methodological quality criteria, with each criterion scored as ‘Yes’, ‘No’, or ‘Can’t tell’. A final quality score was calculated as the proportion of criteria rated ‘Yes’, (maximum score of five) providing an indicative measure of methodological quality for each study (Table 1). Consistent with the MMAT guidance, these scores were not used as a basis for exclusion; rather, they served as a contextual reference to inform the interpretation of findings.

Table 1.

Characteristics of Included Studies including current EPIS stage, setting, model of care and diagnoses of patients managed under each of the HaH programs

Reference no.	Author, Year	Methods	Country	EPIS Stage	Setting	HaH Model	Diagnoses	Volume or Scale	Participants	ROB (yes) / 5
[21]	Zimbroff, 2019	Mixed methods (interviews, document analysis)	USA	Implementation	Urban	Both	Hospitalized patients	NA	• 5 program leaders	3
[26]	Levine, 2021	Interviews and focus groups	USA	Exploration	Rural	NS	NS	NA	• 9 HaH doctors • 9 HaH nurse (4 nurses, 2 nurse practitioners) • 3 HaH administrators	5
[27]	Moulin, 2019	Survey	France	Exploration	Mixed	ESD	Type 1 Diabetes in children	NA	• 30 referring GPs	5
[28]	Nilsson, 2018	Ethnographic	Sweden	Preparation	Urban	ESD	Type 1 Diabetes in children	NA	• Ethnographic study of 6 teams meetings of 24 individuals (10 paediatricians, 8 paediatric nurses, 4 dieticians, 2 social workers)	5
[29]	Barrue, 2020	Interviews	Spain	Implementation	Urban	NS	End of life patients	NA	• 9 HaH nurses	5
[30]	Brody, 2019	Interviews	USA	Implementation	Urban	Both	NS	NA	• 19 clinical, administrative, insurance executive • 7external partners (paramedicine, phlebotomy, durable medical equipment, pharmacy, lab, EHR)	5
[31]	Cecile MA Utens, 2013	Survey	Netherlands	Implementation	Urban	ESD	COPD	Not mentioned	• 45 referring doctors (10 pulmonologists, 10 residents, 25 GPs) • 15 HaH nurses (9 hospital-based, 6 community-based)	3
[32]	Chua, 2022	Interviews	Singapore	Implementation	Urban	Both	NA	NA	• 13 referring doctors • 2 HaH doctors • 4 HaH nurses • 2 HaH pharmacists • 2 HaH therapists	5
[33]	Dismore, 2019	Interviews	UK	Implementation	Urban	ESD	COPD	NA	• 15 physicians (11 respiratory consultants, 3 acute care physicians, 1 specialist registrar) • 11 nurses • 4 mangers (general manager, operational service manager, finance)	5
[34]	Emonds, 2021	Descriptive cohort (quality improvement)	USA, Boston	Implementation	Urban	Both	CCF, COPD, Patients requiring IV medications	102 admissions between May 21 2019 and March 27 2020	• HaH pharmacists	5
[35]	Gorbenko, 2022	Interviews	USA	Implementation	Mixed	Both	NS	NA	• 14 Clinical/medical directors, operational and program managers	5
[36]	Hernandez, 2018	Quantitative descriptive	Spain	Implementation	Urban	Both	Adults - respiratory (35%), cardiology (17%), post-surgery (17%), oncology (11%), acute illness (8%)	4165 admissions over 10 years, increased bed capacity from 12 (2006) to 36 (2016)	• HaH staff (unspecified)	5
[37]	Karacaoglu, 2021	Interviews	Scotland	Implementation	Urban	ESD	Geriatric	NA	• 3 HaH advance practice nurse • 5 HaH healthcare support worker • 2 HaH pharm tech • 3 HaH management	5
[38]	Knight, 2021	Survey	UK	Implementation	Mixed	Both	NS		• 130 hospitals across the UK participated in Society for Acute Medicine Benchmarking Audit collecting hospital-level data on the structure and organisation of acute care delivery	3
[39]	Lippert, 2017	Cross sectional descriptive and survey	Canada	Implementation	Urban	Both	Children- oncology, haematology, bone marrow transplant	Since inception, the H@H program has served 136 patients and their families during a total of 1701 home visits	• HaH staff including pharmacists, nurses, physicians (not specified number)	3
[40]	Maniaci, 2022	Survey	USA	Implementation		Both	NS	NA	• HaH doctors • HaH nurses	4
[41]	Mora, 2022	Descriptive cohort	Spain	Implementation	Urban	ESD	Oncology neurosurgery patients	NA	• Description of implementation of an oncology neurosurgery program	1
[42]	Ohvanainena, 2020	Survey	Finland	Implementation	Mixed	Both	Infections (53%), palliative (25%). 63% adults only, 35% both adults and children	NA	• 34 HaH programs	4
[43]	Pian, 2019	Survey	USA	Implementation	Urban	Both	General Medicine Adults	NA	• 2 physicians • 4 nurses • 3 research assistants	3
[44]	Ramshorst, 2022	Descriptive cohort	Netherlands	Implementation	Urban	Both	Cardiac (infective endocarditis and fluid overload)	NA	• 16 HaH nurses	3
[45]	Rodriguez-Cerrillo, 2012	Descriptive cohort (quality improvement)	Spain	Implementation	Urban	Both	Not specified	1570 admissions/ year (during year of study 2008)	• Description of implementation of a quality monitoring system	5
[46]	Siu, 2019	Descriptive cohort and mixed qualitative methods	USA	Implementation	Urban	AA	General Medicine Adults	NA	• Used a framework to code interventions	5
[47]	Aasen, 2022	Interviews	Norway	Sustainment	Urban	ESD	Paediatrics: newly diagnosed diabetes, infections and cancer	NA	• 13 HaH nurses • 3 HaH doctors	4
[48]	Levine, 2022	Randomized controlled trial	USA	Sustainment	Urban	Both	Infection, heart failure, COPD and asthma	NA	• 84 remote care patients and 88 controls	3
[49]	Lim, 2021	Cross sectional descriptive	Australia	Sustainment	Urban	Both	Acute conditions and rehabilitation	45 acute and 10 subacute beds	• Referring doctors • Nurses on wards involved in inpatient care	4
[50]	Mittaine-Marzac, 2021	Descriptive cohort	France	Sustainment	Urban	Both	Haematology and Oncology	365 patients over 5 weeks to 473 patients over 1 month at end of study	• Description of scale-up process	4
[51]	Wergeland, 2022	Interviews	Norway	Sustainment		ESD	Stem cell transplant patients	NA	• 6 HaH nurses • 8 Hospital nurses	4

Summary of each study including the volume or scale-up in each study (if applicable), participants involved in the studies and risk of bias (ROB) according to the MMAT

Note: NA (not applicable), NS (not specified), ESD (early supported discharge), AA (admission avoidance), HaH (hospital-at-home), COPD (Chronic Obstructive Pulmonary Disorder), CCF (congestive cardiac failure)

Data synthesis

Data synthesis was conducted through a structured coding process using Microsoft Excel. Each included study was assigned a study identification (ID) number and the context of the free text in the introduction, methods and results, coded against the relevant sub-domains of each framework – CFIR, ERIC and RE-AIM. From each sub-domain, relevant exerpts from studies were extracted and recorded, enabling direct traceability between the source data and framework mapping. Once coding was complete, the frequency of studies mapped to each subdomain was calculated to identify which implementation context, strategies and outcomes were most reported across the studies.

Deviation from study protocol

In our original protocol (https://osf.io/mbxpy/), we planned to include only scaling studies – defined as increase in number of beds or number of HaH programs within a hospital or healthcare group from beginning to the end of the study period. However, due to paucity of such studies (only six) and inconsistent reporting of the unit of scale up, we pragmatically decided to broaden our inclusion to a wider range of HaH implementation studies. We also originally stated a limited set of outcome and contextual factors in the protocol; this was broadened to the entire set of RE-AIM- and CFIR-covered factors to capture the richness of the data we found in the literature.

Results

The search yielded 7523 records. Of these, 2510 duplicate records were removed before screening and 4899 excluded following title and abstract screening. Forty-one reports were excluded as only abstracts were available. The remaining 73 records were considered in full text. Of these, 47 were excluded. Studies were excluded for the following reasons: no reported HaH evidence, absence of contextual factors or primary data, no implementation outcomes, and a specific focus on COVID-19 or a single component of home hospitalization. One article was identified from searching grey literature, and was included in the review. A total of 27 articles were included in the final review. The PRISMA flow diagram is present in eAppendix Fig. 1.

Study characteristics

Characteristics of the studies are summarized in Table 1. The studies included various clinical conditions such as chronic obstructive pulmonary disease, asthma, congestive cardiac failure, infective endocarditis, haematology and oncology conditions, neurosurgery, and palliative care across both adult and paediatric populations.

These studies encompassed populations from North America (USA, Canada), Europe (France, UK, Scotland, Sweden, Netherlands, Finland, Norway, Spain), and the Asia-Pacific region (Australia, Singapore). Most (n = 16) studies reported on HaH programs that included both AA and ESD models, eight included ESD only [27, 28, 31, 33, 37, 41, 47, 51], one AA only [46], and two not specified [16, 29].

The majority (n = 19) of the included studies [21, 29–46] were in the implementation phase, five studies [47–51] in the sustainment phase, and three studies [26–28] in the exploration and preparation phases according to the EPIS framework (Table 1). Of the included studies, six studies [34, 36, 39, 45, 49, 50] reported an increase in volume or scale of the studied HaH program. Of the 27 included studies, the majority employed quantitative descriptive study designs (n = 15), followed by qualitative studies (n = 10), one mixed methods study combining interviews and document analysis, and one randomized controlled trial.

Of the 27 studies, 25 had a clear research question and data sufficient to address it (screening questions). In two studies [21, 41] it was not clear whether the data were sufficient to answer the research question. The medial ROB score for all studies was four out of five (eAppendix Table 2). We used the ROB score as a guidance during interpretation of findings, acknowledging that studies with lower scores may carry greater uncertainty in their reported implementation outcomes.

Implementation context

Implementation determinants were extracted, identified as barriers and/or facilitators and coded onto the CFIR framework (Table 2 and eAppendix Table 3). Of 48 possible CFIR implementation determinants, 24 determinants featured in the included studies, with at least one featuring in each of the five CFIR domains. The most commonly featured implementation determinant was Inner setting, followed by Outer setting, then Characteristics of individuals (33, 20 and 14 times across included studies, respectively).

Table 2.

Implementation determinants for reported Hospital at Home models of care

CFIR domain	CFIR measure	CFIR description	Definition of this measure in relation to HaH implementation	Barrier/Facilitator/Both	Studies (# and reference no.)
I. Intervention Characteristic: this refers to characteristics of HaH models of care	A. Innovation Source	The group that developed and/or visibly sponsored use of the innovation is reputable, credible, and/or trustable.	HaH and supporting organizations was reputed to have high standards of care	Facilitator	1 [41]
	D. Innovation Adaptability	The innovation can be modified, tailored, or refined to fit local context or needs.	Interventions that are suitable for the hospital environment may not be entirely suitable for the home environment, and may need adapting	Barrier	1 [33]
	H. Innovation Cost	The innovation purchase and operating costs are affordable.	Need for a funding model for HaH that is financially sustainable	Barrier	2 [26, 35]
II. Outer setting: this refers to aspects of the external to a host organization, wider health system setting, in which HaH models of care are implemented	A. Critical Incidents	Large-scale and/or unanticipated events disrupt implementation and/or delivery of the innovation.	Pandemic leading to an increased need for medical care and hospital beds resulting in an increase in demand for HaH	Barrier	1 [50]
	B. Local Attitudes	Sociocultural values (e.g., shared responsibility in helping recipients) and beliefs (e.g., convictions about the worthiness of recipients) encourage the Outer Setting to support implementation and/or delivery of the innovation.	Patient perceptions towards hospitals or HaH (including direct patient perceptions and clinician perspective of patient perceptions)	Both	3 [26, 28, 41]
			Referring teams’ perceptions towards hospitals or HaH	Both	3 [27, 41, 50]
	C. Local Conditions	Economic, environmental, political, and/or technological conditions enable the Outer Setting to support implementation and/or delivery of the innovation.	Variations in local conditions affect the access and ability to effectively develop HaH services	Both	3 [26, 32, 38]
			Need for technological support and infrastructure for the implementation of HaH	Barrier	1 [26]
			Geographical challenges for HaH	Barrier	5 [26, 35, 41, 42, 49]
	D. Partnerships & Connections	The Inner Setting is networked with external entities, including referral networks, academic affiliations, and professional organization networks.	Establishing a strong network with hospitals, referral teams and professional organizations	Both	4 [35, 37, 42, 49]
	E. Policies & Laws	Legislation, regulations, professional group guidelines and recommendations, or accreditation standards support implementation and/or delivery of the innovation.	Availability of a regulatory model to enable provision of acute care at home	Both	1 [42]
	F. Financing	Funding from external entities (e.g., grants, reimbursement) is available to implement and/or deliver the innovation.	Availability of reimbursement for hospital admission	Both	4 [26, 35, 36, 39]
			Availability of external partners or commercial payor contracts to support the financing of HaH	Facilitator	1 [35]
	G. External Pressure 2. Market Pressure	Competing with and/or imitating peer entities drives implementation and/or delivery of the innovation.	Competing pressure with hospitals who depend on admissions for revenue	Barrier	1 [26]
III. Inner setting: this refers to the internal organizational structures and characteristics that affect HaH implementation locally	A. Structural Characteristics	Infrastructure components support functional performance of the Inner Setting. Note: Use this construct to capture themes related to Structural Characteristics that are not included in the subconstructs below.	Controlling rate of scale of HaH to ensure efficiency while maintaining the reputations and standards of the HaH program	Both	2 [30, 40]
	1. Physical Infrastructure	Layout and configuration of space and other tangible material features support functional performance of the Inner Setting.	Availability of a conducive, permanent workspace to promote productivity	Barrier	1 [37]
	2. Information Technology Infrastructure	Technological systems for tele-communication, electronic documentation, and data storage, management, reporting, and analysis support functional performance of the Inner Setting.	Streamlining documentation processes and access to patient records	Barrier	6 [27, 30, 31, 33–35]
			Building technological systems compatible with HaH	Barrier	2 [35, 47]
	3. Work Infrastructure	Organization of tasks and responsibilities within and between individuals and teams, and general staffing levels, support functional performance of the Inner Setting.	Effective organization of manpower and job roles to increase productivity and maintain staff satisfaction	Both	5 [28, 37, 39, 42, 43]
			External resources available to support HaH	Both	3 [30, 32, 38]
	B. Relational Connections	There are high quality formal and informal relationships, networks, and teams within and across Inner Setting boundaries (e.g., structural, professional).	Maintaining a good working relationship amongst HaH staff	Facilitator	3 [32, 37, 43]
	C. Communications	There are high quality formal and informal information sharing practices within and across Inner Setting boundaries (e.g., structural, professional).	Ensuring clear communication channels between HaH team and external parties	Both	2 [31, 32]
	D. Culture	There are shared values, beliefs, and norms across the Inner Setting. Note: Use this construct to capture themes related to Culture that are not included in the subconstructs below.	Establishing shared values, beliefs and a supportive work culture within the HaH team	Facilitator	2 [43]
		(4) Recipient-Centeredness There are shared values, beliefs, and norms around caring, supporting and addressing the needs and welfare of recipients	Establishing shared values and mutual respect between the HaH team and patient	Facilitator	1 [28]
	E. Tension for Change	The current situation is intolerable and needs to change.	Problems identified with the current model of care prompting a need for change	Facilitator	1 [34]
	I. Mission Alignment	Implementing and delivering the innovation is in line with the overarching commitment, purpose, or goals in the Inner Setting.	Having a shared goal and vision within the organization	Both	2 [28, 43]
			Setting clear goals and targets for specific job groups	Both	1 [34]
	J. Available Resources	Resources are available to implement and deliver the innovation. Note: Use this construct to capture themes related to Available Resources that are not included in the subconstructs below.	Availability of resources (manpower) to run a HaH service	Barrier	3 [26, 37, 42]
IV. Characteristics of individuals: this refers to the people involved in the implementation of HaH care models – in any role relevant to implementation	E. Implementation Leads - Opportunity	Individuals who lead efforts to implement innovation. The individual(s) has availability, scope, and power to fulfill Role.	Capable HaH leadership dedicated to building the HaH service	Both	1 [42]
	E. Implementation Leads - Motivation	The individual(s) is committed to fulfilling their role.			1 [28]
	H. Innovation Deliverers - Capability	The individual(s) has interpersonal competence, knowledge, and skills to fulfill Role.	Ensuring the staff delivering care have the capabilities to fulfil the role	Both	5 [29, 31, 33, 41, 43]
	H. Innovation Deliverers - Opportunity	The individual(s) has availability, scope, and power to fulfill Role.	Empowering staff to manage challenges of HaH	Facilitator	1 [29]
	H. Innovation Deliverers - Motivation	The individual(s) is committed to fulfilling Role.	Keeping staff motivated by aligning staffs’ values with the mission of HaH	Facilitator	4 [29, 31, 35, 37]
	I. Innovation Recipients - Capability	Individuals who are directly or indirectly receiving the innovation. The individual(s) has interpersonal competence, knowledge, and skills to fulfill Role.	Ensuring that patients have the ability to manage their own care at home	Barrier	2 [26, 49]
V. Process: this refers to the process of implementing the reported HaH model of care	A. Teaming	Join together, intentionally coordinating and collaborating on interdependent tasks, to implement the innovation	Availability of external partners to support HaH services	Barrier	1 [35]

Note: reported implementation determinants mapped onto the Consolidated Framework for Implementation Research, CFIR [14]

Implementation determinants vaired in how frequently they were reported across studies, with a median number of two studies per determinant. The most commonly reported determinant, appearing in six studies [27, 30, 31, 33–35], was streamlining documentation processes and access to patient records (inner setting; information technology infrastructure). Three determinants were each reported in five studies: effective organisation of manpower and job roles to increase productivity (inner setting – work infrastructure) [28, 37, 39, 42, 43]; ensuring staff delivering care have capabilities to fulfil the role (characteristics of individuals – capability of innovation deliverers) [29, 31, 33, 41, 43]; and geographical challenges of HaH (outer setting – local conditions) [26, 35, 41, 42, 49].

Implementation strategies

Implementation strategies were coded to the ERIC framework (Table 3 and eAppendix Table 4). Of 73 possible ERIC strategies, 25 featured in the included studies across all nine strategy clusters. The most commonly cited strategy clusters were: change infrastructure, support clinicians, and use evaluative and iterative strategies.

Table 3.

Implementation strategies used to implement Hospital at Home models of care

Strategy cluster	Strategy	Definition	Strategy as applied to HaH implementation	Studies (# and reference no.)
1. Use evaluative and iterative strategies	18 Conduct local need assessment	Collect and analyze data related to the need for the innovation	Collect and analyse data related to existing ambulatory services (e.g. outpatient antibiotic therapy (OPAT), home-based hospice care) to identify patient profile HaH should target	1 [39]
	27 Develop and organize quality monitoring systems	Develop and organize systems and procedures that monitor clinical processes and/or outcomes for the purpose of quality assurance and improvement	Develop a quality monitoring system that includes processes, patient satisfaction, objectives, staff training, incident reporting and non-adherence to protocols	5 [34, 35, 39, 42, 45]
	4 Assess for readiness and identify barriers and facilitators	Assess various aspects of an organization to determine its degree of readiness to implement, barriers that may impede implementation, and strengths that can be used in the implementation effort	Assess readiness by identifying regulatory and finance barriers, logistical barriers, staff readiness, patient acceptance, and technological facilitators.	2 [21, 28]
	46 Obtain and use patients/ consumers and family feedback	Develop strategies to increase patient/consumer and family feedback on the implementation effort	Deploy patient/family satisfaction surveys and referring clinician satisfaction surveys	2 [40, 45]
	56 Purposefully reexamine the implementation	Monitor progress and adjust clinical practices and implementation strategies to continuously improve the quality of care	Carry out regular audits on internal processes, external supplies and conduct continuous process improvement.	5 [34–36, 41, 45]
	61 Stage implementation scale up	Phase implementation efforts by starting with small pilots or demonstration projects and gradually move to a system wide rollout	Expanding the program incrementally in various ways: (a) staged introduction of specific patient populations, (b) staged expansion of geographical catchment, (c) staged expansion of referral hours, (d) staged expansion of referral sources (e.g. to include GPs), d) staged introduction of new resources such as mobile diagnostic units, virtual consultation tools or new drug formularies, (e) staged addition of adjacent models of care (e.g. 30-day post-acute transition, home observation/rehab units). Regular evaluation at each stage allows for adjustments and improvements before the next phase of growth.	7 [21, 30, 36, 37, 39, 46, 50]
2. Provider interactive assistance	33 Facilitation	A process of interactive problem solving and support that occurs in a context of a recognized need for improvement and a supportive interpersonal relationship	Designated individuals or teams who can guide the implementation, troubleshoot issues, coordinate between hospital-based and home-based care teams.	3 [27, 31, 47]
	53 Provide clinical supervision	Provide clinicians with ongoing supervision focusing on the innovation. Provide training for clinical supervisors who will supervise clinicians who provide the innovation	Create clear communication protocols to supervise clinicians in HaH	1 [39]
3. Adapt and tailor to context	51 Promote adaptability	Identify the ways a clinical innovation can be tailored to meet local needs and clarify which elements of the innovation must be maintained to preserve fidelity	Customising the implementation approach to fit (a) needs of different patient populations, (b) needs of different services sites (e.g. aged care), d) available resources, and e) work environment	7 [35, 37, 45–49]
4. Develop stakeholder interrelationships	35 Identify and prepare champions	Identify and prepare individuals who dedicate themselves to supporting, marketing, and driving through an implementation, overcoming indifference or resistance that the intervention may provoke in an organization	Identify and prepare key individuals to drive change by promoting HaH to their colleagues, address concerns and help overcome resistance e.g. by guiding the team through the adoption process.	1 [41]
	48 Organize clinician implementation team meetings	Develop and support teams of clinicians who are implementing the innovation and give them protected time to reflect on the implementation effort, share lessons learned, and support one another’s learning	Meetings to bring together key clinicians, including doctors, nurses, pharmacists, and allied health professionals, to discuss activities, clinical outcomes, refining care protocols, and ensuring that the team stays aligned with program goals and best practices.	2 [36, 39]
5. Train and educate stakeholders	19 Conduct ongoing training	Plan for and conduct training in the clinical innovation in an ongoing way	Plan and conduct HaH specific training including both clinical skills (e.g., managing complex cases remotely, using mobile diagnostic tools) and non-clinical skills (e.g., communication with patients and families, navigating home environments). Ongoing training including on-the-job training ensures that staff stay up-to-date on the latest technologies, clinical guidelines, and best practices in home-based care.	9 [26, 29, 33, 34, 36, 37, 44, 45]
	20 Create a learning collaborative	Facilitate the formation of groups of providers or provider organizations and foster a collaborative learning environment to improve implementation of the clinical innovation	Facilitate bringing together healthcare providers, administrators, and other stakeholders from different hospitals and health systems that are implementing or have implemented HaH models.	5 [29, 34, 37, 47]
	60 Shadow other experts	Provide ways for key individuals to directly observe experienced people engage with or use the targeted practice change/innovation	HaH staff to observe and learn from established programs and professionals with HaH experience, including site visits and professional exchanges.	2 [34, 41]
6. Support clinicians	21 Create new clinical teams	Change who serves on the clinical team, adding different disciplines and different skills to make it more likely that the clinical innovation is delivered (or is more successfully delivered)	“Hiring dedicated clinical and non-clinical staff for HaH (e.g. nurses, physiotherapy, pharmacists, pharmacy technicians and support staff). Creation of dedicated multidisciplinary teams to discuss patient care plans and facilitate verbal handovers between teams and shifts.”	8 [30, 34, 39, 41, 44, 46, 50, 51]
	30 Develop resource sharing agreements	Develop partnerships with organizations that have resources needed to implement the innovation	Develop partnerships between hospitals, home health agencies, and community care providers, including establishing contractual standards that address response times, patient safety and emergency escalation.	4 [30, 35, 45, 46]
	59 Revise professional roles	Shift and revise roles among professionals who provide care, and redesign job characteristics	Define job scope of each professional role within HaH, including revision of existing roles or emergence of new roles.	2 [31, 46]
	101 Create new protocols (new category)		Creation of new protocols ranging from clinical, monitoring, telemedicine, pharmacy, safety and emergencies ensures that clinicians have clear steps to follow.	6 [21, 30, 34, 39, 41, 45]
7. Engage consumers	39 Intervene with patients/consumers to enhance uptake and adherence	Develop strategies with patients to encourage and problem solve around adherence	Develop strategies to educate patients and caregivers about the benefits of HaH, and outreach to referring clinicians about benefits and criteria for HaH	5 [41, 45, 47, 49, 51]
8. Utilise financial strategies	1 Access new funding	Access new or existing money to facilitate the implementation	Funding may include grants from government health agencies, securing private or philanthropic investments, or collaborating with insurance providers.	2 [35, 39]
	34 Fund and contract for the clinical innovation	Governments and other payers of services issue requests for proposals to deliver the innovation, use contracting processes to motivate providers to deliver the clinical innovation, and develop new funding formulas that make it more likely that providers will deliver the innovation	Securing funding strategy for HaH similar to inpatient care (e.g. through government funding or insurance), and additional financial resources for the setup of HaH as a new service	2 [35, 37]
9. Change infrastructure	11 Change physical structure and equipment	Evaluate current configurations and adapt, as needed, the physical structure and/or equipment (e.g., changing the layout of a room, adding equipment) to best accommodate the targeted innovation	“A dedicated central space conducive for staff to connect and work. Availability of mobile devices, portable diagnostics, home visit bags, cars for transport. Home visit bags well equipped and stocked. “	6 [26, 29, 37, 44, 45, 49]
	12 Change record systems	Change records systems to allow better assessment of implementation or clinical outcomes	Adapting or integrating electronic health records (EHR) to support seamless care in both hospital and home setting to (a) enable real-time data sharing between hospital-based teams and HaH providers and community partners, (b) medication dispensing and administration, (c) automated capture of charges for billing	8 [27, 31, 34, 35, 45, 46, 50, 51]
	13 Change service sites	Change the location of clinical service sites to increase access	Move pharmacy services to be on site at an internal inpatient pharmacy facility	2 [34, 46]
	22 Create or change credentialing and/or licensure standards	Create an organization that certifies clinicians in the innovation or encourage an existing organization to do so. Change governmental professional certification or licensure requirements to include delivering the innovation. Work to alter continuing education requirements to shape professional practice toward the innovation	Credentialling by national bodies (e.g. CMS in USA), or international organisations (e.g. ISO 9001) can help to standardise procedures and protocols	2 [35, 45]
	44 Mandate change	Have leadership declare the priority of the innovation and their determination to have it implemented	Institution leadership to determine reporting structure and organization of hospital at home	1 [28]
	104 Use Technology (new strategy)		“Develop and deploy telemedicine platforms for virtual consultations to supplement in-person visits in order to maximise staff efficiency Training to ensure that staff are able to use these tools effectively for clinical assessment “	6 [34, 41, 46, 48, 49, 51]

Note: reported implementation strategies mapped onto the Expert Recommendations for Implementing Change framework, ERIC [15]

The median number of studies that referenced each strategy was two. The most commonly referenced strategies were: (1) conduct ongoing training – plan and conduct HaH specific training including clinical and non-clinical skills, on the job training, ensure staff stay up-to-date with the latest technologies, clinical guidelines and best practices in home-based care (nine studies [26, 29, 33, 34, 36, 37, 44, 45], (2) create new clinical teams – hiring dedicated clinical and non-clinical staff for HaH and multidisciplinary teams (eight studies [30, 34, 39, 41, 44, 46, 50, 51] and (3) promote adaptability – customising the HaH program to fit the needs of the patient population, different services, available resources and work environment(seven studies [35, 37, 45–49]. We created two new categories not found in the original ERIC framework: (1) create new protocols and (2) use technology, as the data we found in the studies did not map to any of the existing strategy categories in ERIC.

Only one study [48] measured the effectiveness of the strategy implemented, demonstrating that remote physician visits were non-inferior to in-hospital physician care based on the primary outcome of adverse event count and patient experience using the Picker Patient Experience Questionnaire.

Implementation outcomes

Implementation outcomes were categorized according to the RE-AIM framework (Table 4 and eAppendix Table 5). All of the implementation outcomes were aligned to the framework, which we then sub-categorized into specific indicators relevant to HaH. The majority of reach indicators (e.g. capacity, volume) and adoption (number of referring physicians) lacked a denominator. For patient, caregiver or staff outcomes, only two studies [46, 48] used validated questionnaires to measure patient-reported outcomes or experience, and one study [43] for assessment of HaH staff burnout. The rest of the studies used internally developed questionnaires, qualitative interviews and surveys to assess outcomes.

Table 4.

Implementation outcomes of hospital at home

Outcome	Indicators Identified	Examples [Reference no.]
REACH: Representativeness of eligible patients who participated in the HaH model of care	Capacity	HaH bed capacity [36, 42]
	Volume	Number of HaH admissions over time [21, 36, 45, 51]
	Acceptance	Proportion of eligible patients enrolled into HaH [21, 36, 39]
	Dropout	Proportion of HaH patients that prematurely left the program [39]
EFFECTIVENESS: HaH model of care effects on targeted outcomes	Unplanned escalationsescalations	Proportion of HaH episodes that required an unscheduled transfer to hospital during the episode. The definition of unscheduled is not clearly defined. [37, 45, 46]
	Mortality	Mortality rate of HaH patients 30 days post discharge [36]
	Readmission / ED Visits	Proportion of HaH patients that had hospital readmissions and ED room visits at specified time interval (e.g. 30 or 90 days) [36, 37, 46]
	Adverse events	Proportion of HaH patients that had fall, pressure, injury, delirium, medication incidents, medical equipment failures [34, 45, 48]
	Patient-reported experience or outcome measures	Patient satisfaction (e.g. HCAHPS, Picker Patient Experience Questionnaire, internal tool, proportion of compliments received, qualitative interviews) [45–48]
	Caregiver-reported experience or outcome measures	Caregiver experience (qualitative interviews) [47]
ADOPTION: Representativeness of participating settings and providers in reported HaH models of care	Hospital Units	Proportion of hospital units that have access to a HaH [38]
	Referring Physicians	Number of referring physicians [45]
		Proportion of patients being referred from a hospital apart from the hospital HaH was initially implemented [21]
		Proportion of patients being referred from home or primary care (rather than emergency departments) [21]
IMPLEMENTATION: Extent to which HaH models of care were consistently implemented	Visit frequency	Average number of home visits / phone calls per day or patient or year [36, 37, 39, 42]
	Visit duration	Average duration of individual visit including transport [39, 48]
	Inappropriate referrals	Percentage of patients referred that are not suitable for HaH [45]
	Compliance to protocols	Compliance to protocols by internal staff and external suppliers [45]
MAINTENANCE: Extent to which HaH models of care reportedly became part of routine organisational practices and maintained effectiveness over time	Length of stay	Length of stay in HaH [36, 46]
	Patient complexity	Average number of comorbidities in HaH patients, need for intravenous therapies, complex dressing and care [36]
	Cost	Cost savings [34, 36]
	Referring physician satisfaction	Referring doctors satisfaction (qualitative, internal survey tool) [27]
	HaH staff satisfaction	Staff satisfaction (internal survey tool, qualitative interviews, Method for ASsessment of Telemedicine applications framework) [29, 31, 32, 36, 40, 43, 44, 47]
	HaH staff burnout	Staff burnout survey (Mini Z Burnout Survey) [43]
	HaH staff perceptions of appropriateness	Staff perceptions of appropriateness (qualitative interviews) [28, 33, 39]

Note: reported outcomes mapped onto the Reach, Effectiveness, Adoption, Implementation, Maintenance Framework, RE-AIM [16]; HaH = Hospital at Home

Discussion

In this systematic review, established implementation frameworks were used to map implementation determinants, strategies and outcomes involved in the implementation and scale-up of HaH worldwide. For the first time to our knowledge, we mapped available knowledge, identified gaps with current HaH implementation evidence and proposed a synthesised framework for HaH implementation evaluation indicators.

Synthesis of HaH implementation evidence: what we know globally

As HaH programs by design reflect the health system in which they operate, cross-program comparison has historically been constrained by heterogeneity in clinical scope, funding models and reporting practices. A key contribution of this review is demonstrating that systematic application of implementation science frameworks can help to reveal coherent and generalizable patterns. By applying CFIR, ERIC and RE-AIM to a diverse seires of 27 studies spanning general medicine, cancer care, end-of-life across Europe, North America and Asia Pacific, we show that implementation determinants, strategies and outcomes converge and provide a common language in ways that can support cross-context learning.

The implementation determinants identified across CFIR domains are largely consistent with the other CFIR-based healthcare studies [18–20] and qualitative evidence synthesis by Wallis and colleagues [12], who similarly identified system factors including policy and financing structures, and resources and skills, as central to successful implementation. Our review extends their narrative synthesis in two important ways. First, mapping determinants systematically to CFIR constructs, we provide a replicable checklist against which implementors in new contexts can audit their local conditions before attempting scale-up. For example, local attitudes were defined to include both patient and referring teams’ perceptions of HaH safety or effectiveness; and physical infrastructure was important to create a conducive permanent workspace for HaH teams despite the care being out of the hospital. In areas where health system implementors perceive there may be local differences in health service context, we recommend further evaluation of the domains this review identified as impacting implementation to define them better and identify strategies that best address them. Second, we identify information technology infrastructure as the single most frequently reported barrier (six studies), a finding that sits within CFIR’s inner setting domain. The recurrence of this barrier across geographically and clinically diverse settings suggests it represents a structural constraint on HaH scaling rather than a context-specific challenge, and points to IT interoperability as a priority target for implementation strategy development. Our review also identifies the key strategy clusters – training, new clinical team creation, and adaptability – as the most commonly deployed responses, suggesting generalisability of these strategy-barrier pairings beyond any single health system.

The ERIC framework helped identify generalisable scale-up strategies, ranging from internal planning of processes, training and organization, to advocacy for policy and financing framework changes. The dominance of strategies in the ‘change infrastructure’ and ‘support clinicians’ clusters aligns with the Cochrane synthesis finding that workforce configuration and organisational support are foundational enablers [12]. Crucially, this review identified studies – Mittaine-Marzac [50] and Hernández (2018) – that report what can be understood as partial implementation cycles: a contextual determinant prompts a strategy, which in turn produces a measurable outcome. Mittaine-Marzac [50] demonstrated that COVID-19 (implementation determinant: critical incidents) drove the development of a combination of strategies like staged scale-up, new clinical teams being established, and changes to record systems, which in turn led to an increase in volume of HaH admissions by 30% over a five-week period. Similarly, Hernández [36] reported an increase in capacity from 12 virtual beds in 2006 to 36 in 2016, facilitated by reimbursement changes and the use of evaluative and iterative strategies. These cases provide the closest available HaH evidence of associative links between CFIR-identified barriers, ERIC-mapped strategies, and RE-AIM outcomes. They represent exactly the kind of theory-informed, prospective evaluation that the field now needs to pursue at scale, moving toward designs capable of establishing causal rather than merely associative relationships between strategy selection and implementation success, as called for by Powell [25] in their research agenda for implementation strategy evaluation.

Weaknesses and gaps in HaH implementation evidence

Most strategies in the included studies were evaluated informally through clinician interviews or operational observation rather than formally tested, and only one study (Levine et al. 2022 [48]) measured strategy effectiveness using a validated tool. This pattern is not unique to HaH. Powell 25] have argued that implementation science as a field lacks prospective, controlled evaluations of individual implementation strategies, and that this gap constrains the development of an evidence-based menu of scale-up interventions applicable across contexts. Our review confirms that HaH is no exception: strategies are routinely selected on the basis of local logic and expert judgement rather than comparative evidence. The CFIR-ERIC matching tool offers a structured approach to strategy selection grounded in expert consensus, but its recommendations require empirical validation in HaH contexts – a gap this review’s framework is designed to help close by providing a structured starting-point for strategy specification and outcome measurement.

The weakest evidence in the included studies concerned implementation outcomes reporting. We applied RE-AIM to synthesise outcomes, as the most commonly used by clinical program implementors and evaluators [22, 23]. However, outcomes were ambiguously defined and conceived (the review team had to discuss the categorization extensively); often lacked denominators [24] (resulting in difficulty in comparisons within and between programs), and used non-standardised/validated survey tools (e.g. caregiver experience, referring physician satisfaction, or HaH staff satisfaction. This limits comparability and growth of a global evidence base. This echoes findings from D’Lima and colleagues (2022 [24]), whose systematic review of RE-AIM application across health research identified the absence of denominators and heterogeneity in outcome operationalisation as the primary barriers to cumulative implementation evidence. Kwan and colleagues (2019 [22]) further noted that RE-AIM is frequently applied post hoc and incompletely, particularly in the Adoption and Maintenance dimensions, which are the most directly relevant to scale-up decisions. Our review confirms this pattern specifically in HaH: adoption indicators (proportion of hospital units with HaH access; proportion of physicians referring) and maintenance indicators (staff satisfaction; programme longevity) were the least consistently reported dimensions across included studies. This gap is consequential: without standardised adoption and maintenance data, health system managers and policymakers face a lack of evidence at the scale-up phase.

Lastly, despite studies such as those by Mittaine-Marzac [50] and Hernández [36], most studies did not hypothesise links between challenges, strategies, and outcomes. We suspect that this is a weakness both of study design as well as study reporting, and limited use of implementation science methods in HaH evaluation as a health service. For example, although outer setting determinants were frequently identified, we cannot deduce which strategies addressed them most effectively. Likewise, staff satisfaction was a commonly reported implementation outcome (maintenance), but rarely linked to specific strategies. Future studies should clearly hypothesise and test these links to generate generalisable knowledge [25].

A global HaH implementation outcomes framework

To address the measurement gap identified above, we developed an integrative outcome framework – the HaH RE-AIM (Table 5) – grounded onto the RE-AIM framework and informed by the full implementation knowledge base curated through this review. RE-AIM was selected as the organising structure because it was the framework applied most commonly by clinical program implementors and evaluators in the included studies [22, 23], and because its five dimensions map directly onto the questions that health system leaders ask when considering whether and how to scale a service. Prior RE-AIM applications in home-based and community care settings have consistently identified denominators and standardised tools as the most significant barriers to comparability [24]; the HaH RE-AIM addresses both by proposing standard definitions and denominator specifications for each indicator. The framework is designed to be modular: a programme evaluating the impact of a clinician education campaign would apply the Adoption and Reach dimensions; a programme assessing long-term viability would apply Maintenance; implementation of a new clinical pathway may use all domains. It may also be used to facilitate comparative studies of HaH implementations, or longitudinally – e.g., they may be used to offer a longitudinal evaluation of how a single HaH program is faring over time, against hospital standards, or against other HaH programs regionally or globally. Next steps in the development of the HaH RE-AIM framework include developing standardised tools for patient, caregiver, staff and referrer satisfaction, to facilitate wider cross-setting comparisons and reduce unwarranted variation in measures across studies.

Table 5.

Proposed indicators and definitions for hospital at home model of care implementation evaluation (HaH RE-AIM)

Outcome	Measure	Proposed Definition
REACH: Representativeness of eligible patients who participated in the HaH model of care	Capacity	Proportion of hospital beds that are HaH beds
	Volume	Proportion of all hospital admissions managed in HaH
	Acceptance	Proportion of referred patients that accepted HaH
	Dropout	Proportion of HaH patients who leave the service prematurely
EFFECTIVENESS: HaH model of care effects on targeted outcomes	Unplanned escalations	Proportion of HaH admissions requiring unplanned escalation to hospital
	Mortality	Proportion of HaH admissions with unplanned mortality during episode
	Readmission / ED Visits	Proportion of HaH discharges that reattend ED or readmitted to ward in 30 days after discharge
	Adverse events	Proportion of HaH admissions experiencing adverse event (e.g. falls, phlebitis, pressure injury, delirium, medication errors, medical equipment failures)
	Patient-reported experience or outcome measures	Average satisfaction score of patients (standardised tool required)
	Caregiver-reported experience or outcome measures	Average satisfaction score of caregiver (standardised tool required)
ADOPTION: Representativeness of participating settings and providers in reported HaH models of care	Hospital units	Proportion of hospitals within health system/nationally with access to HaH
	Referring physicians	Proportion of eligible patients who are referred to HaH Proportion of physicians (hospital or primary care) that have referred to HaH in select time period
IMPLEMENTATION: Extent to which HaH models of care were consistently implemented	Visit frequency	Average number of home visits or virtual visits by nurses or doctors per day
	Visit duration	Average number duration of home visits including travel time
	Inappropriate referrals	Proportion of referrals found to be inappropriate for HaH
	Compliance to protocols	Average compliance to select HaH protocol
	Bed occupancy rate (new)	Proportion of maximum bed days (bed capacity * time) that are occupied
MAINTENANCE: Extent to which HaH models of care reportedly became part of routine organisational practices and maintained effectiveness over time	Length of stay	Average length of stay in both HaH only and in episode (inpatient + HaH)
	Patient complexity	Average measure of complexity or acuity amongst HaH patients, e.g. CCI, max NEWS
	Cost	Average cost per bed day
	Referring physician satisfaction	Average satisfaction score of referring physicians (standardised tool)
	HaH staff satisfaction	Average satisfaction score of HaH staff (standardised tool required)
	HaH staff burnout	Average burnout scores of HaH staff e.g. Mini Z burnout tool
	HaH staff perceptions of appropriateness	Average appropriateness perception of HaH staff (standardised tool required)

Future outlook of HaH research

The global HaH evidence base has matured to a point where further descriptive pilots are less of a priority. Answering Lai and Ko’s (2024) call to shift from “does HaH work?” to “how do we scale HaH successfully?“, this review provides three practical tools for that next generation of research. The CFIR-mapped checklist of implementation determinants gives program leaders a structured pre-scale-up audit instrument to identify which barriers are likely to impede expansion before investment is committed. The catalogued ERIC strategies provide a selection menu matched to those barriers, replacing ad hoc strategy choice with evidence-informed options. The HaH RE-AIM offers a common set of indicators that allow implementation trajectories to be tracked over time, compared across sites, and reported to policymakers in terms directly relevant to scale-up decisions. Together, these tools provide the infrastructure for a learning HaH implementation system, where programs contribute to a cumulative, globally comparable evidence base rather than being evaluated in isolation.

Limitations

First, while our search strategy and subsequent mapping to implementation science frameworks was thorough and rigorous, this led to a time lag between the final literature search and manuscript publication. Given the practical resourcing constraints and the intensive process required for framework-based extraction, coding, and quantitative synthesis of implementation determinants, strategies, and outcomes, studies published after 2022 were not included. Second, a formal systematic grey literature search was not performed and expert opinon was used; this approach may limit reproducibility and may have resulted in omission of other relevant unpublished or non-indexed work. Due to the degree of information required to identify implementation factors, we also had to exclude abstracts and conference proceedings. Third, restricting the search to studies only in the English language may have exluded studies done in some European nations where HaH has been well-developed. Fourth, while the use of predefined frameworks as inclusion criteria may have introduced some restrictiveness, we conducted a careful and exhaustive mapping of all identified implementation concepts to the established frameworks. Concepts that did not align with existing categories were proposed as additional categories where appropriate, ensuring that no relevant implementation concept was overlooked or inappropriately categorized. The comprehensiveness of CFIR and RE-AIM also made it unlikely that relevant studies were excluded from the search strategy. The grounding in established frameworks supports the comparability and transferability of findings across diverse HaH programs and settings. Fifth, intervention details were not always clearly reported, hence there may be implementation factors that were not identified during the coding and we had to make some assumptions as to whether the reported factors were contextual factors, strategies or outcomes.Finally, our assessment of risk of bias is limited. Although a final MMAT quality score was calculated for each included study, it is acknowledged that the MMAT was not designed or validated for score aggregation and these score should be interpreted as a contextual reference rather than a measure of methodological quality. Furthermore, lower quality studies contributed equally to the synthesis, which may introduce some uncertainty in the interpretation of identified patterns.

Conclusions

This systematic review synthesised implementation evidence from 27 HaH studies across diverse clinical areas and health systems, mapping determinants to CFIR, strategies to ERIC, and outcomes to RE-AIM. The resulting checklist of implementation determinants, catalogue of scale-up strategies, and HaH RE-AIM outcome framework provide a structured basis for more consistent evaluation and comparison of HaH programs globally. Future studies should move toward prospective, controlled evaluation of implementation strategies, with standardised outcome reporting to enable cumulative evidence development across settings.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

AA

Admission avoidance

CCF

Congestive cardiac failure

CFIR

Consolidated Framework for Implementation Research

COPD

Chronic obstructive pulmonary disorder

ERIC

Expert Recommendations for Implementing Change

ESD

Early supported discharge

HaH

Hospital-at-home

RE-AIM

Reach, Effectiveness, Adoption, Implementation, Maintenance

Author contributions

SK and NS conceptualised the study. RC and LY collected the data. RC and SK wrote the first draft of the manuscript. All authors read and approved the final manuscript.

Funding

This study was funded by PHRG Grant PHRGOC23Jan-0002.

Data availability

All data generated or analysed during this study are from the published articles cited. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable (systematic review of available data).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.