Published on in Vol 11 (2024)

This is a member publication of Imperial College London (Jisc)

Preprints (earlier versions) of this paper are available at, first published .
Barriers to and Facilitators of Key Stakeholders Influencing Successful Digital Implementation of Remote Monitoring Solutions: Mixed Methods Analysis

Barriers to and Facilitators of Key Stakeholders Influencing Successful Digital Implementation of Remote Monitoring Solutions: Mixed Methods Analysis

Barriers to and Facilitators of Key Stakeholders Influencing Successful Digital Implementation of Remote Monitoring Solutions: Mixed Methods Analysis

Original Paper

1Division of Surgery, Imperial College London, London, United Kingdom

2West Middlesex University Hospital, London, United Kingdom

3Innovation Business Partner, Chelsea and Westminster NHS Trust, London, United Kingdom

Corresponding Author:

Fahad Mujtaba Iqbal, BSc, MBChB, PhD

Division of Surgery

Imperial College London

10th Floor QEQM Building

Praed Street

London, W2 1NY

United Kingdom

Phone: 44 020 3312 6666


Background: Implementation of remote monitoring solutions and digital alerting tools in health care has historically been challenging, despite the impetus provided by the COVID-19 pandemic. To date, a health systems–based approach to systematically describe barriers and facilitators across multiple domains has not been undertaken.

Objective: We aimed to undertake a comprehensive mixed methods analysis of barriers and facilitators for successful implementation of remote monitoring and digital alerting tools in complex health organizations.

Methods: A mixed methods approach using a modified Technology Acceptance Model questionnaire and semistructured interviews mapped to the validated fit among humans, organizations, and technology (HOT-fit) framework was undertaken. Likert frequency responses and deductive thematic analyses were performed.

Results: A total of 11 participants responded to the questionnaire and 18 participants to the interviews. Key barriers and facilitators could be mapped onto 6 dimensions, which incorporated aspects of digitization: system use (human), user satisfaction (human), environment (organization), structure (organization), information and service quality (technology), and system quality (technology).

Conclusions: The recommendations proposed can enhance the potential for future remote sensing solutions to be more successfully integrated in health care practice, resulting in more successful use of “virtual wards.”

Trial Registration: NCT05321004;

JMIR Hum Factors 2024;11:e49769



Since the onset of the COVID-19 pandemic, adoption and implementation of novel health care pathways have accelerated globally. A key change has been transitioning beyond the traditional face-to-face model of health care delivery with the incorporation of novel remote monitoring solutions [1,2]. They offer a significant advantage in moderating viral exposure risk to health care staff, reducing community spread, and delivering quality health care remotely for exposed or infected individuals [3,4].

The integration of telemedicine and remote monitoring into medical practice is expected to expand by appropriately permitting selected individuals to continue living at home rather than admitting them into secondary care; this very premise is the foundation of “virtual wards” [5]. With the recent improvements made to wearable technology, they can support health provider assessment and clinical decision-making through collected biometric data both in secondary care and in the community [6-10].

However, successful implementation of digital technologies across complex hospital systems is seldom a smooth process [11-13]. The absence of standardized procedures for implementation and evaluation alongside the deficiency of published implementation strategies adds to these difficulties. One study in the National Health Service (NHS) that implemented wearable sensors and alerting systems in secondary care reported no improvements in clinical outcomes among patients [14,15]. The aim was to use wearable sensors to provide continuous remote monitoring to patients admitted to acute (nonintensive) wards and alert health care staff upon recognition of deterioration. Interestingly, although the digital solution was able to pick up clinical deterioration in vital signs and alert health care staff, responding to the alert was met with significant delay. This was in spite of health care staff in the NHS reporting favorable perceptions of digital solutions with potential improvements to patient safety and reduced staff burden [16]. Therefore, there is a need to further explore implementation issues.

Patients have reported high levels of acceptance, comfort, and safety and deemed such digital tools favorable [17-19]. The main concerns, from a patient perspective, surround potential overreliance on numbers with diminishing contact from clinical staff [17,20,21]. Health care staff perceptions, however, have been more mixed, with concerns regarding changing and increasing workloads, uncertainty surrounding the clinical meaningfulness of captured data, and alert fatigue [19-21]. Although mixed methods exploration of these 2 key stakeholder groups has been well documented, understanding how to integrate remote monitoring digital tools in the NHS requires further examination of cultural and management issues in the health care organization, an area where evidence is missing.

In the United Kingdom, large health informatics programs and widespread digital transformations are delivered by NHS Digital, a nondepartmental public body [22,23]. To support digitization, NHS England has formed a framework consisting of 3 ambitions: digital readiness, maturity, and data-enabled services [24]. In line with this, NHS England has supported the development and use of virtual wards, further indicating the “digital push” [5]. For policy makers, understanding the barriers and facilitators as perceived by key organizational members is crucial for the effective provision and smooth deployment of digitally enabled care. A proposed framework evaluates these aspects, incorporating the concept of fit among humans, organizations, and technology (HOT-fit) [25]. This framework offers a structured basis to examine factors that focus on alignment and compatibility across these 3 domains, thereby enhancing the effectiveness of digital health care initiatives.

Therefore, the aim of this study was to evaluate key stakeholder perspectives on an organizational level of implementing remote monitoring solutions in the NHS, identifying factors that could affect successful execution and adoption using the HOT-fit framework. In doing so, we propose a road map for implementing wearable solutions in secondary care.

Study Design

A mixed methods approach was implemented that consisted of semistructured interviews and questionnaires [26]. This was developed in accordance with recommendations from the Standards for Reporting Qualitative Research (SRQR) guidelines where appropriate [27]. The semistructured interviews were conducted with high-level stakeholders from industry and academia, as well as with health care providers who played an instrumental role in and had prior experience of implementing digital solutions. Additionally, a validated questionnaire was used to ascertain the perceived technological acceptance of new remote monitoring systems.

To ensure appropriate recruitment among all key stakeholder groups, a key informant strategy was followed for purposive recruitment [28,29]. Individuals were identified through their notable work with implementation of remote monitoring solutions in health care, including authors of impactful research in the literature, major digital technology companies, technicians involved with digital tool infrastructure development, and experts recommended by peers. This represented a variety of groups, including academics, clinicians, allied health care professionals, and employees of Google Health, who had experience with implementing digital solutions with the NHS.

Ethical Considerations

All recruited participants provided written informed consent. Ethical approval for this study was obtained by Imperial College London’s Science Engineering Technology Research Ethics Committee (20IC6331), and it was conducted in accordance with the Good Clinical Practice guidelines and the Declaration of Helsinki. Storage and handling of personal data complied with the General Data Protection Regulation. Interviews were recorded, anonymized, and transcribed.


An adapted version of the Technology Acceptance Model (TAM) questionnaire was used; this validated questionnaire has shown acceptably high Cronbach α values [30]. This ensures the reliability of our findings, contributing to the robustness of the study’s methodology and its implications in understanding technology acceptance dynamics. The proposed theoretical framework (information technology acceptance) is shown in Figure 1. It has been adapted from Chau and Hu [31], comprising individual context, technological context, and organizational context. Further adaptations from Gagnon et al [30], with the inclusion of theories of interpersonal behavior and reasoned action building on the TAM, proposed by Davis [32], have been included [30-34]. As such, individual context consists of compatibility (factors that affect acceptance of a new technology) and attitude (perception of the individual to adopting a technology); technological context consists of perceived usefulness and perceived ease of use of technologies. Lastly, organizational context consists of facilitators and subjective norms; the latter can be described as social (an individual’s perception of a behavior) or descriptive (behavior of others).

Figure 1. Theoretical framework for the modified Technology Acceptance Model questionnaire [30].

Semistructured Interviews

All participants were invited to take part in semistructured interviews conducted by the lead researchers. A structured topic guide was created following a literature review that drew heavily from a model proposed by Simblett et al [35] and by the HOT-fit framework [25].

Data collection was an iterative process; emerging recurring concepts were incorporated into the interview guide for further exploration with remaining participants. Interviews were recorded, anonymized, and transcribed verbatim before being entered into NVivo (version 12; QSR International) for analysis.

HOT-Fit Framework

This validated framework identifies dimensions that can be mapped onto and used as reference models for evaluating the performance, effectiveness, and impact of health systems [25,36]. A fit between human, organizational, and technological factors is required to ensure successful implementation and has been highlighted in Figure 2.

Figure 2. The fit among humans, organizations, and technology (HOT-fit) framework, adapted from Yusof et al [25].

Data Analysis

Frequency distributions were generated for the 7-point Likert scale responses to the modified TAM questionnaire using R studio (R Foundation for Statistical Computing) with the Likert package (Bryer and Speerschneider).

Transcribed interviews were analyzed using a broadly deductive approach [37], with the topic guide adapted as previously described [35]. This formed the basis for the initial predefined coding framework and was undertaken by 2 independent researchers to determine barriers and facilitators [37]. An iterative process of coding and data indexing occurred, ensuring key aspects were not missed from the predefined coding framework. Subsequent emerging themes were summarized and mapped to the evaluation measures corresponding to each dimension of the HOT-fit framework [25]. The results were discussed until consensus was reached.

Overviews of the included participants and the reported evaluation measures are shown in Tables 1 and 2, respectively.

Table 1. Demographics of included participants.
GroupRole 1Role 2Role 3Role 4Role 5Role 6Role 7
Health care trustsDirector of strategy, research and innovationChief clinical information officer and Caldicott GuardianDigital quality improvement leadProject managerChief information officerSystems, integration interoperability architectLead nurse for remote monitoring
AcademicsClinical lecturerClinical lecturerChief scientific advisora
Google HealthClinical leadClinical specialistProduct managerImplementation specialistImplementation managerProgram manager
OtherProgramme director: innovation of healthManaging director: digital health

a—Not applicable.

Table 2. Overview of reported evaluation measures.
Dimension and evaluation measuresFactors
System use

Expectation and beliefs
  • Improved efficiency (facilitator)
  • Appropriate selection of end users suitable for digital tool (facilitator)

Training, knowledge, and expertise
  • Lack of troubleshooting support (barrier)
  • Engagement with new starters (facilitator)

  • Large data burden (barrier)
  • Post–COVID-19 fatigue of staff (barrier)
  • Finding local champions (facilitator)
User satisfaction (no evaluation measures)
  • Developing relationships for feedback (facilitator)
  • Previous negative experiences with no feedback on benefit (barrier)
Environment (no evaluation measures)
  • Overburdened National Health Service system (barrier)
Structure (clinical process)
  • Clear strategic framework and partnership (facilitator)
Information and service quality (no evaluation measures)
  • Poor interoperability (barrier)
  • Poor user interface and user engagement (barrier)
System quality
  • Failure to provide added value (barrier)

TAM Questionnaire

A total of 11 participants (response rate 11/22, 50%) responded to the questionnaire; the responses are represented as a Likert plot (Figure 3). Overall, the technology surrounding remote monitoring and virtual wards was perceived well by the questioned stakeholders, who considered that it facilitated the care of patients and that these pathways, initially introduced during the pandemic, were likely to change long-term provision of health care. However, some concerns were noted regarding whether the existing infrastructure could support the technology’s use and whether it would improve efficiency. Of note, there was uncertainty regarding whether most patients would welcome virtual wards or remote monitoring.

Figure 3. Likert plot displaying responses to the modified Technology Acceptance Model questionnaire. The percentages on the left and right sides of the plot represent the totals for negative and positive responses, while the percentages in the center represent neutral responses.

Semistructured Interviews

A total of 22 participants were approached, of whom 18 (response rate: 82%) participated in the semistructured interviews (Table 1). An overview of the factors, by dimension, that respondents felt were responsible for contributing to implementation is summarized in Table 2.

System Use
Expectations and Beliefs

The prospect of introducing novel remote monitoring technologies was felt to facilitate implementation through improved efficiency, particularly since the implementation of electronic health records has improved data availability and clarity:

...with the implementation [and] introduction of electronic health records where the data that’s available is so granular. And in addition to new technologies that are coming. I think that you can do a lot more, remotely or virtually, and it does make things a lot more efficient...
[Participant 15]

Moreover, respondents also commented that for successful implementation, a selective process should be in place for patients who would benefit the most from novel technologies, rather than using the technology in cases that would not be meaningful:

From a patient perspective, we don’t want to one size fits all approach. We need to be clear about how we personalize this and how it’s relevant and meaningful.
[Participant 17]
Training, Knowledge, and Expertise

Problems with troubleshooting and available training were reported to reduce successful implementation due to a lack of support:

We’ve had problems when trying to use the remote monitoring, it came up with an error and then I have to try and sort that out, you know? It’s just things like that that make extra work.
[Participant 16]
I know that the nurses have struggled a huge amount with remote monitoring, and I expected that...because there’s a lot of upskilling.
[Participant 18]

However, engaging early with health care workers and obtaining their involvement was shown to improve implementation of remote monitoring solutions:

[We received] better engagement by tying the implementation with the new starters in the role and the changeover of junior doctors, because it was a new product to offer to new junior doctors.
[Participant 3]

It was felt that motivation to engage with technologies would be impacted through the excessive availability of data acting as a deterrent:

We need to be mindful about the data burdens, not just for patients but for staff because this kind of remote technology follows you around. You basically could work 24/7 365 of the year.
[Participant 17]

In addition, following the pandemic, many health care workers were fatigued and unmotivated to engage in change, acting as a barrier to successful remote technology implementation:

Post-COVID the workforce has been decimated, been exhausted and is fatigued. It’s not the only problem though, because you know as well as I do that the NHS has run this model of where it’s good will. We’ve never had infrastructure that we needed to do stuff and we still get a huge amount done. So it’s not the only driver at the moment. It’s more noticeable because of where people’s heads are at and obviously where their physical levels and mental levels of exhaustion are...
[Participant 17]

However, respondents also noted that finding a few motivated individuals to champion change at a local level can help implementation:

I asked them to self-nominate three of them who were interested in helping [implement]. So they led and supported the [technology]...
[Participant 18]
User Satisfaction

Respondents reported that previous experience with digital tools tied into user satisfaction. Feedback to end users demonstrating meaningful impact was deemed important for engagement and successful implementation:

Where staff or patients, for example, have been involved in projects before that they haven’t had any feedback from, haven’t seen any meaningful outcome from...they’re like, well, why would I want to get engaged with this? That’s a lot of energy and effort from me and I won’t see any benefit.
[Participant 17]
...develop relationships, so between, if you like, supplier and developer and clinical staff so you’ve got these rapid cycles of feedback and learning.
[Participant 1]

Respondents reported that previous hindrance of effective implementation was because of an overburdened system unable to give the appropriate attention to integrating a digital solution in the NHS:

NHS is overburdened and so that level of diligence...wasn’t there until it had to be, until things became mission critical...that comes down to a bandwidth problem...
[Participant 10]

Similarly, underresourcing was noted to be a barrier, particularly during the early stages, where issues would arise:

More resource[s] to get [things] kick started [are usually needed]...because we had to go through all the teething problems ourselves which created extra work for us.
[Participant 16]
We’ve got very limited resources, that they’re very thinly spread across all of the IT projects that require integration and interoperability...just the sheer volume of work that the Trust has heaped on us over the last three or four years is the bigger constraining factor.
[Participant 4]

Lastly, organizational culture supporting digitization was a commonly reported theme, with some institutions more readily accepting of innovation than others:

Organisational culture can be both the barrier and facilitator. We know that there are some organisations that are much more ready and able to adopt innovation. I think from an organisational perspective, competing priorities are a huge issue...If your IT is majorly engaged in doing something else, for example an EHR implementation, its ability to support remote monitoring and other technologies is really poor.
[Participant 17]

Respondents also commented on the need for a clear process and said that developing a strategic partnership and framework would facilitate implementation and should be planned before rollout:

Strategic framework is crucial on things.... What does a strategic partnership look like? What is the direction that we want to jointly head in? What do we want to achieve together, and what are the different components to get there...
[Participant 1]
Making [the product vision and roadmap] clear as early on and getting that input right at the beginning of any kind of feature development. So that there is expectation alignment on what is being developed whether the minimal viable product meets the use cases that it needs to, and that there’s a partnership in prioritizing these features and when they’re delivered. As opposed to just showing a feature set a few weeks before it gets deployed.... I think that initial understanding of the vision...and getting that clinical engagement as early on helps to set the path going forward.
[Participant 12]
Information and Service Quality

Respondents noted the need for digital tools to be interoperable and usable, as poorly designed digital tools would be a barrier, hindering an overly strained NHS system:

The challenges are IT and interoperability…you don’t want 20 bits of data…from 20 apps that don’t work, so that’s the usability and the accessibility and the staffing of these models because traditionally they basically get added onto someone’s day job. But that day person’s already overwhelmed.
[Participant 17]
System Quality

Respondents highlighted that for a digital tool to be successfully implemented, it needed to provide added value, with perceived usefulness and ease of use being crucial.

[What] was the added value in [this digital app]? All it did was render some of the information that we already had in a limited manner, back in the mobile device.
[Participant 8]
Usability, the accessibility, and the staffing of these models [are really poor] because traditionally they basically get added onto someone’s day job.... The data element [is also] really poor, so you get a lot of enthusiasts doing a lot of projects. But if you then say where’s your evidence that makes any difference to anything meaningful that matters to patients and staff, they can’t produce that. I think the digital health tech industry has been really slow at that.
[Participant 17]

Furthermore, it was believed that the best way to implement a digital tool (eg, remote monitoring solutions) was through rapid quality improvement cycles following the plan-do-study-act (PDSA) technique, focusing on targeting user experience issues:

...believe the technology suffered from very poor clinical and user engagement. So I know [technological companies] will tell us they’ve had loads of user engagement, but actually most patients wouldn’t say that, they’d say well, why is it like this? No, why is nobody been engaged in the design for this?
[Participant 17]
...trying to give clinical input into feasibility, usability, implementation in terms of the design of how we were going to implement stuff, so...[a] genuine PDSA type approach to implementation, and I was quite involved in some of the thinking about spread and how do you get this utilized across different parts of the Trust...
[Participant 1]

Principal Findings

This study explored barriers and facilitators for implementing digital tools, in particular remote monitoring solutions, in the NHS, alongside the acceptance of such technology using the modified TAM questionnaire. Using the HOT-fit framework, human, organization, and technological factors were categorized, allowing for a multiple-angled approach to a multifaceted problem. Therefore, key barriers and facilitators could be mapped onto 6 dimensions, which incorporated aspects of digitization: system use (human), user satisfaction (human), environment (organization), structure (organization), information and service quality (technology), and system quality (technology).

With regards to system use, the importance of improving workflow efficiency, having appropriate troubleshooting support available for staff, finding local champions to help integration within the clinical workforce, and positively engaging with health care staff were highlighted as facilitators. To support this, young staff have been deemed the most likely to engage with and benefit from a new workflow [36,38-40]. This, in part, may be explained by more adept digital literacy skills and technical proficiencies associated with junior members [41]. In the literature, concise and tailored education surrounding implementation has been promoted as an important facilitator [42].

Key barriers relating to system use and environment included poor training and the burden of data, particularly with continuous remote monitoring of vital signs. These data may not always be clinically meaningful or because of poor resourcing may not be acknowledged appropriately, generating additional work for existing staff, who are already overburdened [14,43]. Previously, this unincentivized workflow change led to poor response times to alerts generated through alerting systems in an acute surgical ward [14]. In this study, 36% (4/11) of respondents to the modified TAM questionnaire were unsure whether allied health care professionals would welcome virtual wards (Figure 3). One study highlighted that these workers, in particular nurses and clinicians, were the most important gatekeepers for remote monitoring solutions [44]. Therefore, engaging these groups, fostering positive relationships, and delivering regular feedback would enhance user satisfaction, allow user interface and engagement issues to be proactively tackled, and subsequently enable successful implementation.

Concerning system quality, perceived usefulness and ease of use were deemed as important facilitators for successful implementation. In the literature, intuitive and user-friendly systems have been confirmed to have easier acceptance [36,39]. The modified TAM questionnaire similarly confirmed this in our cohort, particularly through questions concerning acquiring new skills and impact, emphasizing that remote monitoring technology could be readily accepted.


This study included key stakeholders belonging to a broad selection of groups (academics, industry, and health care) in order to create a broad understanding of factors that influence implementation of remote monitoring solutions in the NHS. Given that previous studies have focused on end user testing, this study sought to provide a top-down view to give a better understanding of considerations that could influence widespread implementation [16,18]. However, in doing so, our interpretations have some limitations. First, the broad, heterogeneous sample of key stakeholders included may identify issues that are generalizable, but the nonprobabilistic sampling may have resulted in a selection bias. Moreover, the included sample size was limited. Despite this, the use of semistructured interviews yielded pertinent considerations for pragmatic implementation in hospital settings. In addition, differences between various hospitals and departments, which may have different attitudes toward digital technologies, were not explored in this study. A final limitation relates to the HOT-fit framework; although it is considered useful, the mapping of factors is a subjective undertaking and mapping to one specific measure was, at times, difficult.

Further Research and Recommendations

Although our cohort showed that there was overall acceptance of remote monitoring technology (Figure 3), there remains a deficiency with respect to successful implementation. This was noted most recently in one study where the median time to acknowledge an alert from health care staff was 111 (range 1-2146) minutes, despite early recognition of deterioration from remote sensing [14]. Therefore, further research should incorporate human factors and behavior evaluation when implementing remote monitoring solutions with the NHS; moreover, implementation frameworks such as HOT-fit should be used to ensure multiple angles have been carefully considered.

To facilitate the effective integration of remote monitoring solutions in clinical workflows, a comprehensive strategic framework is paramount. This framework should prioritize the early involvement of end users, fostering relationships that enable rapid feedback on implementation strategies, user interfaces, and user experience issues. Such engagement allows for iterative enhancements through PDSA cycles, promoting continuous improvement [45].

Industries aiming to develop remote monitoring technologies must collaborate closely with key stakeholders, ensuring the creation of products that provide significant value and feature user-friendly interfaces. This approach emphasizes the importance of a bottom-up strategy in technology implementation, valuing the autonomy and insights of end users, who play a crucial role in the successful adoption of these solutions. Crucial to this process is the establishment of robust infrastructural support prior to the deployment of remote monitoring systems. Adequate resourcing and the involvement of technical support staff are essential to facilitate seamless integration with existing information technology frameworks, thereby enhancing the prioritization and effectiveness of digital health initiatives. By adhering to these guidelines, health care organizations can enhance the integration of remote monitoring into clinical practice, leading to improved operational efficiency, patient care, and overall health care service delivery.


Implementation of remote monitoring solutions in the NHS remains a complex challenge. The results of this study have highlighted key stakeholder perceptions that could influence successful integration. Through the proposed recommendations, there is potential for future remote sensing solutions to be more successfully integrated into our health care practices, resulting in novel pathways expanding beyond virtual wards.


Infrastructure support for this research was provided by the National Institute for Health and Care Research (NIHR) Imperial Biomedical Research Centre and the NIHR Imperial Patient Safety Translational Research Centre.

Data Availability

The data sets used and/or analyzed during the study are available from the corresponding author on reasonable request.

Authors' Contributions

FMI drafted the manuscript. Significant amendments were made by RA, MJ, MW, SK, HA, and AD. All authors approved the final manuscript.

Conflicts of Interest

AD is chair of the Health Security initiative and HA is chief scientific officer at Flagship Pioneering UK Ltd. Flagship Pioneering had no role in the development, conduct, or analysis of the study.

  1. Watson AR, Wah R, Thamman R. The value of remote monitoring for the COVID-19 pandemic. Telemed J E Health. Sep 2020;26(9):1110-1112. [CrossRef] [Medline]
  2. The impact of the COVID-19 pandemic on outpatient visits: changing patterns of care in the newest COVID-19 hot spots. The Commonwealth Fund. URL: https:/​/www.​​publications/​2020/​aug/​impact-covid-19-pandemic-outpatient-visits-changing-patterns-care-newest [accessed 2020-09-19]
  3. Xu S, Li Y. Beware of the second wave of COVID-19. Lancet. Apr 25, 2020;395(10233):1321-1322. [FREE Full text] [CrossRef] [Medline]
  4. WHO coronavirus disease (COVID-19) dashboard. World Health Organization. URL: [accessed 2020-06-25]
  5. Supporting information: virtual ward including hospital at home. NHS England. URL: https:/​/www.​​publication/​supporting-information-virtual-ward-including-hospital-at-home/​ [accessed 2022-06-07]
  6. Martirosyan M, Caliskan K, Theuns D, Szili-Torok T. Remote monitoring of heart failure: benefits for therapeutic decision making. Expert Rev Cardiovasc Ther. Jul 2017;15(7):503-515. [CrossRef] [Medline]
  7. Annis T, Pleasants S, Hultman G, Lindemann E, Thompson JA, Billecke S, et al. Rapid implementation of a COVID-19 remote patient monitoring program. J Am Med Inform Assoc. Aug 01, 2020;27(8):1326-1330. [FREE Full text] [CrossRef] [Medline]
  8. Ford D, Harvey JB, McElligott J, King K, Simpson KN, Valenta S, et al. Leveraging health system telehealth and informatics infrastructure to create a continuum of services for COVID-19 screening, testing, and treatment. J Am Med Inform Assoc. Dec 09, 2020;27(12):1871-1877. [FREE Full text] [CrossRef] [Medline]
  9. O'Carroll O, MacCann R, O'Reilly A, Dunican EM, Feeney ER, Ryan S, et al. Remote monitoring of oxygen saturation in individuals with COVID-19 pneumonia. Eur Respir J. Aug 2020;56(2):2001492. [FREE Full text] [CrossRef] [Medline]
  10. Medina M, Babiuch C, Card M, Gavrilescu R, Zafirau W, Boose E, et al. Home monitoring for COVID-19. Cleve Clin J Med. Jun 11, 2020.:1-4. [FREE Full text] [CrossRef] [Medline]
  11. Wachter R. Making IT work: harnessing the power of health information technology to improve care in England. Gov.UK. URL: https:/​/assets.​​media/​5a8091afe5274a2e87dba8f2/​Wachter_Review_Accessible.​pdf [accessed 2023-12-02]
  12. Greenhalgh T, Swinglehurst D, Stones R. Rethinking resistance to ‘big IT’: a sociological study of why and when healthcare staff do not use nationally mandated information and communication technologies. Health Soc Care Deliv Res. Nov 2014;2(39):1-112. [CrossRef] [Medline]
  13. Kruse C, Betancourt J, Ortiz S, Valdes Luna SM, Bamrah IK, Segovia N. Barriers to the use of mobile health in improving health outcomes in developing countries: systematic review. J Med Internet Res. Oct 09, 2019;21(10):e13263. [FREE Full text] [CrossRef] [Medline]
  14. Iqbal F, Joshi M, Fox R, Koutsoukou T, Sharma A, Wright M, et al. Outcomes of vital sign monitoring of an acute surgical cohort with wearable sensors and digital alerting systems: a pragmatically designed cohort study and propensity-matched analysis. Front Bioeng Biotechnol. 2022;10:895973. [FREE Full text] [CrossRef] [Medline]
  15. Iqbal FM, Joshi M, Khan S, Ashrafian H, Darzi A. Implementation of wearable sensors and digital alerting systems in secondary care: protocol for a real-world prospective study evaluating clinical outcomes. JMIR Res Protoc. May 04, 2021;10(5):e26240. [FREE Full text] [CrossRef] [Medline]
  16. Joshi M, Archer S, Morbi A, Ashrafian H, Arora S, Khan S, et al. Perceptions on the use of wearable sensors and continuous monitoring in surgical patients: interview study among surgical staff. JMIR Form Res. Feb 11, 2022;6(2):e27866. [FREE Full text] [CrossRef] [Medline]
  17. Downey C, Brown J, Jayne D, Randell R. Patient attitudes towards remote continuous vital signs monitoring on general surgery wards: an interview study. Int J Med Inform. Jun 2018;114:52-56. [CrossRef] [Medline]
  18. Joshi M, Archer S, Morbi A, Arora S, Kwasnicki R, Ashrafian H, et al. Short-term wearable sensors for in-hospital medical and surgical patients: mixed methods analysis of patient perspectives. JMIR Perioper Med. Apr 22, 2021;4(1):e18836. [FREE Full text] [CrossRef] [Medline]
  19. Leenen JPL, Dijkman EM, van Dijk JD, van Westreenen HL, Kalkman C, Schoonhoven L, et al. Feasibility of continuous monitoring of vital signs in surgical patients on a general ward: an observational cohort study. BMJ Open. Feb 17, 2021;11(2):e042735. [FREE Full text] [CrossRef] [Medline]
  20. Weenk M, van Goor H, Frietman B, Engelen LJ, van Laarhoven CJ, Smit J, et al. Continuous monitoring of vital signs using wearable devices on the general ward: pilot study. JMIR Mhealth Uhealth. Jul 05, 2017;5(7):e91. [FREE Full text] [CrossRef] [Medline]
  21. Areia C, King E, Ede J, Young L, Tarassenko L, Watkinson P, et al. Experiences of current vital signs monitoring practices and views of wearable monitoring: A qualitative study in patients and nurses. J Adv Nurs. Mar 2022;78(3):810-822. [FREE Full text] [CrossRef] [Medline]
  22. About NHS Digital. National Health Service. URL: [accessed 2020-09-19]
  23. NHS Digital annual report and accounts 2019 to 2020. National Health Service. URL: [accessed 2020-09-19]
  24. Digital framework for allied health professionals. NHS England. 2019. URL: https:/​/www.​​wp-content/​uploads/​2019/​04/​a-digital-framework-for-allied-health-professionals.​pdf [accessed 2023-12-02]
  25. Yusof M, Kuljis J, Papazafeiropoulou A, Stergioulas L. An evaluation framework for health information systems: human, organization and technology-fit factors (HOT-fit). Int J Med Inform. Jun 2008;77(6):386-398. [CrossRef] [Medline]
  26. Iqbal FM, Joshi M, Khan S, Wright M, Ashrafian H, Darzi A. Key stakeholder barriers and facilitators to implementing remote monitoring technologies: protocol for a mixed methods analysis. JMIR Res Protoc. Jul 21, 2022;11(7):e38437. [FREE Full text] [CrossRef] [Medline]
  27. O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. Sep 2014;89(9):1245-1251. [FREE Full text] [CrossRef] [Medline]
  28. Marshall MN. The key informant technique. Fam Pract. Feb 1996;13(1):92-97. [CrossRef] [Medline]
  29. Campbell S, Greenwood M, Prior S, Shearer T, Walkem K, Young S, et al. Purposive sampling: complex or simple? Research case examples. J Res Nurs. Dec 2020;25(8):652-661. [FREE Full text] [CrossRef] [Medline]
  30. Gagnon MP, Orruño E, Asua J, Abdeljelil AB, Emparanza J. Using a modified technology acceptance model to evaluate healthcare professionals' adoption of a new telemonitoring system. Telemed J E Health. 2012;18(1):54-59. [FREE Full text] [CrossRef] [Medline]
  31. Chau P, Hu P. Examining a model of information technology acceptance by individual professionals: an exploratory study. J Manag Inf Syst. Dec 23, 2014;18(4):191-229. [CrossRef]
  32. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. Sep 1989;13(3):319. [CrossRef]
  33. Hill RJ, Fishbein M, Ajzen I. Belief, attitude, intention and behavior: an introduction to theory and research. Contemp Sociol. Mar 1977;6(2):244. [CrossRef]
  34. Triandis HC. Nebraska Symposium on Motivation, 1979: Beliefs, Attitudes and Values. Lincoln, NB. University of Nebraska Press; 1980;195-259.
  35. Simblett S, Greer B, Matcham F, Curtis H, Polhemus A, Ferrão J, et al. Barriers to and facilitators of engagement with remote measurement technology for managing health: systematic review and content analysis of findings. J Med Internet Res. Jul 12, 2018;20(7):e10480. [FREE Full text] [CrossRef] [Medline]
  36. Kilsdonk E, Peute LW, Jaspers MWM. Factors influencing implementation success of guideline-based clinical decision support systems: A systematic review and gaps analysis. Int J Med Inform. Feb 2017;98:56-64. [CrossRef] [Medline]
  37. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. Jan 2006;3(2):77-101. [CrossRef]
  38. Lugtenberg M, Pasveer D, van der Weijden T, Westert GP, Kool RB. Exposure to and experiences with a computerized decision support intervention in primary care: results from a process evaluation. BMC Fam Pract. Oct 16, 2015;16(1):141. [FREE Full text] [CrossRef] [Medline]
  39. Varonen H, Kortteisto T, Kaila M, EBMeDS Study Group. What may help or hinder the implementation of computerized decision support systems (CDSSs): a focus group study with physicians. Fam Pract. Jun 2008;25(3):162-167. [CrossRef] [Medline]
  40. Zheng K, Padman R, Johnson MP, Diamond HS. Understanding technology adoption in clinical care: clinician adoption behavior of a point-of-care reminder system. Int J Med Inform. Aug 2005;74(7-8):535-543. [CrossRef] [Medline]
  41. Mobasheri MH, King D, Johnston M, Gautama S, Purkayastha S, Darzi A. The ownership and clinical use of smartphones by doctors and nurses in the UK: a multicentre survey study. BMJ Innov. Oct 07, 2015;1(4):174-181. [CrossRef]
  42. Trivedi MH, Daly EJ, Kern JK, Grannemann BD, Sunderajan P, Claassen CA. Barriers to implementation of a computerized decision support system for depression: an observational report on lessons learned in "real world" clinical settings. BMC Med Inform Decis Mak. Jan 21, 2009;9:6. [FREE Full text] [CrossRef] [Medline]
  43. Downey C, Randell R, Brown J, Jayne D. Continuous versus intermittent vital signs monitoring using a wearable, wireless patch in patients admitted to surgical wards: pilot cluster randomized controlled trial. J Med Internet Res. Dec 11, 2018;20(12):e10802. [FREE Full text] [CrossRef] [Medline]
  44. Whitten PS, Mackert MS. Addressing telehealth's foremost barrier: provider as initial gatekeeper. Int J Technol Assess Health Care. 2005;21(4):517-521. [CrossRef] [Medline]
  45. Leis JA, Shojania KG. A primer on PDSA: executing plan-do-study-act cycles in practice, not just in name. BMJ Qual Saf. Jul 2017;26(7):572-577. [CrossRef] [Medline]

HOT-fit: fit among humans, organizations, and technology
NHS: National Health Service
PDSA: plan-do-study-act
SRQR: Standards for Reporting Qualitative Research
TAM: Technology Acceptance Model

Edited by A Kushniruk; submitted 08.06.23; peer-reviewed by A Acharya, C Sakal; comments to author 19.01.24; revised version received 26.01.24; accepted 07.04.24; published 06.05.24.


©Fahad Mujtaba Iqbal, Ravi Aggarwal, Meera Joshi, Dominic King, Guy Martin, Sadia Khan, Mike Wright, Hutan Ashrafian, Ara Darzi. Originally published in JMIR Human Factors (, 06.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.