Factors Affecting Clinician Readiness to Adopt Smart Home Technology for Remote Health Monitoring: Systematic Review

Introduction

Background

The global population is aging, and people are living longer. Adults aged ≥65 years have a high burden of disease, and the current practice of housing and caring for older people in nursing homes is often considered the last resort [1]. With reports of lower-quality care in residential aged-care settings and demands for alternative aging-in-place solutions by a much more technology-savvy baby boomer generation [2,3], health-smart homes (HSHs) are capable of unobtrusive in-home monitoring to support the health and safety of older people. In addition to protecting older adults from unnecessary exposure to communicable diseases such as COVID-19, aging in place has several benefits, including the maintenance of social connectivity and proximity to friends and family [1,4,5]. Furthermore, the ability to age in place supports and maximizes independence, thereby enhancing well-being and quality of life while decreasing the financial burden of residential care costs [1,6,7]. Older adults are a unique population with age-related changes and conditions and yet could remain living in their ancestral homes with the help of HASs, which could also augment public or private home care services. However, while HAS technologies are maturing, to improve wider use of the HAS, gaining an understanding of clinician readiness to use HAS technologies is important because clinicians that make up the multidisciplinary care team constitute important stakeholders [8].

Smart Home Technology for Remote Health Monitoring

The term Internet of Things refers to a collection of “smart” devices that can acquire and connect data or information across environments and act on the information [9], for example, by sending an alert that a person’s overall activity has significantly decreased. Although wearables can be used in an in-home context, there are many different types of wearables for different purposes and that collect different types of data compared with the health-assistive smart home. In addition, wearables usually do not attract some of the privacy concerns associated with remote monitoring using health-assistive smart home technologies. The health-assistive smart home consists of Internet of Things devices such as unobtrusive sensors that are deployed in a home to monitor a person’s routine behaviors and activities of daily living, including movement around the home such as sleeping, eating, steps ambulated, and more without the person having to “wear” a device [10]. The health-assistive smart home can collect and analyze a variety of data with the help of intelligent algorithms. Data can then be used by clinicians to monitor potential changes in health in their older patients [11,12]. Clinicians and the multidisciplinary care team are positioned to be the primary end users of patient data derived from the smart home sensors. Smart devices enable clinicians to unobtrusively monitor their older patients using automated assessment of behaviors that are associated with changes in health, which could support pragmatic, data-driven clinical decision-making [13].

Readiness and Acceptance Models

Readiness and acceptance models have been developed to help integrate computer-based information systems and digital technologies into specific settings, including health care. Researchers have studied the factors that may impact acceptance of and readiness for new technologies for several decades [14,15]. Examples of studies that explore technology readiness and acceptability in clinicians include mobile electronic health records [16], information and communications technologies [17], electronic care plan systems [18], telemedicine readiness [19], and computer-generated nursing care plans [20]. Frameworks and theories have been developed to understand a person’s likelihood of accepting and using health technologies [21]. The technology acceptance model (TAM) is the predominant framework cited in the literature [22-24]. This framework is used to model the behavioral intention that leads people to accept a certain technology [22-24]. In this model, behavioral intention is influenced by the person’s attitude generated from their impression of the perceived usefulness of the technology, which then predicts the actual use of the technology [22-24]. The TAM was expanded to include social influences and cognitive instrumental processes (TAM 2) [22], and later, the TAM 2 was combined with determinants of perceived ease of use to form the TAM 3 [23]. Determinants of perceived ease of use included computer self-efficacy, perception of external control, computer anxiety, computer playfulness, perceived enjoyment, and objective usability [23]. The Unified Theory of Acceptance and Use of Technology was developed from previous work with the aim of explaining end-user intention to and use of IT [24]. Models can help with intentionally integrating computer-based information systems and digital technologies into specific settings, including health care. However, there are larger differences between adopting, for example, an electronic health record and the readiness of clinicians to adopt home health monitoring using smart home technology. Clinician adoption of smart home technology requires clinicians to use and understand a new form of evidence. Accordingly, using the findings of this systematic review, we developed a theoretical model to support clinician readiness for and adoption of HAS technology, which will be discussed at the end of the Results section.

Clinician Readiness

The research and development surrounding the health-assistive smart home is maturing, with deployment as part of research studies across a variety of care settings, including private homes, assisted living, residential memory care, and residential care settings such as nursing homes [25-27]. Accordingly, the readiness of clinician end users to adopt smart home technology–generated data for clinical decision-making is important if wider adoption of the health-assistive smart home is desired. Clinicians such as nurses, physicians, and other allied health professionals will play a key role in monitoring older adults living using health-assistive smart home technology. As end users, clinicians will need to use sensor data integrated with other health information and apply clinical judgment to triage information and liaise with the multidisciplinary health care team for early interventions [28,29]. Exploring factors that may impact readiness to adopt, including perceived benefits and challenges, is needed to inform the integration of the HAS into new models of home care and clinical practice. The purpose of this systematic review was to identify and summarize the factors that may impact clinician readiness to adopt smart home technology for remote health monitoring.

Methods

Review Question

What are the factors affecting clinician readiness to adopt smart home technology for remote health monitoring of community-dwelling older adults?

Inclusion Criteria

Participants

A preliminary search showed that, frequently, a variety of clinicians and other stakeholders have been included in studies that examined the phenomenon under review. Accordingly, clinicians aged ≥18 years, such as nurses, registered nurses, clinical nurses, physicians, allied health professionals, and other health care workers, were included. Studies that included nonclinical stakeholders as participants were included if clinicians were also included. Studies were excluded if we were unable to identify clinician participation.

Interventions

Because of the unique data that the health-assistive smart home can generate with the potential to augment clinician decision-making, this review included studies on health-assistive smart home sensor technology embedded or deployed in the home environment (eg, ceilings, walls, furniture, and appliances) to detect motion in persons living in private dwellings in the community, retirement villages, or aged-care homes and residential care homes. Studies focusing on telehealth and remote health monitoring were included if smart home sensors were also embedded in the home. Studies focusing only on wearable sensors or other technologies used for remote monitoring (eg, implanted defibrillators) were beyond the scope of this review and were excluded. Studies that did not provide sufficient information to ascertain whether smart home sensors were embedded or deployed in the home environment were also excluded.

Comparators

This review considered studies that compared the use of smart home technology to usual care. Studies that did not use comparisons such as cohort, case studies or descriptive qualitative studies, were also included. Due to the challenges with the feasibility of implementing the health-assistive smart home in real-world settings, studies that used “mock” or hypothetical health-assistive smart homes to study clinician readiness were included.

Outcomes

The primary outcome of this review was the identification of factors that may impact clinician readiness to adopt smart home technologies. Studies reporting clinician feedback, attitudes, perceptions, and experiences, including barriers, facilitators, and enablers regarding smart home technology, were included. Studies that reported nonclinical stakeholder feedback were included if clinicians were also included in the study.

Types of Studies

This review considered experimental and quasi-experimental studies, randomized controlled trials (RCTs), qualitative studies, case studies, and surveys. Human studies conducted in any geographical area and published in English from database inception to July 2024 were included.

Review Registration

The review protocol was registered with PROSPERO (CRD42020195989) before the commencement of the database searches.

Review Methodology

This review was conducted in accordance with the Joanna Briggs Institute (JBI) methodology for systematic reviews and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting.

Search Strategy and Study Selection

The initial search [30] of 12 databases up to July 2024 resulted in 17,204 references. Additional records were identified (n=24) through manual searching, including searching systematic review reference lists. After duplicates were removed, of the 17,228 study titles, a total of 13,423 (77.91%) were assessed. After the title search was completed, a title screen was performed by MD, and abstract screening was performed independently by 4 reviewers (GD, CG, MD, and DW). Full-text screening was performed independently by GD, CG, MD, and DW. A total of 155 full-text articles were comprehensively assessed for inclusion. Disagreements arising between the reviewers were resolved through discussion until consensus was reached. Of the 155 full-text articles, a total of 128 (82.6%) were excluded for the reasons previously stated. A total of 27 papers addressing clinician readiness, perspectives, and attitudes, among other things, regarding smart home technology were included in this systematic review.

The results will inform researchers and clinicians regarding the challenges and perceived benefits of adopting smart home technology for remote health monitoring. Finally, the findings of this review were used to develop a smart home adoption model for clinicians, which is presented in Figure 1.

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Assessment of Methodological Quality and Certainty of Findings

Eligible studies were critically appraised, and methodological quality was assessed by independent reviewers (GD, CG, MD, and DW) using the standardized critical appraisal instruments from the JBI [31]. This allowed the reviewers to achieve a greater insight into the methodological strengths and limitations of the selected studies. Blinding treatment groups was not always conceivable given the nature of the intervention; hence, it was not considered a criterion for inclusion. Any incongruities in appraisal that arose between reviewers were discussed and resolved by all authors. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach for assessing the certainty of evidence for an effect, summarized in narrative form, was used to assess the overall quality of the findings of each paper [31]. The GRADE assessment evaluates the limitations, indirectness, imprecision, inconsistency, and publication bias of the studies [31]. The overall quality of the evidence was then categorized as high, moderate, low, or very low. In total, 2 reviewers (GD and DW) independently completed the GRADE assessment for each article; there were no disagreements.

Data Extraction

Data were extracted from the studies by 2 independent reviewers (GD and DW) using an adapted version of the JBI standardized data extraction tool. Extracted data included specific details about the populations, study methods, types of smart home technology intervention used, outcomes assessed, and themes relevant to the review objective. Disagreements arising between the reviewers were resolved through team discussions. Both team members’ input was equally valued and used when coming to agreements.

Data Synthesis

As the studies included were heterogeneous, the findings of the selected studies were narratively synthesized to examine the barriers, facilitators, enablers, perceptions, and attitudes of clinicians and how these factors may impact the readiness to adopt smart home technology.

Results

Summary of the Search Results

The initial search (Figure 2) of 12 databases resulted in 17,204 references. Additional records were identified (n=24) through manual searching, including searching of systematic review reference lists.

Table 1 presents the comparative characteristics of the studies and the GRADE rating. Table 2 presents the summary of the key factors that may affect clinician readiness to adopt smart home technology, which have been categorized into perceived challenges and perceived benefits. Multimedia Appendix 1 presents descriptive themes and subthemes. The results will inform researchers and clinicians regarding the challenges and perceived benefits of adopting smart home technology for remote health monitoring. Finally, the findings of this review were used to develop a smart home adoption model for clinicians, which is presented in Figure 1.

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Outcomes Targeted

Clinician feedback, attitudes, perceptions, and experiences regarding the use of HAS technology is critical because clinicians are an important end-user group [46,58,59]. Clinicians may use the technology to monitor people living with chronic diseases [46] and use the information in care planning [13]. While the smart technology could optimize clinical care delivery and augment clinical decision-making, certain factors may impact clinicians’ readiness to adopt smart home technology for health monitoring.

Populations Included in the Review

Among the 27 studies included, most (n=19, 79%) had a mix of clinical and nonclinical participants, including social workers [32-34,43,44], management [33,35,45,50,51,55,58], policy makers [55,58] or decision makers [41], aging-in-place specialists or experts [35,40,41], older adults [40,42,50,55-57], relatives [42,52], informal caregivers [56,57], and technology designers and suppliers [35,41,55,58]. Table 1 shows that the clinician population recruited was highly variable. Health care workers and care staff and support workers were included in several studies [32-34,37,38,40-42,44,51,53,55,57,58]. The represented clinical disciplines included nursing [33,37,38,40,41,43,44,46-48,58], medicine [32,38,39,43,44,46,49,50,52,53], physiotherapy [34,35,37,43,44], ergotherapy [37], cardiology [46], occupational therapy [35,43,44], psychology [33,56], psychiatry [45], pharmacy [46], gerontology and geriatrics [45], speech and language pathology [35], and neurology and neuropsychology [45,50]. Clinicians worked in a variety of settings in various roles, including in community settings such as home care as direct care providers, navigators, or managers.

Smart Home Technology Use

The studies described a variety of smart home–type technology to monitor specific aspects of activities of daily living. The use of mainstream smart home technology such as home automation devices and smart speakers was described [35], and other studies (n=14, 52%) reported on the use of technologies for remotely monitoring several aspects of activities of daily living, including appliance use [41,55]; use of water [51]; medication adherence [47,49,55,57]; phone and coffee maker and kettle use [41,49]; bathroom and toilet visits [41]; time spent in different rooms and time spent outside [48]; cooking [51]; nighttime monitoring of resident location [38,39,42,52]; general in-home physical motion [57]; resident mobility, including steps ambulated, such as location, activity time, and duration [42]; door use and opening [39,41,42,51,52]; health issues [36,55]; fall detection [34,55,57]; wandering detection [55]; and monitoring of getting in and out of bed [39,42,46]. Several studies (n=6, 22%) included the use of cameras or microphones [33,53], wearables to track activity [46,54], and electrocardiograms to monitor heart rate connected to web-based portals and mobile devices [39]. Pressure mats [42], smart beds [40], security blankets [39], and alarms [52] were used to monitor residents’ activities of daily living, such as sleep and other activities, and remind them to engage in health activities (eg, taking their medication and sleep hygiene). Telecare and remote coaching to facilitate communication were also reported [33,39,52]. Authors described monitoring of task-based resident in-home activities [49] to facilitate residents’ independence and emotional connectivity while minimizing staff intrusions [37,51]. For example, in the study by Glasby et al [41], the authors report the implementation of home-based sensors with artificial intelligence capabilities in pilot case study sites. The sensors were placed in key locations of individual homes to remotely monitor opening and closing of doors, kettle use, refrigerator door opening and flushing of the toilet [41].

Notably, 30% (8/27) of the studies did not implement the smart home technologies but investigated health care professionals’ predeployment preferences for using smart home technologies [34,43,44,46,56,58], privacy concerns [33], and the issues associated with using smart home and telemonitoring systems [43,44,46]. For example, to obtain clinicians’ perspectives, Islam et al [46] provided the clinicians involved in the clinical care of patients with cardiovascular disease with a schematic for a prototype smart home system that included sensor-enabled medication monitoring; blood pressure, weight, and fat percentage monitoring; and a wearable for sleep and heart rate, as well as educational content, alerts, and messaging [46].

In another study, Warner et al [58] explored key stakeholders’ perceptions of the barriers to and facilitators of implementing passive remote monitoring technology, including motion sensors, cameras, and medication administration sensors among older home care service recipients [58].

Similarly, the studies by Hunter et al [43,44] investigated the issues that are potentially associated with using smart home and telemonitoring technologies to support older adults. The work by Hunter et al [43,44] was first reported as a research brief [43] and later as a traditional research paper [44]. Another study explored whether sensor-generated motion patterns could support the work of nurses [48] and elicited clinician’s views on the sensor-generated data visualization displays. The study by Beaudin et al [32] examined the types of physiological indicators that clinicians would like to monitor for the patients in their care [32]. The study by Rampioni et al [56] examined stakeholder perspectives on how information and communications technologies and sensing technologies could address the needs of older adults for active and healthy aging [56].

Discussion

Principal Findings

Perceived Benefits

Many clinicians in the studies included in this systematic review recognized the benefits of integrating smart home technology to monitor and facilitate care of their patients. First, clinicians perceived that overall quality of care can be enhanced with the use of a variety of patient data collected objectively and over time by the sensor technology [32,34,43,45,47-49,54]. For example, previously difficult-to-obtain data such as nighttime behaviors can provide accurate information about a patient’s well-being that may otherwise not be included in clinical decision-making [47]. In addition, there is often a mismatch between what patients report and what is actually happening in the home. Understanding the actual holistic needs of patients as unobtrusively as possible was considered helpful for health professionals [41]. Second, clinicians perceived that adopting smart home technology for remote health monitoring could lead to feeling more informed by real-time and just-in-time data [34,46-49]. Provision of just-in-time data can facilitate timely patient transitions [34], allow for appropriate clinical responses [34,47,48], and enable clinicians to act quickly in response to data that could forecast clinical emergencies [46,53,58]. Third, clinicians recognized that the combination of access to real-time, objectively collected data can facilitate clinicians’ understanding of patients’ needs and risks and tailoring of care approaches accordingly [46,53,58].

Fourth, clinicians perceived that access to data about activities of daily living collected in the context of the patients’ environment was beneficial because it could empower clinicians to identify early patterns of decline [32,34,47,48] and changes in cognitive and functional capacity [34,47-49], forecast future health events [46,47], and enable clinicians to tailor appropriate actions to address health concerns [32,49]. In addition, in-context sensor data [32] were considered more reflective of a patient’s health, which could empower clinicians to implement and evaluate tailored care approaches and identify and act upon any issues quickly [32,34,48]. Importantly, contextually collected smart home sensor data case studies could be useful to promote adoption of a specific intervention or care approach [49,58].

A final benefit perceived by clinicians was the potential for enhancing clinician-patient relationships and the ensuing positive psychosocial impact on patients. Clinicians recognized the potential of smart home technology to streamline clinical visits, saving clinicians and patients time [43,58] and reducing health care costs [45,50]. Importantly, clinicians could present the collected data to patients in the form of a visual summary [49], which could facilitate meaningful discussions with patients or family members about their care [32,51,58], enabling patient-centered, informed care decisions [48]. Smart home technology–enabled patient-centered care could support clinicians in addressing numerous dimensions of care concerns, such as respect for the values, preferences, and needs of patients; coordination of care and integration of services; communication and accurate, timely, and appropriate information; enhanced physical comfort and emotional support; involvement of family and friends; and transition and continuity of care [58,60]. Addressing these dimensions of care can lead to strong clinician-patient relationships, which can empower patients to feel more involved in their care [61,62] and self-management [46,56]. Consequently, contrary to perceived threats of smart home technology resulting in disconnection between clinicians and patients [39,57], many clinicians reported that the use of smart home technology for remote health monitoring could strengthen clinician-patient relationships and enhance patient motivation [32,50] and social relations [45].

Perceived Challenges

Clinicians perceived several factors that were challenges to the adoption of smart home technology for remote health monitoring. Chief concerns included privacy, data security, and the ethical and moral use of potentially “invasive” technology and the clinicians’ capacity to learn how to use the smart home technology and the generated data. First, nearly half (11/27, 41%) of the reviewed studies highlighted clinician concerns about the privacy and dignity of patients and the security of their data [32-35,39,42-44,46,47,51,53,57]. Brand et al [33] provided insight into the privacy concerns of a mix of stakeholders, including nonclinicians. Survey respondents emphasized the importance of both patient privacy and the secure storage of personal data. They were particularly concerned with the use of video cameras with visual fields that included bedroom and bathroom entrances [33]. While such locations within the home are essential for establishing and monitoring important patient norms regarding activities of daily living [63] and well-being [64], in contrast, Nilsen et al [39] acknowledged that monitoring using a digital night surveillance system with digital communication, sensors on doors, and electronic security blankets made it possible to monitor the patient without disturbing them [39]. These findings suggest that clinicians may perceive sensor technology as less intrusive than video cameras. In addition, clinicians who experienced using the smart home technology highlighted that physically entering a patient’s room was also an invasion of privacy [39]. Concerns were also raised regarding the privacy and ethical use of sensor-collected data by Peek et al [55] and Islam et al [46]. These authors emphasized the importance of confidentiality and protecting patients’ privacy via clear privacy policies when such technology is used. This suggests that both clear policies and a working knowledge of the available smart home sensor technology will be important for its successful adoption by clinicians.

Second, clinicians expressed concerns regarding their capacity to successfully adopt smart home technology, highlighting the demands required to master software and build the requisite competency [35,43,54] and the perceived time required to view and interpret data [47,54]. A lack of familiarity with sensor technology was a clear challenge [39], highlighted by clinicians citing the effort required to “get used to the system deployed” and “understand and interpret graphs” [47]. Notably, Ienca et al [45] reported a lack of clinician technical skills and digital resources as perceived causes of suboptimal care. While clinicians perceived the need to gain competency regarding smart home technology and the interpretation of smart home sensor data as a challenge, clinicians have been described as capable of understanding the depth of information derived from smart home technology over time [47].

In addition, while older adults enjoyed using smart home technology [55], clinicians expressed concerns about patient readiness to adopt complex smart home technology [44]. Similarly, Ding et al [35] explored the service delivery practices of mainstream smart home technology as assistive technology and found that there were concerns about lack of instructions or manufacturer guidelines on the compatibility and interoperability of devices.

Subsequently, the use of mainstream smart technology in combination with other health-related smart home technology may encounter barriers to adoption by clinicians. Clinicians may need to anticipate substantial education and knowledge gaps, especially among patients who may have concerns about Big Brother–type intrusion into their private life [43,58]. The potential deficits (skills and resources) of both clinicians and patients will need to be addressed for broad adoption. Deployment of a user-friendly and easily navigated system [51,55] could facilitate successful adoption and ensure optimal provision of care.

The perceived negative impact of smart home technology on patient well-being is another important factor that could impact clinician readiness to adopt from psychosocial, quality of care, and equity perspectives. Clinicians reported perceptions that smart home technology could be harmful to patients [58,65]. For example, patients may incorrectly interpret their sensor data assuming causation rather than correlation between behaviors and changes in health [32]. This could lead to self-directed lifestyle changes to the potential detriment of their overall health and quality of life [32,39]. In another example, Warner et al [58] found that patients may experience paranoia because of monitoring devices in the home.

Similarly, clinicians recognized that potential evidence of a decline in health could lead to depression or have a negative self-fulfilling effect on a patient’s self-perception [32], exacerbating their physical and mental conditions. Clinicians also raised concerns that remote monitoring may create real or imagined distance between themselves and their patients, which could have a negative impact on the health of the patient [39,57]. For example, many patients placed a high value on nurse-patient interactions [60]. Conversely, the question of equity of access was also raised by some clinicians, posing a social justice challenge to adoption wherein some patients may benefit from access to the technology while it remains inaccessible to others [39], thereby making it inequitable for clinicians to adopt smart home technology for certain segments of the population (eg, those who can afford it). All these factors regarding the well-being of patients expressed by clinicians need to be considered because they could impact clinicians’ readiness to adopt smart home technology [55].

Finally, some clinicians in the reviewed studies perceived the use of smart home technology as a threat to their professional culture, highlighting both logistic concerns and misconceptions about this technology [39,42,45,57]. In contrast to the perceived benefits of deploying smart home technology, some clinicians raised concerns that it may replace the role of some allied health professionals and create distance with patients [34]. Others expressed concern about the clash of professional cultures [39], emphasizing that technology cannot replace human-delivered person-centered care [39,42,45,57]. Some participants voiced concerns about potential incongruity between health care and IT and business priorities [39,42,45]. Such views highlight an important misconception and knowledge gap regarding smart home technology—that it is deployed to replace clinician contact with patients. However, the opposite is true if smart home technology can enhance clinician-patient communication [32,51] and enable clinicians to better understand their patients’ needs [49] and quickly respond [32,34,47]. Previous negative experiences with technology may exacerbate such misconceptions, highlighting the need for clinician-driven technology showcases and peer-reviewed evidence to establish the credibility, system reliability, and clinical validity of smart home technology [45].

A well-designed co-creation process involving key professional stakeholders can help address negative experiences, the incongruity between health care and IT priorities, and reported logistical concerns regarding time and money cost [35,39,58]. Theoretical models to help advance clinician readiness for smart home adoption could provide strategies that address or mitigate challenges that negatively impact the readiness for adoption. The findings of this systematic review suggest that there are several factors that impact clinician readiness to adopt smart home technology. The factors related to using smart technology and interventions for remotely monitoring patient health and well-being were categorized into perceived challenges and benefits.

If widespread adoption of health smart homes is the goal, a model is needed to facilitate the co-design of technology use, support its implementation, and ensure its ethical use. Figure 1 is a proposed theoretical model that could enable clinicians to weigh the benefits and challenges, including potential ethical issues of smart home implementation.

The first step in the model is to provide clinicians with opportunities to acknowledge both the challenges and potential benefits of smart home technology for remote health monitoring. Discussing potential benefits alone is not nor should be sufficient to convince clinicians to adopt smart home technology. For widespread adoption, potential challenges will need to be practically discussed and addressed. For example, the use of remotely collected sensor data on activities of daily living is a new form of evidence that clinicians will need to learn to use for evidence-based practice. Clinicians will need to have confidence that they will receive training on how to optimize their clinical decision-making using their patients’ sensor–collected smart home data [56].

Second, the experiences of early adopters [66] with smart home technology could be useful for clinicians because early adopters can acknowledge challenges, provide education on potential misconceptions, and affirm that clinicians are patient advocates. Early adopters can present the benefits of adopting this technology by showcasing the findings and lessons learned of real-world small- or larger-scale smart home technology implementation. Third, researchers [8] have an important role in facilitating clinician adoption through the co-creation process with clinicians, IT experts, business logistics professionals, patients, and patient advocates that includes the development or refinement of smart home technology, conduct of studies, development of policies, and creation of new smart home technology–enabled care paradigms. The dissemination of small- and large-scale implementation study findings and the presentation of data-driven case studies can collectively provide valuable, pragmatic evidence; critical evaluation; and commentary in peer-reviewed manuscripts. The dissemination of such evidence is useful to enhance clinician readiness and would support eventual widespread adoption of smart home technology for remote health monitoring as a standard of care for older adults who wish to age in place. Finally, reports of experiences of the co-creation process, such as that proposed by Nilsen et al [39] and Islam et al [46] and exemplified by the study by Glasby et al [41], are invaluable as they signpost a practical pathway to adoption and validate the clinical relevance of the purpose of smart home technology to support older adults.

Implications for Clinicians

More studies are showing that both older adults and family caregivers are open to adopting smart homes to facilitate and support aging in place [34,54,67]. It will be important for the members of the multidisciplinary health team to become knowledgeable of smart home technology and how it can be used to augment traditional care models by remotely monitoring health and well-being and supporting efficient and effective care coordination for the older person [51,59]. Although several different health disciplines were part of the studies in this review, it is clear that there are major differences in clinician expectations of the HAS. For example, home health nurses may be interested in smart home data to ensure that the older person is performing their usual daily routines, whereas a primary care physician may be more interested in certain data points that could help with early identification of a change in health. In addition, very little is known about how the different disciplines may envision the integration of the HAS into their clinical practice and the changes needed in the current systems and processes for the translation of the HAS to support the health and wellness of older adults living at home.

To benefit clinical care, clinicians will need to learn some technical and practical aspects of how to use the data collected by the smart home system that may be deployed in the homes of clients whom they are caring for. Accordingly, it is conceivable that the ever-evolving roles of clinicians will include leading change in the digital health space [50]. It is likely that workloads will be impacted; however, more knowledge is needed to understand how to integrate the smart home system into new “smart” models of care and how to use the sensor data to support clinical decision-making. The cost-benefit of this technology as a health care solution, including impacts on staffing shortages and care delivery in rural and remote locations, also needs to be investigated. Finally, for clinicians to embrace the artificial intelligent agent as a new member of the health care team, and to optimize the smart homes’ capability, clinicians need to be offered training specifically focused on the use of smart home technology and sensor data interpretation. Tertiary education frameworks need to include these monitoring technologies as a core competency.

Limitations

This review demonstrates that the scientific understanding of clinician readiness to adopt smart home technology is progressing. The studies reviewed were varied and all studies included a range of stakeholders with relatively small sample sizes, making it challenging to synthesize the findings for specific clinical disciplines. The diversity in study populations, which often included multiple types of clinicians and other stakeholders, makes it difficult to extract findings that are directly applicable to any single clinical discipline.

Only English-language papers were included; however, during our search, we did not encounter non–English-language papers. While qualitative studies with relatively small sample sizes are often ranked lower in traditional evidence hierarchies compared with RCTs, which are considered the “gold standard,” qualitative research provides essential insights into user experiences that are crucial for designing effective, user-centered technologies and the policies and procedures needed for implementation. Dismissing these contributions in favor of a rigid adherence to RCTs overlooks critical aspects of practical implementation and user acceptance that determine the real-world success of interventions. Moreover, the rapid pace of technological development necessitates timely research to keep up with innovations, suggesting that waiting for large-scale RCTs (or waiting for the funding to execute such studies) might render findings obsolete by the time they are applied or indeed stifle implementation in new models of care. The integration of smart home technology into models of care for older adults has been challenging, requiring researchers to partner with a variety of clinical and nonclinical professionals, such as technologists, designers, clinicians, and business and industry professionals, to study various aspects of co-design, acceptability, and implementation. This could explain why researchers have used a more pragmatic approach for their studies, including nonprobability sampling, purposive and convenience sampling, case studies, and multiple sources of data.

Another major drawback to synthesizing the literature is the incongruence of the definitions used for smart home technology and the fact that most studies included a mix of clinicians and nonclinicians. As clinicians represent a major end-user group, they need to be more involved in smart home co-design and implementation studies. More studies that focus on specific clinician groups (eg, nurses and physicians) are needed to determine the specific factors that could potentially undermine the success of smart home monitoring for patients at home. Finally, the COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines should be used to report comprehensive qualitative findings. While the conduct of RCTs may not be sufficiently pragmatic for this field, the use of mixed methods could yield more generalizable findings. The studies by Delbreil and Zvobgo [37], Elavsky et al [40], and Offerman-van Heek and Ziefle [53] are all good examples of providing readers with greater depth of knowledge regarding clinicians’ readiness.

Conclusions

This systematic review synthesized the literature on clinician readiness to adopt HAS technology for remote health monitoring—a key determinant for the future successful adoption of such technologies. The review identified several factors that may impact readiness for adoption among clinicians, highlighting both challenges and perceived benefits of smart home technology. The ensuing proposed theoretical model outlines the steps and key roles that early adopting clinicians, researchers, and cocreating stakeholders will have if widespread clinician adoption of smart home technologies for health monitoring occurs. Importantly, an incongruence in the terminology used in the limited body of literature to date reinforces the need for standardized reporting when discussing smart home technology to develop a clear and consistent body of credible evidence. Future research should continue to evolve this body of literature, evidencing the credibility, reliability, and clinical validity of smart home technology through a combination of peer-reviewed case studies, well-designed research trials, and commentaries. Continuing to reflect, act, and report on clinician’s experiences of smart home technology will help identify and overcome challenges to adoption and simultaneously affirm and embrace its benefits. Doing so can establish the professional acceptability and clinical validity of this technology, underpinning its widespread adoption in clinical practice.