Background: As dementia progresses, symptoms and concerns increase, causing considerable distress for the person and their caregiver. The integration of care between care homes and health care services is vital to meet increasing care needs and maintain quality of life. However, care home access to high-quality health care is inequitable. eHealth can facilitate this by supporting remote specialist input on care processes, such as clinical assessment and decision-making, and streamlining care on site. How to best implement eHealth in the care home setting is unclear.
Objective: The aim of this review was to identify the key factors that influence the implementation of eHealth for people living with dementia in long-term care.
Methods: A systematic search of Embase, PsycINFO, MEDLINE, and CINAHL was conducted to identify studies published between 2000 and 2020. Studies were eligible if they focused on eHealth interventions to improve treatment and care assessment or decision-making for residents with dementia in care homes. Data were thematically analyzed and deductively mapped onto the 6 constructs of the adapted Consolidated Framework for Implementation Research (CFIR). The results are presented as a narrative synthesis.
Results: A total of 29 studies were included, focusing on a variety of eHealth interventions, including remote video consultations and clinical decision support tools. Key factors that influenced eHealth implementation were identified across all 6 constructs of the CFIR. Most concerned the inner setting construct on requirements for implementation in the care home, such as providing a conducive learning climate, engaged leadership, and sufficient training and resources. A total of 4 novel subconstructs were identified to inform the implementation requirements to meet resident needs and engage end users.
Conclusions: Implementing eHealth in care homes for people with dementia is multifactorial and complex, involving interaction between residents, staff, and organizations. It requires an emphasis on the needs of residents and the engagement of end users in the implementation process. A novel conceptual model of the key factors was developed and translated into 18 practical recommendations on the implementation of eHealth in long-term care to guide implementers or innovators in care homes. Successful implementation of eHealth is required to maximize uptake and drive improvements in integrated health and social care.
Dementia is a progressive, complex neurodegenerative condition with a multitude of types and clinical presentations. It is the leading cause of death in the United Kingdom  and is projected to have the highest global proportional rise (264%) in suffering associated with a need for palliative care [ ]. Dementia is characterized by a complexity of care needs, which advance as the condition progresses [ ]. These needs span multiple domains of health care, and multimorbidity is common [ ]. Symptoms such as pain and breathlessness [ , ] cause significant distress for the person and their caregiver and increase toward the end of life [ ].
Over half of the people with dementia (58%) die in care homes in England . They are the main providers of end-of-life dementia care, with the average life expectancy on admission for a resident with dementia being 1 to 2 years [ ]. The term care home in the United Kingdom refers to both residential and nursing homes. These differ with regard to the provision of input from health care professionals, with nursing homes providing additional access to 24-hour on-site nursing care. Care homes require the resources to deliver multidisciplinary care to meet these advancing and acute dementia-specific needs [ ]. Providing access to good quality, continuous care throughout the dementia trajectory is essential. This can be achieved by integrating care homes with primary care, palliative care, and dementia care teams to enable multidisciplinary and specialist input on vital care processes [ ].
Care needs change and develop over time and cause considerable distress if left unmet . A comprehensive assessment by a multidisciplinary team to review medical, functional, mental, and social abilities is essential for this population with complex needs [ ]. Interprofessional collaboration is also required to share clinical expertise and experience, best inform complex clinical decisions concerning treatment for multimorbidities, and deliver appropriate care [ ]. Integrating these processes across services is widely acknowledged to improve person-centered treatment outcomes for older people with complex needs [ ] and reduce detrimental transitions between settings occurring in the final years of life, such as unplanned hospitalization [ ].
To integrate services and deliver continuous and coordinated care, established methods of communication are required to share information between systems about residents’ care needs and outcomes. A way to facilitate this is through the use of eHealth , which is defined as “health services and information delivered or enhanced through the internet and related technologies” [ ], encompassing an array of interventions that enable care to be delivered remotely.
The COVID-19 pandemic has had major implications for the use of eHealth in care homes. In response, former barriers to the integration of health and social care services in England have been liberalized, with restrictions on information sharing between services relaxed . The pandemic highlighted the lack of systems for efficiently sharing information between services nationally and internationally [ , ]. This compromised the delivery of care to meet the escalating health care needs and residents’ quality of life. Innovation was required to communicate between services for the comprehensive assessment of symptoms and management of care and outcomes. eHealth provides a way to do this [ ].
There is little evidence to guide the design and implementation of eHealth resources to manage symptoms and concerns for people with dementia in care homes. The key task at hand is to understand how we can scale-up eHealth interventions to embed them in routine care. Despite positive findings regarding their benefit , eHealth interventions are yet to attain widespread implementation in care homes, and the way of effectively achieving this remains unclear. This study aimed to explore factors that influence the implementation of eHealth interventions to support assessment and decision-making regarding care and treatment for people with dementia in care homes.
The review is reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses;) guidelines. A systematic review, using narrative synthesis and thematic analysis, was conducted to identify common facilitators of and barriers to eHealth implementation in care homes. The synthesis followed the guidance of Popay et al [ ], which provides a specific direction for reviews concerned with the implementation of interventions. The protocol for this review was registered on PROSPERO (International Prospective Register of Systematic Reviews; registration number CRD42020184587). Before registering this review, a search of PROSPERO was conducted to ensure that no similar reviews were underway. Our final search was performed in the week the review was registered (June 1, 2020). Originally, the scope of the review also included collecting data on intervention effectiveness and key components; however, given the breadth of evidence relating to these outcomes, the findings regarding effectiveness will be reported in a second review.
A total of 4 databases (Embase, PsycINFO, MEDLINE, and CINAHL) were searched for studies published in English from January 2000, with the final search being on April 28, 2020. The year 2000 was chosen as the cutoff year to exclude eHealth that may be outdated in the context of today’s technological advancements. A search strategy was developed with the help of an information support specialist and informed by scoping the literature for the types of eHealth interventions currently in use (seefor the full search strategy). A combination of Medical Subject Headings terms and keywords was used to develop a strategy based on the following concepts: dementia AND care homes AND eHealth AND assessment OR decision-making. The search strategy was complemented through reference chaining and citation tracking using Google Scholar, following the initial identification of studies from the database search. The eligibility criteria were developed using the PICO (population, intervention, control, and outcomes) acronym, following recommendations on its suitability and enhanced sensitivity when conducting qualitative systematic reviews [ ]. The criteria are outlined in .
- Population: residents with a diagnosis of dementia residing in a long-term care facility; studies that included residents with dementia in a mixed population
- Intervention: eHealth interventions that aimed to facilitate comprehensive assessment of care home residents or improve decision-making about care and treatment; eHealth interventions that enable care coordination between practitioners and the sharing of information between settings to facilitate integrated working, such as between care homes and health care services
- Outcome: data relating to factors that influence or inhibit the implementation of eHealth interventions in care homes
- Comparator: no restrictors
- Study design: all study designs that reported data relating to implementation
- Population: people with a diagnosis of dementia living at home or staying in short-term care or acute care settings; studies that did not mention dementia in the population
- Intervention: nondigitalized intervention studies; eHealth interventions that did not focus on aiding comprehensive assessment or supporting clinical decision-making; interventions that monitored clinical signs only, such as a motion sensor, or recorded biodata, for example, blood pressure remote monitoring, were out of scope
- Outcome: not applicable
- Comparator: not applicable
- Study design: opinion pieces
Study Selection Procedure
Studies identified from the search were exported to Endnote . Duplicates were identified and removed. All titles and abstracts were screened by 1 researcher (JG). Approximately 20% were randomly selected for blinded double screening by 2 independent researchers (TA and EY) as a calibration process to test the application of the eligibility criteria. Once an agreement of 90% was confirmed between the reviewers, the eligibility criteria were applied to all identified studies. This was undertaken to screen for eligibility at the title and abstract screening and again at the full paper review. Discrepancies were resolved through discussion.
The quality of publications was appraised using the Critical Appraisal Skills Programme (CASP) tool , which was appropriate for the study design. It was conducted by 1 author (JG) and reviewed independently by 2 authors (CE and ND). Where the design was not amenable to the CASP, alternative tools were used, including the Mixed Methods Appraisal Tool (MMAT) [ ] and the Joanna Briggs Institute (JBI) critical appraisal tools [ ]. No studies were excluded only on the basis of their appraised quality; rather, it was conducted to help understand and describe the studies.
A standardized data extraction tool was developed in Microsoft Excel and informed by the review questions. Extracted data included study aim, country of origin, design, population of interest, setting, eHealth intervention (type, components, and summary), methods of data collection and analysis, outcomes regarding intervention implementation and conclusions, implications, and limitations. Implementation data were extracted from both the results and discussion sections to capture relevant findings relating to the authors’ observations of why an intervention was or was not effective.
Data Analysis and Synthesis
A deductive thematic analytic approach was undertaken, underpinned by the adapted version of the Consolidated Framework for Implementation Research (CFIR) . The original CFIR [ ] comprises 5 broad theory-based constructs and 39 subconstructs within these: intervention characteristics, inner setting, outer setting, individual characteristics, and implementation process. It is a comprehensive and practical guide for assessing the potential factors that influence implementation. A recent adaptation of the framework added a sixth construct [ ], patient needs, acknowledging the importance of person-centered care in health care interventions and the paucity of attention it frequently receives in implementation frameworks. Given the nature of this review, the adapted CFIR was used.
Preliminary synthesis involved tabulation to organize the findings and compare data across different studies. The data from each study were then mapped onto the framework. Common themes that arose across the studies in line with the subconstructs of the framework were then synthesized to form a narrative regarding facilitating elements of and inhibiting barriers to successful implementation. Where data did not align to a subconstruct, an inductive thematic analytic approach was undertaken to avoid biasing data and identify gaps in the framework when applying it to this context. The themes were then developed to identify additional constructs to adapt the framework to this specific context.
A total of 1055 papers were screened by title and abstract, of which 128 (12.13%) full-text articles were assessed; of the 128 articles, 29 (22.7%) met the eligibility criteria for the review ([ ]). The included studies reported 27 unique interventions that aimed to facilitate comprehensive assessment of care home residents or improve decision-making surrounding care and treatment, published between 2000 and 2020. The sample sizes ranged from 5 to 4171 for residents and 6 to 609 for carers. Approximately 31% (9/29) of studies omitted the number of participants.
Of the 29 articles, the included studies comprised 7 (24%) randomized controlled trials, 7 (24%) quasi-experimental studies [- ], 6 (21%) qualitative studies [ - ], 3 (10%) descriptive studies [ - ], 2 (7%) mixed methods studies [ , ], 2 (7%) cohort studies [ , ], and 2 (7%) cross-sectional studies [ , ].
All studies included adults with dementia, focusing specifically on requirements for people with dementia (11/29, 38%) [, , - , , , , , , ] or within a mixed population (18/29, 62%) [ - , , , - , , , , - ]. The average percentage of residents with dementia was 52.8% in the mixed population studies that reported the proportion (6/29, 21%) [ , , , , , ]. Approximately 41% (12/29) of studies did not delineate the proportion of residents with dementia [ , , , , , , , , , - ] (see for a summary of study characteristics).
|Characteristics, study, and country of origin||Study design||Population (n)||Age (years), mean||Setting (n)||Type of eHealth intervention|
|Population with dementia and specific requirements|
|Catic et al , United Statesa||Cohort||Dementia (46)||82.5||Nursing home (11)||Video consultation|
|Gordon et al , United Statesa||Matched-cohort study||Dementia (115)||Not specified||Nursing home (11)||Video consultation|
|Klein et al , Australia||Qualitative||Dementia (5)||59-88||Regional aged care facility (1)||PDA|
|Lee et al , Korea||Quasi-experimental||Dementia (53)||Not specified||Nursing home (1)||Video consultations and computerized system|
|Lyketsos et al , United States||Quasi-experimental||Dementia (not specified)||Not specified||Long-term dementia facility (1)||Video consultations|
|Mitchell et al , United States||Cluster RCTb||Dementia (402)||86.7||Nursing home (64)||Video decision support tool|
|Qadri et al , United States||Mixed methods||Dementia (not specified)||Not specified||Nursing home (3)||PDA|
|Moniz-Cook et al , United Kingdom||Cluster RCT||Dementia (832)||Not specified||Care home (58)||Computerized decision support tool|
|Piau et al , France||Qualitative||Dementia (90)||Not specified||Long-term care facility (10)||Video consultations|
|Shiells et al , Czechia||Qualitative||Dementia (not specified)||Not specified||Nursing home (3)||Electronic patient records|
|Keenan et al , United Kingdom||Qualitative||Dementia (not specified)||Not specified||Care home (27)||Computerized decision support tool|
|Population with mixed requirements (dementia and nondementia)|
|Salles et al , France||Descriptive||Mixed (304); others: wounds and psychiatric||85.6||Nursing home (1)||Video consultations|
|Daly et al , United States||RCT||Mixed (22)||86||Long-term care facility (1)||Electronic patient records|
|De Luca et al , Italy||RCT||Mixed (59)||79.1||Nursing home (1)||Video consultations and tele-counseling|
|Johnston and Jones , United States||Descriptive||Mixed; dementia (52.5%); others: delirium, depression, and dysthymia||79.3||Rural skilled nursing facility (1)||Video consultations|
|Krüger et al , Norway||Quasi-experimental||Mixed; dementia (76.6%) and stroke (23.4%)||84.4||Nursing home (7)||Electronic patient records with decision support tool|
|Mitchell et al , United States||Cluster RCT||Mixed; dementia (70%)||Not specified||Nursing home (intervention=119; control=241)||Video decision support tool|
|Perri et al , Canada||Quasi-experimental||Mixed; dementia (69%); other: cardiovascular, respiratory, frailty, and psychiatric.||87||Long-term care facility (1)||Video consultations|
|Alexander , United Statesc||Descriptive||Mixed; dementia (20), Alzheimer (13), and other: osteoarthritis, pneumonia, and cerebrovascular accident||Not specified||Nursing home (3)||PDA, electronic patient records, and decision support tool|
|Mor et al , United States||RCT||Mixed (not specified); dementia (30%); others: cardiopulmonary||Not specified||Nursing home (360)||Video decision support tool|
|Vuorinen , New Zealand||Qualitative||Mixed (not specified)||≥65 (intervention)||Long-term care facility dementia unit (1)||Web-based assessment tool|
|Weiner et al , United States||RCT||Mixed (369)||64||Nursing home (1)||Video consultations|
|Pillemer et al , United States||Quasi-experimental||Mixed (761)||79.4||Nursing home (10)||Electronic patient records and PDAs|
|O’Mahony et al , United States||Quasi-experimental||Mixed (not specified); dementia, cancer, chronic obstructive pulmonary disease, liver disease, and renal failure||Not specified||Skilled nursing facilities (2)||Video consultations|
|Munyisia et al , Australia||Mixed methods||Mixed (not specified)||Not specified||Nursing home (1) and specialized home (1)||Electronic patient records|
|Alexander et al , United Statesc||Qualitative||Mixed (not specified)||Not specified||Nursing home (4)||PDA, electronic patient records, and decision support tool|
|Bjarnadottir et al , United States||Cross-sectional analysis||Mixed (not specified)||Not specified||Nursing home (927)||Electronic patient records|
|Wakefield et al , United States||Cross-sectional analysis/longitudinal||Mixed (62); dementia, seizure, Parkinson, and urinary tract infections||72||Long-term care facility (1)||Video consultations|
|Fossum et al , Sweden||Quasi-experimental||Mixed (491)||84.5||Nursing home (15)||Electronic patient records with decision support tool|
aArticles from the same study.
bRCT: randomized controlled trial.
cArticles from the same study.
The included studies varied in their quality. In general, the CASP criteria for the experimental studies identified consistent reporting of clear and focused aims, with appropriate methodologies to address the research questions. Weaknesses were related to small sample size, evidence of selection and attrition bias, poor description of analysis, and nonblinding. Descriptive studies were generally well-reported (average 86% of MMAT criteria met) and quasi-experimental studies (average 77.7% of JBI criteria met). The weaker study design used mixed methods (average 50% of MMAT criteria met) and cross-sectional studies (average 56.2% of JBI criteria met). Weaknesses pertained to the management of confounding factors and the integration of qualitative and quantitative data.[ - ], details the quality appraisal results for each study using the respective appraisal checklist.
CFIR Constructs Associated With eHealth Implementation
details the respective CFIR constructs and subconstructs that were identified as important determinants for implementation. The number of subconstructs identified per study ranged from 1 to 13, with a median of 6. No major differences in implementation requirements were identified between studies with a specific focus on requirements for people with dementia and studies reporting on dementia within a mixed population ( ). Findings for each CFIR construct and respective subconstructs is presented in turn. Constructs that were identified in ≤2 studies are not presented as a narrative because of insufficient data.
|CFIRa construct and subconstructs||Definition in the context of care homes and integrated care for people with dementia||Total, n (%)|
|Intervention characteristics: aspects of eHealth that might affect implementation success|
|Intervention source||How end users perceive the legitimacy of the eHealth source—whether it has been developed internally as a response to a problem in the care home or externally||0|
|Evidence strength and quality||Stakeholder perception of the strength of the evidence supporting the belief that eHealth will produce the desired outcomes from sources such as published literature||0|
|Relative advantage [- , , , - ]||Whether stakeholders perceive eHealth as advantageous over current practice||9 (31)|
|Adaptability [, , , , , , ]||How interoperable eHealth is with current care home information technology systems||7 (24)|
|Trialability||Whether eHealth can be tested initially on a small scale, such as piloted in a small number of care homes||0|
|Complexity [- , , , , , , , , , , ]||How simple and user-friendly end users perceive eHealth to be within routine care||13 (45)|
|Design quality and packaging||Stakeholder perception of the physical presentation of the eHealth intervention||0|
|Cost [, , , - , , ]||Cost associated with implementing eHealth||8 (28)|
|Patient needs: the extent to which resident needs are known and prioritized by the care home|
|Clinical benefitb [, , , , , , , , , , ]||How clinically beneficial eHealth is perceived to be for the resident||11 (38)|
|Person-Centered careb [, , , ]||Whether eHealth can be tailored to the individual needs of the resident and care home||4 (14)|
|Resident experienceb [, , , , , ]||The effect that eHealth has on resident needs and satisfaction with care||6 (21)|
|Outer setting: external influences on eHealth implementation|
|Cosmopolitanism [, ]||The degree to which the care home is networked with others||2 (7)|
|Peer pressure||Pressure experienced by the care home to implement eHealth||0|
|External policy and incentives [, , - ]||External influences of implementation of eHealth for the care home||5 (17)|
|Inner setting: characteristics of the implementing care home|
|Structural characteristics [, , , ]||The social architecture, age, maturity, and size of the care home||4 (14)|
|Networks and communications ||The nature and quality of social networks and communication within a care home||1 (3)|
|Culture||Norms and values of the care home||0|
|Implementation climate||The capacity for change and shared receptivity of individuals to eHealth and the extent to which it will be supported within the care home|
|Tension for change ||The extent to which stakeholders perceive current practices as needing change||1 (3)|
|Compatibility [- , , , , , ]||The degree of fit between the care home and eHealth in terms of values and existing workflows||8 (28)|
|Relative priority||Individuals’ shared perception of the importance of implementation within the care home||0|
|Organizational incentives and rewards||Incentives to increase participation with eHealth such as awards and promotions for staff||0|
|Goals and feedback||The degree to which goals of eHealth are acted upon and feedback to staff||0|
|Learning climate [, , , , , , , , , ]||A climate in which staff feel valued in the implementation process and comfortable to participate through encouragement by care home leaders||10 (34)|
|Readiness for implementation||Indicators of care home commitment to eHealth implementation|
|Leadership engagement [, , , , ]||Commitment and involvement of care home managers and leaders in implementation||5 (17)|
|Available resources [, , , , , , , , , , - , ]||The level of care home resources dedicated to eHealth implementation, including money and staff time||14 (48)|
|Access to knowledge and information [- , , , , - , , , , , , ]||Access to sufficient eHealth training for end users||17 (59)|
|Individual characteristics: end-user individual beliefs, knowledge, and attitudes toward eHealth and implementation|
|Knowledge and beliefs about the intervention [, , , , , - , - , , , , ]||End users’ attitudes toward eHealth and its impact||16 (55)|
|Self-efficacy [, , , , , , ]||End users’ belief in their own abilities to use the eHealth intervention||7 (24)|
|Individual stage of change||The phase an individual is in as they progress toward sustained use of eHealth||0|
|Individual identification with organization||End user’s perception of their relationship with the care home||0|
|Other personal attributes [, , , , , , ]||Individuals’ attributes that affect implementation such as staff willingness, experience, age, or grade||7 (24)|
|Process: stages of the implementation process that can impact its success|
|Planning [, , - , , , , , - ]||The degree to which tasks required for implementation of eHealth are agreed in advance||13 (45)|
|Engaging||Attracting and involving stakeholders in eHealth implementation|
|Champions [, , , , , , ]||Individuals who are dedicated to driving the implementation of eHealth and overcoming resistance in the care home||7 (24)|
|End usersb [, , , , , - , , ]||Other stakeholders, including end users and staff, within the care home||10 (34)|
|Opinion leaders||Individuals in a care home who have a formal or informal influence on others’ attitudes toward implementation||0|
|Formally appointed internal leaders||Individuals from within the care home who are formally appointed to implement eHealth||0|
|External change agents||Individuals from outside the care home who formally influence implementation of eHealth||0|
|Executing [, , , , ]||The extent to which eHealth implementation is conducted as planned||5 (17)|
|Reflecting and evaluating [, , , , , , ]||Monitoring of eHealth implementation and feedback about its progress||7 (24)|
aCFIR: Consolidated Framework for Implementation Research.
bAdditional subconstructs identified inductively from the data.
This refers to aspects of the eHealth intervention that might affect implementation success in care homes and includes findings relating to intervention complexity, adaptability, and cost.
eHealth that is perceived as advantageous to an alternative system by improving access to emergency care , increasing efficiency [ , , , ], and reducing paperwork [ ] is more likely to be adopted [ ]. Barriers to uptake include a preference for face-to-face consultations [ ] and increased time required to organize eHealth consultations [ ].
eHealth is at an advantage if it aligns with data and technology already in use [, , , ]. Concerns on patient privacy and electronic transfer of confidential information act as institutional firewalls [ , ] but can be overcome by encrypting data and assigning residents confidential numbers [ ]. A way to increase the adaptability of a device is through the provision of customizable tools such as drop-down menus [ ]. Incorporating a decision support system in eHealth interventions is advocated to respond to changes in individual residents’ needs by providing alerts and directing staff to appropriate care [ , , ].
eHealth is more likely to be implemented if it is straightforward and user-friendly [- , , , , , , , , , , ]. Simple devices are regarded as more reliable [ , , ], and it is recommended that more advanced technology be used only where necessary [ ]. Dual systems of paper-based and electronic devices should be avoided to minimize inconsistency [ ] and the complexity of data recording [ ]. Uptake is also influenced by the ease of access to eHealth [ , , , , , ], with portable tools improving ease of access [ , ] and thereby saving staff time [ , , , ]. However, some staff report that handheld devices are easy to break and misplace [ ] and prefer a desktop computer [ ]. Technological difficulties, including software and memory issues [ ], inability of patients and physicians to see or hear each other [ , ], and difficulty in obtaining technical support despite frequently requiring it [ , ], can impede eHealth implementation [ , , , , , , ]. Providing training and specialist technical support for staff are key to overcoming these barriers [ - , , , , ].
Cost is a major barrier to implementing electronic health records in care homes ; for an intervention to be implemented, the benefits must be perceived to outweigh the costs [ , ]. eHealth tools that incur no additional financial cost beyond staff time [ , ] and installation [ ] can integrate with preassembled data [ ], are cheaper to purchase than full-size computers [ ], and are more likely to be adopted. Uptake is optimized by external funding [ , , ] and intervention outcomes that minimize spending such as reduced resident transition between care settings, for example, unplanned hospitalization [ , , ], or reduced antipsychotic prescriptions [ ].
This refers to the extent to which resident needs are known, prioritized, and pursued by a care home. A total of 3 novel subconstructs were identified inductively.
An important contributor toward successful implementation is the perceived clinical benefit for residents [, , , , , , , ]. eHealth interventions are reported to help staff focus on the resident’s condition [ ], better manage residents’ symptoms and vital signs [ , , ], improve safety around medication administration [ ], enable staff to attend to residents in a timely manner, and identify common behavior patterns [ ]. The positive impact of eHealth interventions to minimize burdensome care transitions has also been recognized. Reduced unplanned hospital transitions are suggested to improve residents’ quality of life [ ] by preventing disruption in care continuity with attendance to care needs in the care home [ ]. Residents appreciate minimal journeys to acute care settings [ ] and the benefit of emergency telemedicine sessions to deliver timely skilled health care interventions remotely [ ]. Barriers to uptake of eHealth interventions pertained to opinions that the intervention little benefited residents [ ] by inadequately monitoring and identifying resident change or deterioration and failing to improve assessment processes [ ].
Interventions must be tailored to the individual and accommodate changing resident needs if they are to be successfully implemented [, , ]. Given the heterogeneous population in care homes and variable incidence of dementia, eHealth interventions will not make a positive difference if they do not have the capacity to assist with dementia-specific needs when required [ , , ]. Extra consideration must go into ensuring that technology is unobtrusive if used in the presence of this patient group [ , ] who may lack the capacity to understand the change in care. Where residents experience cognitive, visual, or hearing difficulties, eHealth must be tailored, such as through the provision of a larger monitor [ ].
A negative resident experience of eHealth obstructs implementation [, , ]. Dissatisfaction stems from devices interfering with the time spent with staff [ ] and a switch from face-to-face to remote communication [ ]. Staff report concerns that using eHealth in the presence of residents may be intrusive and dehumanizing [ , ]. Concerns tend to diminish with continued use of eHealth tools; however, over time, staff acknowledge that eHealth can improve care quality [ , ]. Other residents report no unintentional harm or negative effects on communication [ ], with 1 study attributing positive findings to residents, reporting that they felt more followed and cared for [ ].
The outer setting is concerned with external influences on intervention implementation. This was least considered construct across the studies, with the main focus on the ‘External policy and barriers’ subconstruct. External barriers include policies on the medical liability of telemedicine , licensing requirements that do not allow physicians to consult across different parts of the country [ ], and issues around reimbursement policies [ - ]. External financial support acts as an incentive to circumvent the additional cost barriers to implementation [ , , ].
The most commonly considered construct across the studies was the inner setting, referring to the internal characteristics of a care home. These focused on the 2 main subconstructs of implementation climate (compatibility and learning climate) and readiness for implementation (leadership, available resources, and access to knowledge).
The structural characteristics of the care home setting that affect implementation success include the complex patient population, with the heterogeneity of residents’ conditions affecting the compatibility between the intervention and the care home [, ]. Care home size can also affect the uptake of an eHealth intervention, with larger homes facilitating uptake through more comprehensive provision of information technology services or inhibiting uptake with larger home leaders exhibiting more resistance to adoption and delivering training [ ].
Portable eHealth devices that can be used at the point of care [, , ] and during nighttime hours [ ] prevent disruption to workflow, thereby facilitating uptake [ , , , , , , ]. Interventions are at an advantage if their goals are aligned with those of the care home, for example, improving advance care planning [ ], and they may face resistance if they do not support existing practice [ ]. Providing individually tailored implementation protocols [ ] and delivering the intervention when care homes are maximally staffed can ease adoption [ , ].
Creating a climate within the care home that encourages learning and active participation in the intervention is key [, , , , , , ]. Staff members in the frontline must be receptive [ ] and participate if they are to influence patient outcomes [ , ] and affect care delivery [ ]. Changes to personnel at the site [ ] and reluctance from staff [ , , , ] undermine a conducive learning climate. Hierarchical staffing can inhibit implementation [ , ] if junior staff feel they are unable to speak up [ ] or access the new tools [ , ]. If a learning climate is not fostered, the staff lack practice in delivering an intervention [ ] and opportunities to share and embed their learning to change practice [ ].
Readiness for Implementation
Organizational commitment from the top down is crucial [, , , ]. Staff leaders and managers who collaborate with the research team [ ] and lead by example through attending training and contributing to data collection [ ] foster an engaged and cohesive workforce [ ]. A lack of enthusiasm from management can lead to delays in implementation and reluctance from other staff [ ]. Unwillingness and resistance from general practitioners to change practice also act as a barrier [ , ] and contribute to a fragmented learning climate [ ].
Providing extra time for implementation planning [, , , , ] and end-user training [ , , , , ] is required to accommodate a change in practice. A collective staff opinion of insufficient time to adjust to change hinders implementation [ , , , , , , ]. Sufficient bandwidth is required to allow eHealth to function properly [ , , , ], and insufficient bandwidth can lead to significant motion artefact [ ] and jerky motion images [ ]. Additional space may be required for training, either within the home [ ] or off site [ ]. Equipment needs may include extra computers [ ] and software [ ], incurring further financial costs [ , ]. Reduced staffing [ ], high staff turnover [ , ], and staff absence [ ] obstruct the implementation of eHealth interventions. Limited resources can act as a barrier unless benefits can be demonstrated to outweigh costs [ , ]. Managers need to consider the additional resources required to accommodate adoption [ ].
Access to Knowledge and Information
Providing adequate training to end users is crucial for promoting uptake [- , , , , - , , , , , ]. A variety of educational methods are used, including interactive learning strategies [ ], lectures, exercises, and group discussions [ ]. Training is often provided through a cascaded learning train-the-trainer approach [ , , ]. Preference varies as to receiving training on the job [ , , ] or continuously over a designated period in the months leading up to implementation [ , , ]. Training tailored to individual needs [ , , ] can help facilitate uptake, whereas inadequate training can negatively influence perceptions of intervention benefits [ , ] and how staff disseminate knowledge to colleagues [ ]. Incorporating a period of joint work and supervision from a qualified external expert can further facilitate the benefits of training [ ].
This pertains to end users’ individual beliefs, knowledge, and attitudes toward eHealth and implementation. The findings focused on 3 main subconstructs.
Knowledge and Beliefs About the Intervention
Fostering a positive end-user attitude toward eHealth is key to ensuring willing adoption. eHealth tools that are perceived as high quality [, , ] and efficient [ , , , ], by cutting back on paperwork [ ] and providing instant access to data [ ], are more likely to be adopted. Interventions judged to improve staff understanding and knowledge [ , , , , ] and benefit residents [ , , , ] are more likely to be used. eHealth devices that improve overall job satisfaction [ , , , ] while providing additional benefits, such as reducing staff anxiety [ ], and social features that enrich staff lives outside of work [ ] encourage use. Conversely, negative attitudes toward eHealth act as barriers to implementation. These include concerns regarding a lack of care improvement [ , , , ], a preference to deliver care in person [ , , , ], and concerns of inequity in health care provision, resulting in difficulty obtaining family consent [ ]. Other barriers include a lack of positive impact on staff workload [ , , ], concerns over increased time and effort because of implementation [ , , , , ], and uncertainty about the purpose of eHealth and how it works [ , ]. This indicates the need for comprehensive training to highlight the benefits and importance of implementing the intervention [ ].
Self-efficacy refers to an individual’s belief in their own capabilities to implement an intervention. Low self-confidence in ability [, , ] and apprehension toward using new technology [ , ] can act as barriers to implementation. These can be overcome by providing sufficient training, which is tailored to individual needs [ , , ], and on-hand technical support [ , ].
Other Personal Attributes
Other issues that affect implementation relate to staff willingness and commitment to ensuring that the intervention is executed as planned [, , , ]. A lack of experience can also act as a barrier if senior members of staff consider more junior members to be less capable of participating [ , , , ] because of age, grade, educational background, or experience [ , ]. However, demonstrating that training designed for medical caregivers is effective across disciplines and grades should help dispel these notions [ ].
This concerned reporting on the stages of the implementation process that affect its success. A novel subconstruct was inductively identified to capture data related to engaging end users and other stakeholders.
Insufficient planning and preparation for change can result in overwhelmed staff perceiving eHealth to be incompatible with their care home , technology systems and facilities being inundated [ ], and essential care being omitted [ ]. Poorly planned study timelines can lead to a low number of participants if enough time is not allocated to recruitment [ ] and to implementation impact being undetected if insufficient time is allocated to following up and monitoring outcomes [ , , ]. Time must also be allocated to prospectively explore different types of eHealth to ensure suitability [ ]. Multiple strategies are required to initiate a change in behavior and routine practice [ , ]. Developing contingency plans to support the program [ , ] and establishing a team whose primary goal is to assist implementation can help facilitate uptake [ ]. Staff members must be fully informed of the aim of the intervention [ ] and have full clarity of their designated role to achieve maximum impact [ ].
Engaging individuals is key to successful implementation. This is often done through designating champions—individuals from within an organization responsible for driving and supporting implementation [, , , , , , ]. To be successful, there must be a sufficient number of assigned champions in each home [ ]. They must be committed to the position [ ] and not feel undermined by others in leadership roles [ ]. Designating champions is often undertaken by the team manager [ ] to ensure they are appropriately able to support other staff members [ , ].
Engaging: End Users
Engaging champions alone is insufficient; other staff members must be involved for successful implementation [, , , , - , , ]. For a change in the quality of care to be observed, staff engagement must be sustained [ ], with all disciplines, including frontline staff [ , ] and corporate leaders, committed to implementation [ ]. Involving end users in the design and development of technology [ , , ] and considering individual requirements is key if an intervention is to be embedded in a new context.
Low intervention fidelity is a challenge in complex care home settings , and residents often do not receive the intervention as planned [ ]. Flexible protocols tailored to the care home environment should be developed [ , ]. Barriers to successful execution include lengthy gaps between initial contact with participants and data collection and intervention activity, which can undermine the coherence of the intervention [ ].
Reflecting and Evaluating
Ongoing evaluation of progress is vital to ensure a new intervention is effectively embedded [, , , , , ]. This can be done using routine data collected through the eHealth tool [ ], through in-person visits to the home, monthly conference calls, or video status reports [ , ] to determine the effect and use of the intervention [ ]. Receiving feedback can also motivate care home participation, with an absence of evaluative feedback leading to a feeling of being short-changed [ ].
This review investigated the key facilitators and barriers that influence the implementation of eHealth interventions to support comprehensive assessment and decision-making for people with dementia in care homes. Our findings inform a model of eHealth implementation in long-term care for people with dementia (). We identified 4 novel subconstructs (denoted in the figure by an asterisk) required to adapt the CFIR to the care home context and use of eHealth to enhance integrated care for people with dementia. This modification enables the framework to accommodate the context-specific findings identified in this review and enhances its use when applied to implement eHealth in the care home context.
The 3 novel subconstructs—resident experience, clinical benefit, and person-centered care—were identified within the patient needs construct. No subconstructs have previously been delineated here, and the framework was previously not nuanced enough to capture the data and implementation requirements for this population. Clinical benefit was the most commonly identified theme, with the capacity to minimize burdensome transition between care settings as a key facilitator of implementation. This is echoed in the literature that highlights the adverse impact of burdensome care transitions, both on the resident  and on costs [ ]. Although outcomes were not the focus of this review, eHealth interventions are more likely to be embedded if they can improve the delivery of integrated health care where it is required, enhance integrated care, and improve cost-effectiveness in the National Health Service [ ].
Findings around the potential dehumanization of care reiterate a concern raised previously in relation to eHealth implementation . Although the aim of eHealth is to enhance care delivery, entirely omitting in-person contact has clear disadvantages. Striking the right balance between delivering care face to face and virtually is crucial. Future research should focus on delineating the components of health care that must be delivered in person and identifying those that are more amenable to remote delivery to ensure that care quality is not compromised.
The engaging subconstruct of the implementation process was also modified for the purpose of this review. None of the proposed subcategories of engaging (opinion leaders, formally appointed internal implementation leaders, champions, and external change leaders) were suitable for capturing data around engaging end users. The importance of involving end users throughout the implementation process is a key finding and is consistently advocated in the literature to help close the gap between research production and clinical practice [, ]. Therefore, a novel subcategory titled end users was added to the CFIR. Contradictory findings regarding staff and resident preferences on the design and use of eHealth devices are an important indication that there is no one-size-fits-all approach to eHealth implementation. This inconsistency in preferences highlights the critical need to involve end users during intervention development to accommodate a range of requirements and enhance the likelihood of sustained implementation. Progressing the CFIR to include a subcategory suitable for data pertaining to all stakeholders’ input increases its relevance and applicability for this context.
The most salient construct identified in this review was inner setting of the implementing organization. This is concurrent with the existing research on care homes. A systematic review by Goodman et al  identified many of the same factors that influence the readiness of care homes to participate in change. These include ensuring compatibility between the intervention and existing care home routine, providing sufficient training and resources, and engaging care home leaders. Both reviews highlight the significance of the organizational context on implementation success and the need to consider context in the planning stage to inform study design rather than retrospectively. This is contrary to a review of determinants for eHealth implementation with informal caregivers in the community [ ], where few factors relating to context were identified as important. This indicates that factors influencing implementation may not be consistent across health care settings and stresses the importance of understanding specific contextual factors and tailoring implementation strategies accordingly.
One of the most prominent determinants of implementation identified in this review was the complexity of the intervention, consistent with previous findings highlighting ease of use as the most important facilitator of eHealth adoption. The nonadoption, abandonment, scale-up, spread, and sustainability framework  was developed specifically in response to the finding that eHealth, which is categorized as complex is rarely, if ever, successfully embedded in mainstream care. It aims to help innovators and implementers measure and minimize complexity in eHealth and scale-up and sustain innovation, and therefore, it could helpfully be used in this context.
Implications for Policy and Clinical Practice
Using the most salient subconstructs of the framework, a conceptual model was developed to highlight the most important factors that influence the implementation of eHealth interventions to enhance integrated care focusing on care processes of comprehensive assessment and decision-making about care and treatment (). The findings can be translated into practical recommendations for organizations aiming to embed eHealth within long-term care settings for people with dementia ( ). The model indicates that for implementation to be successful, eHealth devices must be low cost, simple to use, and tailored to the care home setting and residents. It must be clinically beneficial to the residents, with special consideration of changing, multimorbid dementia-specific needs. Extensive planning and engagement of care home leaders, end users, and champions in the development and implementation process are key to ensuring successful execution. Providing sufficient training and resources to ensure that care home staff feel valued, motivated, and optimistic about a change in practice is crucial to fostering a positive implementation climate.
Practical recommendation for implementation of eHealth in long-term care for residents with dementia.
Consolidated Framework for Implementation Research constructs and practical recommendations for eHealth implementation
- eHealth should be tailored to the individual resident and accommodate changing and complex needs.
- It should be unobtrusive and not replace in-person contact or compromise care quality.
- eHealth must not just streamline workload but clinically benefit the resident and improve outcomes.
- eHealth should be user-friendly and accessible to increase sustainability.
- eHealth that is interoperable with current systems is advisable to minimize installation cost and complex new learning for end users.
- Technical support should be easily attainable and readily available.
- Policies that endorse eHealth use in care homes ease implementation, for example, rapid policy-driven implementation of remote consultations during the COVID-19 pandemic.
- eHealth tools should be tailored to care home settings to fit with existing workflow and care home values.
- Engaging care home leaders in the intervention is key to promoting enthusiasm and a cohesive working environment.
- Staff participation and learning about eHealth should be encouraged.
- Sufficient training should be provided for end users, which should be tailored to individual requirements.
- Additional resources need to be allocated to accommodate a change in practice. These include technological requirements such as bandwidth and equipment and extra staff time.
- Fostering a positive staff attitude toward eHealth is essential for uptake.
- The tool should benefit staff by easing workload, increasing knowledge, and improving job satisfaction.
- Preparation for change and consideration of implementation timelines, strategies, and contingency plans in advance is crucial.
- Champions should be designated in each care home to drive and support implementation.
- End users and other stakeholders should be engaged from an early point and consulted in the implementation process to accommodate individual requirements.
- Ongoing evaluation and reflection on uptake and adherence to eHealth should occur to inform any necessary developments and improvements.
The CFIR is a comprehensive determinant framework and is chosen to guide analysis owing to its previous application with eHealth interventions [, , ]. Generally, the extracted data were amenable to the CFIR, with no data left uncoded. However, 33% (13/39) of the subconstructs had no associated data ( ), concurrent with findings from a previous review of eHealth implementation across health care settings [ ]. This consistency either suggests that some subconstructs are not relevant to implementing eHealth or highlights a limitation of the evidence base and lack of existing literature for this setting and population. These areas of uncertainty are important for informing future research, which should focus on identifying barriers to and facilitators of the implementation of these underresearched constructs.
Strengths and Limitations
A systematic approach to this review allowed for the rigorous and thorough identification of the relevant literature. Evidence synthesis was theory driven and guided by the CFIR, which has been built upon here to increase its relevance in eHealth and long-term care contexts.
Although this review specifies care home residents with dementia as its population of interest, only 38% (11/29) of the included studies had an exclusive dementia population. This was to reflect the real-world heterogeneous populations in care homes and include interventions that were suitable at an organizational level rather than the individual level. Although there was no real difference in factors that influence implementation between dementia-specific and mixed populations, caution must be exercised when extrapolating these findings to homogeneous dementia populations.
Gray literature was not included in this review. This was because, when identified, it provided little data on the factors that influence implementation. This could have excluded relevant data on the eHealth interventions used in care homes. A recent scoping review on telehealth during the COVID-19 pandemic reported a rapid rise in eHealth during the pandemic .
To our knowledge, this is the first review to synthesize evidence on the implementation of eHealth interventions focusing specifically on improving assessment and decision-making in care homes for people with dementia. We adapted the CFIR and progressed its applicability for use in this context. We developed a conceptual model to demonstrate the most important factors to consider when designing and implementing an eHealth intervention and translated it into 18 practical recommendations for implementers, innovators, and organizations to implement eHealth for people with dementia in long-term care. Particular focus should be placed on the individual care home setting and on the consideration of resident and end-user needs when developing an implementation strategy for use in this context.
The authors would like to thank Adam Garside, an information support specialist at King’s College London, who helped with the development of the search strategy. This project was funded by the Economic and Social Research Council (ESRC), National Institute for Health Research (NIHR), and ESRC/NIHR Dementia Initiative 2018 (grant reference number ES/S010327/1). CE is funded by a Health Education England/NIHR Senior Clinical Lectureship (ICA-SCL-2015-01-001). ND was supported by a fellowship from the Alzheimer’s Society, United Kingdom (grant number AS-JF-16b-012). The views expressed are those of the authors and not necessarily those of the ESRC, NIHR, or the Department of Health and Social Care.
JG, CE, ND, and JEA designed the study. JG, EY, and JA contributed to data screening, extraction, and analysis. JG wrote the manuscript. The authors contributed to revisions and read and approved the final manuscript.
Conflicts of Interest
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.PDF File (Adobe PDF File), 96 KB
Search strategy.DOCX File , 18 KB
Quality appraisal summary table.DOCX File , 16 KB
Differences in implementation requirements.DOCX File , 18 KB
- Deaths registered in England and Wales: 2019. Office of National Statistics. 2020. URL: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsregistrationsummarytables/2019 [accessed 2021-12-10]
- Sleeman KE, de Brito M, Etkind S, Nkhoma K, Guo P, Higginson IJ, et al. The escalating global burden of serious health-related suffering: projections to 2060 by world regions, age groups, and health conditions. Lancet Glob Health 2019 Jul;7(7):e883-e892 [FREE Full text] [CrossRef] [Medline]
- Sampson EL, Burns A, Richards M. Improving end-of-life care for people with dementia. Br J Psychiatry 2011 Nov;199(5):357-359. [CrossRef] [Medline]
- Tonelli M, Wiebe N, Straus S, Fortin M, Guthrie B, James MT, Alberta Kidney Disease Network. Multimorbidity, dementia and health care in older people: a population-based cohort study. CMAJ Open 2017 Aug 14;5(3):E623-E631 [FREE Full text] [CrossRef] [Medline]
- Leniz J, Higginson I, Stewart R, Sleeman K. Understanding which people with dementia are at risk of inappropriate care and avoidable transitions to hospital near the end-of-life: a retrospective cohort study. Age Ageing 2019 Sep 01;48(5):672-679. [CrossRef] [Medline]
- Sampson EL, Candy B, Davis S, Gola AB, Harrington J, King M, et al. Living and dying with advanced dementia: a prospective cohort study of symptoms, service use and care at the end of life. Palliat Med 2018 Mar;32(3):668-681 [FREE Full text] [CrossRef] [Medline]
- Hendriks SA, Smalbrugge M, Hertogh CM, van der Steen JT. Dying with dementia: symptoms, treatment, and quality of life in the last week of life. J Pain Symptom Manage 2014 Apr;47(4):710-720 [FREE Full text] [CrossRef] [Medline]
- Data Analysis Report Dying with Dementia. Public Health England. 2016. URL: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/733065/Dying_with_dementia_data_analysis_report.pdf [accessed 2021-12-11]
- End of life care in frailty: care homes. British Geriatrics Society. 2020. URL: https://www.bgs.org.uk/resources/end-of-life-care-in-frailty-care-homes [accessed 2021-12-11]
- Understaffed and overworked: poor working conditions and quality of care in residential care facilities for the elderly. Publications. 2017. URL: https://digitalcommons.law.ggu.edu/pubs/788/ [accessed 2021-12-11]
- Briggs AM, Valentijn PP, Thiyagarajan JA, Araujo de Carvalho I. Elements of integrated care approaches for older people: a review of reviews. BMJ Open 2018 Apr 07;8(4):e021194 [FREE Full text] [CrossRef] [Medline]
- Ellis G, Whitehead MA, O'Neill D, Langhorne P, Robinson D. Comprehensive geriatric assessment for older adults admitted to hospital. Cochrane Database Syst Rev 2011 Jul 06(7):CD006211 [FREE Full text] [CrossRef] [Medline]
- Disalvo D, Luckett T, Bennett A, Davidson PM, Agar M. Multidisciplinary perspectives on medication-related decision-making for people with advanced dementia living in long-term care: a critical incident analysis. Eur J Clin Pharmacol 2020 Apr;76(4):567-578. [CrossRef] [Medline]
- Coombs MA, Parker R, de Vries K. Managing risk during care transitions when approaching end of life: a qualitative study of patients' and health care professionals' decision making. Palliat Med 2017 Jul;31(7):617-624 [FREE Full text] [CrossRef] [Medline]
- Monaghesh E, Hajizadeh A. The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence. BMC Public Health 2020 Aug 01;20(1):1193 [FREE Full text] [CrossRef] [Medline]
- Eysenbach G. What is e-health? J Med Internet Res 2001 Jun 18;3(2):E20 [FREE Full text] [CrossRef] [Medline]
- Coronavirus (COVID-19): notification to organisations to share information. GOV.UK. URL: https://www.gov.uk/government/publications/coronavirus-covid-19-notification-of-data-controllers-to-share-information [accessed 2021-12-11]
- Hanratty B, Burton JK, Goodman C, Gordon AL, Spilsbury K. Covid-19 and lack of linked datasets for care homes. BMJ 2020 Jun 24;369:m2463. [CrossRef] [Medline]
- Comas-Herrera A, Ashcroft E, Lorenz-Dant K. International examples of measures to prevent and manage COVID-19 outbreaks in residential care and nursing home settings. International Long Term Care Policy Network. URL: https://ltccovid.org/2020/05/02/new-report-international-examples-of-measures-to-prevent-and-manage-covid-19-outbreaks-in-residential-care-and-nursing-home-settings/ [accessed 2021-12-11]
- Haralambous B, Subramaniam S, Hwang K, Dow B, LoGiudice D. A narrative review of the evidence regarding the use of telemedicine to deliver video-interpreting during dementia assessments for older people. Asia Pac Psychiatry 2019 Sep;11(3):e12355. [CrossRef] [Medline]
- Schaller S, Marinova-Schmidt V, Setzer M, Kondylakis H, Griebel L, Sedlmayr M, et al. Usefulness of a tailored eHealth service for informal caregivers and professionals in the dementia treatment and care setting: the eHealthMonitor dementia portal. JMIR Res Protoc 2016 Apr 05;5(2):e47 [FREE Full text] [CrossRef] [Medline]
- Popay J, Roberts H, Sowden A, Petticrew M, Arai L, Rodgers M. Guidance on the Conduct of Narrative Synthesis in Systematic Reviews: A Product From the ESRC Methods Programme. United Kingdom: Lancaster University; 2006.
- Methley AM, Campbell S, Chew-Graham C, McNally R, Cheraghi-Sohi S. PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv Res 2014 Nov 21;14:579 [FREE Full text] [CrossRef] [Medline]
- Hupe M. EndNote X9. J Electronic Resources Med Libraries 2019 Nov 26;16(3-4):117-119. [CrossRef]
- CASP Checklists. CASP-UK. URL: https://casp-uk.net/casp-tools-checklists/ [accessed 2021-12-11]
- Hong QN, Fàbregues S, Bartlett G, Boardman F, Cargo M, Dagenais P, et al. The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Educ Inf 2018 Dec 18;34(4):285-291. [CrossRef]
- Critical appraisal tools. University of Adelaide. URL: https://joannabriggs.org/critical-appraisal-tools [accessed 2021-12-11]
- Safaeinili N, Brown-Johnson C, Shaw JG, Mahoney M, Winget M. CFIR simplified: pragmatic application of and adaptations to the Consolidated Framework for Implementation Research (CFIR) for evaluation of a patient-centered care transformation within a learning health system. Learn Health Syst 2020 Sep 26;4(1):e10201 [FREE Full text] [CrossRef] [Medline]
- Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci 2009 Aug 07;4:50 [FREE Full text] [CrossRef] [Medline]
- Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009 Jul 21;6(7):e1000097 [FREE Full text] [CrossRef] [Medline]
- Mitchell SL, Shaffer ML, Cohen S, Hanson LC, Habtemariam D, Volandes AE. An advance care planning video decision support tool for nursing home residents with advanced dementia: a cluster randomized clinical trial. JAMA Intern Med 2018 Jul 01;178(7):961-969 [FREE Full text] [CrossRef] [Medline]
- Moniz-Cook E, Hart C, Woods B, Whitaker C, James I, Russell I. Challenge Demcare: management of challenging behaviour in dementia at home and in care homes. Programme Grants Appl Res 2017;5(15). [CrossRef]
- Daly JM, Buckwalter K, Maas M. Written and computerized care plans. Organizational processes and effect on patient outcomes. J Gerontol Nurs 2002 Sep;28(9):14-23. [CrossRef] [Medline]
- De Luca R, Bramanti A, De Cola MC, Trifiletti A, Tomasello P, Torrisi M, et al. Tele-health-care in the elderly living in nursing home: the first Sicilian multimodal approach. Aging Clin Exp Res 2016 Aug;28(4):753-759. [CrossRef] [Medline]
- Mitchell SL, Volandes AE, Gutman R, Gozalo PL, Ogarek JA, Loomer L, et al. Advance care planning video intervention among long-stay nursing home residents: a pragmatic cluster randomized clinical trial. JAMA Intern Med 2020 Aug 01;180(8):1070-1078 [FREE Full text] [CrossRef] [Medline]
- Weiner M, Schadow G, Lindbergh D, Warvel J, Abernathy G, Perkins S, et al. Clinicians' and patients' experiences and satisfaction with unscheduled, nighttime, internet-based video conferencing for assessing acute medical problems in a nursing facility. AMIA Annu Symp Proc 2003:709-713 [FREE Full text] [Medline]
- Mor V, Volandes AE, Gutman R, Gatsonis C, Mitchell SL. PRagmatic trial Of Video Education in Nursing homes: the design and rationale for a pragmatic cluster randomized trial in the nursing home setting. Clin Trials 2017 Apr;14(2):140-151 [FREE Full text] [CrossRef] [Medline]
- Klein B, Clinnick L, Chesler J, Stranieri A, Bignold A, Dazeley R, et al. Supporting regional aged care nursing staff to manage residents' behavioural and psychological symptoms of dementia, in real time, using the Nurses' Behavioural Assistant (NBA): a pilot site 'end-user attitudes' trial. Stud Health Technol Inform 2018;246:24-28. [Medline]
- Piau A, Vautier C, De Mauleon A, Tchalla A, Rumeau P, Nourhashemi F, et al. Health workers perception on telemedicine in management of neuropsychiatric symptoms in long-term care facilities: two years follow-up. Geriatr Nurs 2020;41(6):1000-1005 [FREE Full text] [CrossRef] [Medline]
- Shiells K, Diaz Baquero AA, Štěpánková O, Holmerová I. Staff perspectives on the usability of electronic patient records for planning and delivering dementia care in nursing homes: a multiple case study. BMC Med Inform Decis Mak 2020 Jul 13;20(1):159 [FREE Full text] [CrossRef] [Medline]
- Vuorinen M. Registered Nurses' experiences with, and feelings and attitudes towards, the International Resident Assessment Instrument for long-term care facilities in New Zealand in 2017. J Res Nurs 2020 Mar;25(2):141-155 [FREE Full text] [CrossRef] [Medline]
- Alexander G, Rantz M, Flesner M, Diekemper M, Siem C. Clinical information systems in nursing homes: an evaluation of initial implementation strategies. Comput Inform Nurs 2007;25(4):189-197. [CrossRef] [Medline]
- Keenan J, Poland F, Manthorpe J, Hart C, Moniz-Cook E. Implementing e-learning and e-tools for care home staff supporting residents with dementia and challenging behaviour: a process evaluation of the ResCare study using normalisation process theory. Dementia (London) 2020 Jul;19(5):1604-1620 [FREE Full text] [CrossRef] [Medline]
- Johnston D, Jones BN. Telepsychiatry consultations to a rural nursing facility: a 2-year experience. J Geriatr Psychiatry Neurol 2001;14(2):72-75. [CrossRef] [Medline]
- Salles N, Lafargue A, Cressot V, Glenisson L, Barateau M, Thiel E, et al. Global geriatric evaluation is feasible during interactive telemedicine in nursing homes. European Res Telemedicine / La Recherche Européenne en Télémédecine 2017 Jul;6(2):59-65. [CrossRef]
- Alexander G. A descriptive analysis of a nursing home clinical information system with decision support. Perspect Health Inf Manag 2008 Sep 02;5:12 [FREE Full text] [Medline]
- Qadri S, Wang J, Ruiz J, Roos B. Personal digital assistants as point-of-care tools in long-term care facilities: a pilot study. Educ Gerontol 2009 Mar 10;35(4):296-307. [CrossRef]
- Munyisia EN, Yu P, Hailey D. The changes in caregivers' perceptions about the quality of information and benefits of nursing documentation associated with the introduction of an electronic documentation system in a nursing home. Int J Med Inform 2011 Feb;80(2):116-126. [CrossRef] [Medline]
- Catic AG, Mattison ML, Bakaev I, Morgan M, Monti SM, Lipsitz L. ECHO-AGE: an innovative model of geriatric care for long-term care residents with dementia and behavioral issues. J Am Med Dir Assoc 2014 Dec;15(12):938-942. [CrossRef] [Medline]
- Gordon SE, Dufour AB, Monti SM, Mattison ML, Catic AG, Thomas CP, et al. Impact of a videoconference educational intervention on physical restraint and antipsychotic use in nursing homes: results from the ECHO-AGE pilot study. J Am Med Dir Assoc 2016 Jun 01;17(6):553-556 [FREE Full text] [CrossRef] [Medline]
- Bjarnadottir R, Herzig C, Travers J, Castle N, Stone P. Implementation of electronic health records in US nursing homes. Comput Inform Nurs 2017 Aug;35(8):417-424 [FREE Full text] [CrossRef] [Medline]
- Wakefield B, Buresh K, Flanagan J, Kienzle M. Interactive video specialty consultations in long-term care. J Am Geriatr Soc 2004 May;52(5):789-793. [CrossRef] [Medline]
- Lee JH, Kim JH, Jhoo JH, Lee KU, Kim KW, Lee DY, et al. A telemedicine system as a care modality for dementia patients in Korea. Alzheimer Dis Assoc Disord 2000;14(2):94-101. [CrossRef] [Medline]
- Lyketsos CG, Roques C, Hovanec L, Jones BN. Telemedicine use and the reduction of psychiatric admissions from a long-term care facility. J Geriatr Psychiatry Neurol 2001;14(2):76-79. [CrossRef] [Medline]
- Krüger K, Strand L, Geitung J, Eide GE, Grimsmo A. Can electronic tools help improve nursing home quality? ISRN Nurs 2011;2011:208142 [FREE Full text] [CrossRef] [Medline]
- Perri G, Abdel-Malek N, Bandali A, Grosbein H, Gardner S. Early integration of palliative care in a long-term care home: a telemedicine feasibility pilot study. Pall Supp Care 2020 Feb 18;18(4):460-467. [CrossRef]
- Pillemer K, Meador RH, Teresi JA, Chen EK, Henderson CR, Lachs MS, et al. Effects of electronic health information technology implementation on nursing home resident outcomes. J Aging Health 2012 Feb;24(1):92-112 [FREE Full text] [CrossRef] [Medline]
- O'Mahony S, Marthone P, Santoro G, Horn C, Selikson S, Powell T, et al. Preliminary report of a pilot tele-health palliative care and bioethics program for residents in skilled nursing facilities. Palliat Care 2009 Dec 08;3:PCRT.S3296. [CrossRef]
- Fossum M, Alexander GL, Ehnfors M, Ehrenberg A. Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly. Int J Med Inform 2011 Sep;80(9):607-617. [CrossRef] [Medline]
- Shiells K, Holmerova I, Steffl M, Stepankova O. Electronic patient records as a tool to facilitate care provision in nursing homes: an integrative review. Inform Health Soc Care 2019 Sep;44(3):262-277. [CrossRef] [Medline]
- Dwyer R, Gabbe B, Stoelwinder JU, Lowthian J. A systematic review of outcomes following emergency transfer to hospital for residents of aged care facilities. Age Ageing 2014 Nov;43(6):759-766. [CrossRef] [Medline]
- Wolters A, Santos F, Lloyd T, Lilburne C, Steventon A. Emergency admissions to hospital from care homes: how often and what for? The Health Foundation. 2019. URL: https://www.health.org.uk/publications/reports/emergency-admissions-to-hospital-from-care-homes [accessed 2021-12-11]
- Delivering the benefits of digital health care. Nuffield Trust. 2016. URL: https://www.nuffieldtrust.org.uk/files/2017-01/delivering-the-benefits-of-digital-technology-web-final.pdf [accessed 2021-12-11]
- Bailey J. Does health information technology dehumanize health care? Virtual Mentor 2011 Mar 01;13(3):181-185 [FREE Full text] [CrossRef] [Medline]
- Versluis A, van Luenen S, Meijer E, Honkoop PJ, Pinnock H, Mohr DC, et al. SERIES: eHealth in primary care. Part 4: addressing the challenges of implementation. Eur J Gen Pract 2020 Dec;26(1):140-145 [FREE Full text] [CrossRef] [Medline]
- Slattery P, Saeri AK, Bragge P. Research co-design in health: a rapid overview of reviews. Health Res Policy Syst 2020 Feb 11;18(1):17 [FREE Full text] [CrossRef] [Medline]
- Goodman C, Sharpe R, Russell C, Meyer J, Gordon A, Dening T. Care home readiness: a rapid review and consensus workshops on how organisational context affects care home engagement with health care innovation. University of Hertfordshire Research Archive. 2017. URL: https://uhra.herts.ac.uk/handle/2299/18200 [accessed 2021-12-11]
- Christie HL, Bartels SL, Boots LM, Tange HJ, Verhey FR, de Vugt ME. A systematic review on the implementation of eHealth interventions for informal caregivers of people with dementia. Internet Interv 2018 Sep;13:51-59 [FREE Full text] [CrossRef] [Medline]
- Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A'Court C, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res 2017 Nov 01;19(11):e367 [FREE Full text] [CrossRef] [Medline]
- Ross J, Stevenson F, Lau R, Murray E. Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update). Implement Sci 2016 Oct 26;11(1):146 [FREE Full text] [CrossRef] [Medline]
- Ware P, Ross HJ, Cafazzo JA, Laporte A, Gordon K, Seto E. Evaluating the implementation of a mobile phone-based telemonitoring program: longitudinal study guided by the consolidated framework for implementation research. JMIR Mhealth Uhealth 2018 Jul 31;6(7):e10768 [FREE Full text] [CrossRef] [Medline]
- Doraiswamy S, Abraham A, Mamtani R, Cheema S. Use of telehealth during the COVID-19 pandemic: scoping review. J Med Internet Res 2020 Dec 01;22(12):e24087 [FREE Full text] [CrossRef] [Medline]
|CASP: Critical Appraisal Skills Program|
|CFIR: Consolidated Framework for Implementation Research|
|ESRC: Economic and Social Research Council|
|JBI: Joanna Briggs Institute|
|MMAT: Mixed Methods Appraisal Tool|
|NIHR: National Institute for Health Research|
|PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-analyses|
|PROSPERO: International Prospective Register of Systematic Reviews|
Edited by R Kukafka; submitted 23.04.21; peer-reviewed by D Valdes, S Lundell; comments to author 20.07.21; revised version received 29.10.21; accepted 02.11.21; published 03.02.22Copyright
©Juliet Gillam, Nathan Davies, Jesutofunmi Aworinde, Emel Yorganci, Janet E Anderson, Catherine Evans. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.02.2022.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.