Published on in Vol 3, No 1 (2019): Jan-Jun

Preprints (earlier versions) of this paper are available at, first published .
Provider- and Patient-Related Barriers to and Facilitators of Digital Health Technology Adoption for Hypertension Management: Scoping Review

Provider- and Patient-Related Barriers to and Facilitators of Digital Health Technology Adoption for Hypertension Management: Scoping Review

Provider- and Patient-Related Barriers to and Facilitators of Digital Health Technology Adoption for Hypertension Management: Scoping Review

Original Paper

1Partners HealthCare Pivot Labs, Boston, MA, United States

2Harvard Medical School, Boston, MA, United States

3Massachusetts General Hospital, Boston, MA, United States

4American Medical Association, Chicago, IL, United States

Corresponding Author:

Ramya Sita Palacholla, MD, MPH

Massachusetts General Hospital

55 Fruit St

Boston, MA, 02114

United States

Phone: 1 8579199889


Background: The uptake of digital health technology (DHT) has been surprisingly low in clinical practice. Despite showing great promise to improve patient outcomes and disease management, there is limited information on the factors that contribute to the limited adoption of DHT, particularly for hypertension management.

Objective: This scoping review provides a comprehensive summary of barriers to and facilitators of DHT adoption for hypertension management reported in the published literature with a focus on provider- and patient-related barriers and facilitators.

Methods: This review followed the methodological framework developed by Arskey and O’Malley. Systematic literature searches were conducted on PubMed or Medical Literature Analysis and Retrieval System Online, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medica database. Articles that reported on barriers to and/or facilitators of digital health adoption for hypertension management published in English between 2008 and 2017 were eligible. Studies not reporting on barriers or facilitators to DHT adoption for management of hypertension were excluded. A total of 2299 articles were identified based on the above criteria after removing duplicates, and they were assessed for eligibility. Of these, 2165 references did not meet the inclusion criteria. After assessing 134 studies in full text, 98 studies were excluded (full texts were either unavailable or studies did not fulfill the inclusion criteria), resulting in a final set of 32 articles. In addition, 4 handpicked articles were also included in the review, making it a total of 36 studies.

Results: A total of 36 studies were selected for data extraction after abstract and full-text screening by 2 independent reviewers. All conflicts were resolved by a third reviewer. Thematic analysis was conducted to identify major themes pertaining to barriers and facilitators of DHT from both provider and patient perspectives. The key facilitators of DHT adoption by physicians that were identified include ease of integration with clinical workflow, improvement in patient outcomes, and technology usability and technical support. Technology usability and timely technical support improved self-management and patient experience, and positive impact on patient-provider communication were most frequently reported facilitators for patients. Barriers to use of DHTs reported by physicians include lack of integration with clinical workflow, lack of validation of technology, and lack of technology usability and technical support. Finally, lack of technology usability and technical support, interference with patient-provider relationship, and lack of validation of technology were the most commonly reported barriers by patients.

Conclusions: Findings suggest the settings and context in which DHTs are implemented and individuals involved in implementation influence adoption. Finally, to fully realize the potential of digitally enabled hypertension management, there is a greater need to validate these technologies to provide patients and providers with reliable and accurate information on both clinical outcomes and cost effectiveness.

JMIR Cardio 2019;3(1):e11951



Digital health technologies (DHTs) have the potential to support active self-management of chronic conditions via education, monitoring and support, timely feedback, and remote access to health professionals [1]. When designed and implemented successfully, digital health interventions offer an opportunity to support the quadruple aim of health care by improving health outcomes, increasing patient experience, reducing health care costs, and improving clinician satisfaction [2]. The American Medical Association (AMA) defines digital health tools as those systems and solutions that engage patients for clinical purposes, collect, organize, interpret, use clinical data, and manage outcomes and other measures of care quality including telemedicine and telehealth, mobile health, wearables, remote monitoring, and apps [3]. The AMA digital health survey classifies digital health solutions into 7 categories: remote monitoring for efficiency, remote monitoring and management for improved care, clinical decision support, patient engagement, televisits, point-of-care, and tools providing consumer access to clinical data [3].

One-third of the US population has hypertension (85.7 million adults) [4] and the economic burden is close to US $ 53 billion dollars annually [5]. Despite having access to effective drugs for lowering blood pressure (BP), BP control in a vast majority of patients remains suboptimal [5], owing to infrequent monitoring of BP [6], low medication adherence by patients [7], and clinical inertia [8]. DHTs for hypertension management, such as telemonitoring programs, enhance self-monitoring as they allow for BP readings and clinical information to be shared with health care professionals in real time [9]. Remote monitoring for hypertension has been shown to improve medication adherence [10], optimize BP control [11], and reduce use of health care resources [12].

Although the shift to a value-based care system has encouraged the adoption and use of DHT to manage hypertension, the uptake of DHTs has been surprisingly low in clinical practice [13]. In addition, to our knowledge, there is limited information on the factors that influence adoption of digital health from the perspectives of both patients and providers. Previously published literature includes surveys of providers that cite factors influencing DHT adoption such as organizational and financial barriers [14]. Previous systematic reviews of telemedicine for hypertension management report increased access to health services, improved health and quality outcomes, and enhanced patient knowledge and involvement in disease management as strong facilitators of DHT usage in health care settings [13,15]. This review provides a comprehensive summary of facilitators and barriers to adopting digital health for hypertension management with a specific focus on the perspectives of providers and patients.

Literature Search

This scoping review was conducted using the methodological framework developed by Arskey and O’Malley [16]. The Arksey and O’Malley framework is particularly suited to address broad research questions and can help map the current literature, extract key concepts and themes, and identify gaps. The Arksey and O’Malley framework has several steps including (1) identifying the broad research question, (2) study selection using inclusion or exclusion criteria on the basis of familiarity with the topic of interest, (3) sorting the extracted data from studies into themes and patterns, and (4) collating key themes and issues [16]. The primary research question guiding this review was the following: What are the barriers and facilitators of digital health adoption for hypertension management?

Structured literature searches were conducted using 3 databases to identify relevant studies from 2008 to 2017: PubMed or Medical Literature Analysis and Retrieval System Online, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Excerpta Medica database (EMBASE). Medical subject headings (MeSH) and selected keywords were searched using Boolean operator OR and these groups were combined using another Boolean operator AND. Keywords used include (1) hypertension (MeSH), hypertensi, (2) mobile applications (MeSH), mobile device, (3) electronic health records (MeSH), personal electronic health record, (4) decision support systems, decision support, (5) remote monitoring (MeSH), (6) providers (MeSH), clinician. The detailed search strategies for PubMed have been provided as an example (see Multimedia Appendix 1). At first, 2 reviewers, with subject matter and methodological expertise, independently reviewed all abstracts identified by the searches and conflicts were resolved by a third reviewer. Then, 2 reviewers screened the full texts to select the final studies to be included in the review. Cohen kappa test revealed an agreement score of 0.75 between the reviewers. Per Landis and Koch, this agreement score could be categorized as substantial agreement between the reviewers [17].

All articles retrieved were screened using the following inclusion criteria: (1) reported on adoption barriers and/or facilitators of digital health solutions, as defined by the AMA, that were provider- or patient-related, (2) focused on hypertension management, (3) published in English, and (4) published between 2008 and 2017. Studies were excluded if they (1) did not report on barriers or facilitators of digital health, (2) described barriers or facilitators exclusively for nonclinical staff such as pharmacists, (3) were editorials or reviews for editorials, epidemiological studies, and protocols, (4) provided insights on acute management of hypertension in perioperative or intensive care settings, or (5) if full texts were unavailable. The authors also conducted a gray literature search (including conference proceedings) through a Web search engine. In addition, 4 articles were handpicked on the basis of the same inclusion criteria used for articles selected via literature databases.

Thematic Analysis

The selected papers were reviewed to extract relevant data. A data extraction template developed by the authors was used to extract key information and concepts from the included studies and the template included the following constructs: the geography, study design, program setting, disease conditions (in addition to hypertension), study objectives, sample description, sample size, digital health category, design features of the intervention, clinical outcomes, cost outcomes, patient experience, provider experience, patient-related barriers and facilitators, and provider-related barriers and facilitators. Descriptive and inductive thematic analyses were conducted for identifying major themes pertaining to barriers and facilitators of DHT adoption. For the analysis of the text passages from the included articles, the inductive thematic analysis was conducted as described by Braun and Clarke [18]. We developed our own a priori framework to categorize barriers into the following 4 categories: (1) provider-related facilitators, (2) provider-related barriers, (3) patient-related barriers, (4) and patient-related facilitators. This analytic process involved reading and rereading of the selected papers, systematically identifying and naming the unit of meaning with codes (words or sets of words that provide a meaning label), and then searching for patterns in the data and organizing the data (smaller themes or codes) into larger themes representing the main ideas and their relationships. Themes were then reviewed by the team and representative data elements were selected to demonstrate the salient themes. At first, 2 investigators (RP and NF) independently performed the initial coding of the first transcript. This coding was then reviewed by the third reviewer (AC). The codes were then reviewed and discussed with the team including senior researchers in the field, providers, and other subject matter experts. Later, 2 reviewers (RP and NF) then recoded all papers, integrating feedback from the team into the coding structure. A final codebook was created using Microsoft Office Excel (version 1808) on the basis of the consensus of the 3 investigators (RP, NF, and AC). During this process, any discrepancies in coding were discussed and resolved among all investigators. Furthermore, any questions about meaning and interpretation of themes were discussed among the team members and resolved through consensus.


A total of 2299 titles and abstracts from PubMed, CINAHL, EMBASE, and 4 handpicked articles from the supplementary gray literature search were assessed for eligibility after removing duplicates (see Figure 1). Of these, 2165 references did not meet the inclusion criteria. After assessing 134 studies in full text, 98 studies were excluded (full texts were either unavailable or studies did not fulfill the inclusion criteria). A total of 36 studies satisfied the inclusion criteria, including the 4 handpicked articles. The articles included in this review were published between 2008 and 2017, with a majority (n=30) published after 2010. Studies were published across the following countries: United States (n=21), United Kingdom (n=4), Canada (n=3), Finland (n=1), Sweden (n=1), Italy (n=1), Taiwan (n=1), Malaysia (n=1), South Korea (n=1), Kenya (n=1), and Germany (n=1). DHTs included in this review were classified into categories as defined by the AMA: remote monitoring for efficiency (n=6), remote monitoring and management for improved care (n=19), clinical decision support (n=6), patient engagement (n=4), televisits or virtual visits (n=6), point-of-care(n=2), and tools providing consumer access to clinical data (n=1). Most studies were conducted in a primary care setting (n=30). A plurality of studies included qualitative assessments (n=15). Quantitative methodologies included randomized controlled trials (RCTs; n=14), nonrandomized trials (n=2), usability pilots (n=2), and pre and poststudies (n=2). In addition, 1 white paper was also included in this review. Multimedia Appendix 2 displays a summary of the studies included in this review. The results of the thematic analysis have been categorized as provider- and patient-related facilitators and barriers as detailed below. Tables 1 and 2 summarize all the themes.

Figure 1. Study selection flow diagram.
View this figure
Table 1. Summary and frequency of provider-related themes and sub-themes identified from authors’ thematic analysis of the 36 studies in this review. Most studies included in the review reported multiple themes. Frequency of a barrier or a facilitator=total number of occurrences of a facilitator or the barrier and total frequency of occurrences of facilitators and barriers.
VariableOccurrences and frequency, n (%)

1. Ease of integration with clinical workflow [19-25]; Actionable data to provide timely interventions to patient [20,22,23,26]; Integration with clinical routine and less time-consuming tasks [20,21]; Care team support: opportunity for delegation and team-based care [19,20,24,25]8 (33)

2. Improvement in patient health outcomes [20,23,25,27,28]; Technology prevalidated to improve outcomes [20,25,27]; Positive impact on patients and their self-management [20,27,28]; Better monitoring of patients to prevent negative outcomes [23]5 (21)

3. Technology usability and technical support [29-36]; Technology requires minimal training [29,35,36]; Ease of use [29,30,35,36]; Adequate training support [31-33]8 (33)

4. Financial factors [27,37]2 (8)

5. Leadership and organizational support [38]1 (4)

1. Lack of integration with clinical workflow [9,19-21,24,25,30,39-41]; Lack of integration with electronic medical record [24]; Additional time-consuming tasks for providers [9,19-21,39-41]; Clinically irrelevant data [25,30]10 (36)

2. Lack of validation of technology [14,32,38,42-45]; Concern over data accuracy [14,42-44]; Lack of evidence of improvement in patient outcomes [32,38,45]7 (25)

3. Concern over data privacy and security [32]1 (4)

4. Lack of technology usability and technical support [30,34,38-40,43,46]; Frequent technical issues [34,39] Lack of ease of use [30,39,40,43,46]; Long learning curve [38]7 (25)

5. Lack of leadership and organizational support [32,40]2 (7)

6. Increased patient anxiety [14]1 (4)

aTotal frequency of occurrences of facilitators=20.

bTotal frequency of occurrences of barriers=28.

Table 2. Summary and frequency of patient-related themes and sub-themes identified from authors’ thematic analysis of the 36 studies in this review. Most studies included in the review reported multiple themes. Frequency of barrier and facilitator=total number of occurrences of a facilitator or barrier and total frequency of occurrences of facilitators or barriers.
VariableOccurrences and frequency, n (%)

1. Technology usability [19,24,30,34,36,46-50]; Ease of use [19,24,30,36,46,48-50]; Technical support [47,48]; Integration into patient’s daily routine [46]10 (29)

2. Positive impact on patient-provider communication [19,20,28,37,46,49-51]; Improved and more timely feedback from providers [19,20,28,37,46,49-51]; Shared decision making with providers [46]; Better preparation for clinic visits [28]8 (24)

3. Improved self-management and patient experience [19,21,24,30,33,36,39,46,52]; Increased motivation to better manage health [36,39]; Increased access to health data [21,24,30,33,36,46]; Alleviation in anxiety from better monitoring of health data [19,52]9 (26)

4. Reduction of in-office visits [19,21,24,25,37,41,52]7 (20)

1. Lack of technology usability and technical support [14,19,20,30,47,48]; Frequent technical glitches [14,19,21,50,53]; Lack of ease of use of system [43,50]; Patient not confident in using device [14,19,50]9(41)

2. Interference with patient-provider relationship [19,20,37,42,47]; Fear of having less direct in-person communication with provider [19,37]; Lack of feedback from providers [42,47]; Disrupting feelings of independence [20,37]5 (23)

3. Lack of validation of technology [19,43,47]3 (14)

4. Increased patient anxiety [49,52]2 (9)

5. Concern over data privacy and security [48]1 (5)

6. Cost of digital health equipment [42,47]2 (9)

aTotal frequency of occurrences of facilitators=34.

bTotal number of occurrences of barriers=19.

Facilitators of Digital Health Adoption

Provider Factors
Ease of Integration With Clinical Workflow

The findings suggest that integration of a new technology into the existing workflow of a provider strongly influences DHT adoption (n=2) [20,21]. Providers cited that having a care team to support DHT implementation as part of the clinical workflow was an important facilitator of adoption (n=4) [19,20,24,25]. Some studies found that providers were able to successfully adopt DHTs when the data that the DHT provided were actionable and could be readily utilized within preexisting clinical workflows to enable timeline intervention to improve patient outcomes (n=4) [20,22,23,26]. Providers were also attracted to DHTs that provided automatic alerts identifying the need for a change in medications or dosage [23], as they helped perform routine tasks faster (n=1).

Improvement in Patient Health Outcomes

Providers’ beliefs regarding whether the technology improved clinical outcomes or engaged patients in self-management were among the most important considerations (n=3) [20,27,28] for embracing DHTs. In some instances, the DHTs that were validated in pilot and RCTs and shown to improve outcomes were perceived to be more acceptable to providers (n=4) [20,25,27,28]. Furthermore, providers valued their patients becoming more active and engaged in their own health (n=2) [20,28]. Finally, DHTs that enabled a more timely response to elevated BP levels helped providers prevent adverse health outcomes in their patients by addressing the changes in BP levels in a timely manner (n=1) [23].

Technology Usability and Technical Support

Some studies reported that providers valued the simplicity and ease of use of a system (n=4) [29,30,35,36]. Furthermore, providers preferred DHTs that required minimal training (n=3) [29,35,36]. Providers valued adequate technical support when using DHTs as a part of their clinical workflow (n=3) [30,34,35].

Financial Factors

A few studies reported that financial incentives such as physician reimbursement for using DHTs in their clinical practice and cost savings as a result of implementing DHTs were important influencers of provider adoption (n=2) [27,37].

Leadership and Organizational Support

An organizational culture of innovation coupled with the presence of physician champions was cited as a factor influencing the adoption of DHTs in clinical settings, as it was often difficult for clinicians to implement DHTs without the support of their organization and leadership, particularly in terms of required budget and personnel (n=1) [38].

Patient Factors
Technology Usability and Technical Support

DHTs that were easy to use and included timely technical support [19,24,30,34,36,46,47,49,50] fostered patient engagement (n=9). Older patients and those with less experience using technology reported that technical support was a facilitator (n=2) [47,48]. Patients valued solutions that were easy to integrate into their daily routines (n=1) [46]. Interventions were more easily adopted when they were culturally tailored for specific target populations (n=1) [34].

Improved Patient-Provider Communication

Improved communication with providers was a facilitator of adoption for patients. Some patients reported that DHTs enabled direct contact with their providers to share their health data and receive feedback [19,20,28,37,46,49-51]. Data sharing via DHTs helped patients better understand their care plans and promoted shared decision making [46]. DHTs improved visit preparation and accuracy of patient-provided information [28].

Improved Self-Management and Patient Experience

Patients were more likely to adopt DHTs that increased their motivation to manage their own conditions (n=2) [36,39]. Patients reported that being able to access and view their health data from their own device encouraged them to be more proactive about their health (n=6) [21,24,30,33,36,46]. Several studies reported greater patient satisfaction using DHTs for hypertension management (n=6) [19,36,37,47,48,52]. Some patients found that using DHTs to monitor their BP readings helped alleviate health-related anxiety (n=2) [19,52].

Reduction of Office Visits

The opportunity for patients to potentially avoid having to travel to the physician’s office was reported as a facilitator of DHT adoption by patients in some studies (n=7) [19,21,24,25,37,41,52].

Barriers for Digital Health Adoption

Provider Factors
Lack of Integration With Clinical Workflow

Several studies reported the lack of integration of technology with clinical workflow as a major barrier to DHT adoption (n=6) [21,24,25,39-41]. The lack of care team resources available to successfully implement DHTs and perform additional tasks was highlighted by multiple studies (n=3) [19,20,24]. Too many additional tasks associated with implementing DHTs were reported to be problematic for several providers (n=1) [9].

Lack of Validation of Technology

Some providers cited concerns over accuracy of data as a potential road block to using home BP monitors on a wider scale (n=4) [14,42-44]. Another barrier to provider adoption was the lack of evidence or proof that DHTs improved patient outcomes (n=3) [32,38,45].

Concern Over Data Privacy and Security

One study reported that the lack of assurance of patient data security was a big concern for providers as well (n=1) [32].

Lack of Technology Usability and Technical Support

Another barrier frequently highlighted in the literature was the complexity of technologies (n=5) [30,39,40,43,46]. Frequent technical issues coupled with inadequate onsite support to resolve them were cited as reasons for discontinuing engagement with DHTs (n=2) [34,39]. Furthermore, the learning curve associated with new DHTs made it difficult for providers to balance the use of a new system and keep up with their daily clinical routine (n=1) [38].

Lack of Organizational Support

Organizational factors, such as lack of leadership support for integrating technology in practice and budget constraints, delayed implementation of new DHTs (n=2) [32,40]. Hospital budgets were too constrained to gather additional resources necessary to implement DHTs as part of the clinical practice workflow (n=1) [32].

Increased Patient Anxiety

One study reported that providers were concerned that patients may be more anxious if they continuously monitored their BP data and believed excess data could be more harmful than useful for the patients (n=1) [14].

Patient Factors
Lack of Technology Usability and Technical Support

Technical issues such as password access, connectivity, and usability prevented patients from using DHTs (n=5) [14,19,20,30,48]. Patients often preferred DHTs that were easy to use regardless of technical skills and abilities and were less time consuming (n=2) [47,48]. Patients with impaired vision, low dexterity, and chronic conditions had difficulties adopting DHTs into their routine (n=3) [14,20,48].

Interference With Patient-Provider Relationship

Patients expressed concerns that using DHTs would interfere with their current in-person relationship with their providers (n=2) [19,37]. Another barrier that patients experienced was the lack of timely feedback from the provider when using DHTs with a provider-facing portal (n=2) [42,47]. In some cases, DHTs were viewed as an impediment to patients’ feelings of independence as they were forced to share data with providers they may not want to (n=2) [19,20].

Increased Patient Anxiety

Some patients experienced anxiety from using DHTs (n=2) [49,52]. This anxiety stemmed from checking their BP too often and being unable to contact their provider directly and obtain timely feedback (n=2) [49,52].

Concern Over Data Privacy and Security

Patients were comfortable with access to health data being limited to only themselves and their providers. However, patients were concerned about the privacy of data shared via DHTs and were uncomfortable with the risk of a third party accessing their data [48].

Lack of Validation of Technology

In some studies, patients questioned the accuracy of the measurements and data recorded (BP readings) by DHTs [19,43,47].

Cost of Digital Health Equipment

The cost of digital health equipment was also cited as a barrier to adoption [42,47]. Some patients also expressed concern over being liable for cost of damage to the equipment [47].

Principal Findings

This review contributes to existing literature by highlighting factors that enable or hinder the adoption of digital health solutions from the perspectives of both providers and patients. These results show that the key facilitators of DHT adoption by physicians include integration with clinical workflow 33% (8/24), ease of use 21% (5/24), improvement in patient outcomes 21% (5/24), financial factors 8% (2/20), and organizational support 4% (1/20). Technology usability and technical support 29% (10/35), positive impact on well-being and self-management 26% (9/35), improved patient-provider relationship 24% (8/35), and a reduction of in-office visits 20% (7/35) were most frequently reported facilitators for patients. The most frequently reported barriers to use of DHTs reported by physicians include lack of integration with workflow 36% (10/28), lack of validation of technology 25% (7/28), and lack of usability and support 25% (7/28). Finally, a lack of technology usability 41% (9/22), interference with the patient-provider relationship 23% (5/22), and lack of validation of technology 14% (3/22) were the top barriers reported by patients.

Although these findings highlight some common themes reported in previous work, there are several key differences and contributions from this study. A 2017 study by Mileski et al, examining the facilitators and barriers to implementing telemedicine for hypertension management [13], only focused on telemedicine, whereas our study examined all DHTs from the perspective of both patients and providers. Consistent with Mileski et al, we found that improved outcomes, increased patient knowledge and self-management, and cost savings were important facilitators of DHT adoption. Another systematic literature review by Gagnon et al [15] evaluated the factors influencing adoption of DHTs by health care professionals and some barriers reported in this review, such as the lack of organizational support and lack of reimbursement for providers, these were consistent with our study findings. Furthermore, most of the studies included in the review by Gagnon et al were conducted in large hospitals. In contrast, most studies in our review, 86% (31 out of 36 studies), were conducted in primary care settings. Additionally, Gagnon et al [15] examined DHTs across multiple diseases, whereas our review focused specifically on DHTs for hypertension management.

Multiple conceptual models exist to describe acceptance and usage of technology, such as Rogers diffusion of innovations theory [54], the technology acceptance model [55], and the unified theory of acceptance and use of technology (UTAUT) [56]. These models have been applied to describe the adoption of electronic health records and other forms of DHTs [57]. As a thematic analysis approach was used to identify new or emergent themes, we neither tied our analysis to a preexisting conceptual model nor sought to validate a preexisting conceptual model. However, it is worth noting that the themes that emerged from our analysis align with several of the constructs described in UTAUT. For example, the themes of clinical workflow integration and technology usability relate to the UTAUT construct of effort expectancy. Similarly, the theme of improvement in patient outcomes relates to the UTAUT construct of performance expectancy.

Future Implications

Lack of usability or ease of use was found to be a major barrier for both patients and providers in our review. Furthermore, lack of integration with clinical workflow was an important barrier for physicians. In the light of these findings, it is important that developers of DHTs should aim to improve the experience of both patients and providers through human-centered design thinking principles [58]. Such a process considers the needs and perspectives of all stakeholders during the product development cycle and implementation in a health care setting. With the right design, providers can interact with DHTs more easily to gain valuable insights on their patients’ health, without compromising their existing workflow. In addition, successful implementation of DHTs in the clinical setting demands time and resources; new programs deploying DHTs should assess all the additional resources required for managing and coordinating care of patients to reduce the burden on providers.

Furthermore, providers often require hospital leadership to be supportive of a culture of innovation within their organization while weighing risks and benefits to patients and providers [38]. Therefore, organizational commitment to engaging providers at an early stage of DHT implementation by evaluating provider needs, identifying provider champions for implementing DHTs, and providing adequate training in the hospitals are critical to foster adoption.

Although not a prominent theme in this review, some studies show that the current health care policy and regulatory landscape are increasing pressure on health care organizations to provide lower-cost and higher-quality health care [59,60]. With tightening health care budgets, identifying long-term return on investment (ROI) on DHTs and establishing financial incentives through a clear reimbursement policy for providers are vital factors in increasing provider adoption. Therefore, future studies should incorporate discussions of implementation costs and ROI, in addition to examining clinical outcomes seen as a result of DHTs.


First, as technology and policy are evolving at a rapid pace, certain barriers and facilitators that were identified in older articles may be less relevant today. Nevertheless, some facilitators and barriers are likely to remain constant over time, such as the critical importance of integration of DHTs into clinical workflow and technology usability. Second, reporting barriers and facilitators was not the primary aim of some of the studies included in this review. Thus, a portion of the data was collected from impressions reported in discussion sections of the published studies, which includes interpretations and speculations made by the researchers involved in the studies. Finally, some of the studies included in this review provided little context on barriers and facilitators reported. In such instances, reviewers used their best judgement to determine whether the barriers or facilitators reported were best categorized as provider- or patient-related barriers or facilitators. Regardless of limitations, the themes in this review provide comprehensive evidence that could better inform and strengthen DHT development and implementation.


Our findings suggest that DHT adoption for hypertension is influenced by several important factors such as integration into the clinical workflow, usability, improvements in patient outcomes, and positive impact on the patient-provider relationship. Real-world testing and incorporating feedback from both patients and providers in designing technologies will improve their overall usability. Finally, to fully realize the potential of digitally enabled hypertension management, there is a greater need to validate these technologies to provide patients and providers with reliable and accurate information on both clinical outcomes and cost effectiveness.


This research was the result of a collaboration between the AMA and Partners HealthCare Pivot Labs. The research was funded by the AMA. The authors would like to sincerely thank all the members of the steering committee—Dr Joseph Kvedar, Michael Hodgkins, Meg Barron, Dr Michael Rakotz, and Christopher Khoury—who guided them through the process of this review and provided their valuable feedback.

Authors' Contributions

RP, NF, and AC were the primary reviewers and they conducted the thematic analysis in addition to writing the paper. RP, SA, KK, and KJ developed the review plan, process, and methodology. SA, KK, JF, CK, SL, and KJ contributed to the interpretation of results, manuscript review and editing, and provided advice and guidance throughout the review process and manuscript preparation.

Conflicts of Interest

None declared.

Multimedia Appendix 1

PubMed Strategy.

PDF File (Adobe PDF File), 33KB

Multimedia Appendix 2

Summary of characteristics and authors’ analysis of the 36 studies included in the review.

PDF File (Adobe PDF File), 23KB

  1. Morton K, Dennison L, May C, Murray E, Little P, McManus RJ, et al. Using digital interventions for self-management of chronic physical health conditions: A meta-ethnography review of published studies. Patient Educ Couns 2017 Dec;100(4):616-635 [FREE Full text] [CrossRef] [Medline]
  2. Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med 2014;12(6):573-576 [FREE Full text] [CrossRef] [Medline]
  3. American Medical Association. 2016. Digital Health Study: Physicians’ motivations and requirements for adopting digital clinical tools   URL: [accessed 2019-02-19] [WebCite Cache]
  4. Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, American Heart Association Council on EpidemiologyPrevention Statistics CommitteeStroke Statistics Subcommittee. Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation 2018 Mar 20;137(12):e67-e492. [CrossRef] [Medline]
  5. Merai R, Siegel C, Rakotz M, Basch P, Wright J, Wong B, DHSc, et al. CDC grand rounds: a public health approach to detect and control hypertension. MMWR Morb Mortal Wkly Rep 2016 Nov 18;65(45):1261-1264 [FREE Full text] [CrossRef] [Medline]
  6. Serumaga B, Ross-Degnan D, Avery AJ, Elliott RA, Majumdar SR, Zhang F, et al. Effect of pay for performance on the management and outcomes of hypertension in the United Kingdom: interrupted time series study. Br Med J 2011 Jan 25;342:d108 [FREE Full text] [CrossRef] [Medline]
  7. Osterberg L, Blaschke T. Adherence to medication. N Engl J Med 2005 Aug 04;353(5):487-497. [CrossRef] [Medline]
  8. Okonofua EC, Simpson KN, Jesri A, Rehman SU, Durkalski VL, Egan BM. Therapeutic inertia is an impediment to achieving the Healthy People 2010 blood pressure control goals. Hypertension 2006 Mar;47(3):345-351. [CrossRef] [Medline]
  9. McKinstry B, Hanley J, Wild S, Pagliari C, Paterson M, Lewis S, et al. Telemonitoring based service redesign for the management of uncontrolled hypertension: multicentre randomised controlled trial. Br Med J 2013 May 24;346:f3030 [FREE Full text] [CrossRef] [Medline]
  10. Pickering TG, Miller NH, Ogedegbe G, Krakoff LR, Artinian NT, Goff D, American Heart Association, American Society of Hypertension, Preventive Cardiovascular Nurses Association. Call to action on use and reimbursement for home blood pressure monitoring: executive summary: a joint scientific statement from the American Heart Association, American Society Of Hypertension, and Preventive Cardiovascular Nurses Association. Hypertension 2008 Jul;52(1):1-9. [CrossRef] [Medline]
  11. Ward AM, Takahashi O, Stevens R, Heneghan C. Home measurement of blood pressure and cardiovascular disease: systematic review and meta-analysis of prospective studies. J Hypertens 2012 Mar;30(3):449-456. [CrossRef] [Medline]
  12. Minetaki K, Akematsu Y, Tsuji M. Effect of e-health on medical expenditures of outpatients with lifestyle-related diseases. Telemed J E Health 2011 Oct;17(8):591-595. [CrossRef] [Medline]
  13. Mileski M, Kruse CS, Catalani J, Haderer T. Adopting telemedicine for the self-management of hypertension: systematic review. JMIR Med Inform 2017 Oct 24;5(4):e41 [FREE Full text] [CrossRef] [Medline]
  14. Logan AG, Dunai A, McIsaac WJ, Irvine MJ, Tisler A. Attitudes of primary care physicians and their patients about home blood pressure monitoring in Ontario. J Hypertens 2008 Mar;26(3):446-452. [CrossRef] [Medline]
  15. Gagnon M, Desmartis M, Labrecque M, Car J, Pagliari C, Pluye P, et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J Med Syst 2012 Feb;36(1):241-277 [FREE Full text] [CrossRef] [Medline]
  16. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol 2005 Feb;8(1):19-32. [CrossRef]
  17. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977 Mar;33(1):159-174. [Medline]
  18. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006 Jan;3(2):77-101. [CrossRef]
  19. Grant RW, Pandiscio JC, Pajolek H, Woulfe A, Pelletier A, Kvedar J, et al. Implementation of a web-based tool for patient medication self-management: the Medication Self-titration Evaluation Programme (Med-STEP) for blood pressure control. Inform Prim Care 2012;20(1):57-67 [FREE Full text] [Medline]
  20. Eklind H. A hub clinician’s perspective on supported self-care through technology. Prim Health Care 2017 Jan 27;27(1):20-25. [CrossRef]
  21. North F, Elrashidi MY, Ward WJ, Takahashi PY, Ebbert JO, Ytterberg KL, et al. Telemonitoring blood pressure by secure message on a patient portal: use, content, and outcomes. Telemed J E Health 2015 Aug;21(8):630-636. [CrossRef] [Medline]
  22. Ideal Life. White paper: Hypertension   URL: [accessed 2019-02-19] [WebCite Cache]
  23. Neumann CL, Menne J, Rieken EM, Fischer N, Weber MH, Haller H, et al. Blood pressure telemonitoring is useful to achieve blood pressure control in inadequately treated patients with arterial hypertension. J Hum Hypertens 2011 Dec;25(12):732-738. [CrossRef] [Medline]
  24. Hanley J, Ure J, Pagliari C, Sheikh A, McKinstry B. Experiences of patients and professionals participating in the HITS home blood pressure telemonitoring trial: a qualitative study. BMJ Open 2013 May 28;3(5) [FREE Full text] [CrossRef] [Medline]
  25. Mann M, Qiu M. Fraser Street Medical. 2015. Can we improvide myocardial protection during ischemic injury? Addressing hypertension care gaps by implementing an evidence based electronic medical record (EMR) hypertension dashboard   URL: [WebCite Cache]
  26. Crowley MJ, Smith VA, Olsen MK, Danus S, Oddone EZ, Bosworth HB, et al. Treatment intensification in a hypertension telemanagement trial: clinical inertia or good clinical judgment? Hypertension 2011 Oct;58(4):552-558 [FREE Full text] [CrossRef] [Medline]
  27. Wang V, Smith VA, Bosworth HB, Oddone EZ, Olsen MK, McCant F, et al. Economic evaluation of telephone self-management interventions for blood pressure control. Am Heart J 2012 Jun;163(6):980-986. [CrossRef] [Medline]
  28. Wald JS, Businger A, Gandhi TK, Grant RW, Poon EG, Schnipper JL, et al. Implementing practice-linked pre-visit electronic journals in primary care: patient and physician use and satisfaction. J Am Med Inform Assoc 2010;17(5):502-506 [FREE Full text] [CrossRef] [Medline]
  29. Shelley D, Tseng TY, Matthews AG, Wu D, Ferrari P, Cohen A, et al. Technology-driven intervention to improve hypertension outcomes in community health centers. Am J Manag Care 2011 Dec;17(12 Spec No):SP103-SP110 [FREE Full text] [Medline]
  30. Varis J, Karjalainen S, Korhonen K, Viigimaa M, Port K, Kantola I. Experiences of telemedicine-aided hypertension control in the follow-up of Finnish hypertensive patients. Telemed J E Health 2009 Oct;15(8):764-769. [CrossRef] [Medline]
  31. Kim YN, Shin DG, Park S, Lee CH. Randomized clinical trial to assess the effectiveness of remote patient monitoring and physician care in reducing office blood pressure. Hypertens Res 2015 Jul;38(7):491-497. [CrossRef] [Medline]
  32. Sublet ML, Courand PY, Bally S, Krummel T, Dimitrov Y, Brucker M, et al. 3C.07 Are the physicians reluctant to practice telemedicine in hypertension? J Hypertens 2015 Jun;33:e38-e39. [CrossRef]
  33. Frias J, Virdi N, Raja P, Kim Y, Savage G, Unger J, et al. Evaluation of a digital health offering to optimize blood pressure and lipid control in patients with uncontrolled hypertension and type 2 diabetes. Circulation 2016 Nov;134(Suppl_1):E [FREE Full text]
  34. Kressin N, Long J, Glickman M, Bokhour B, Orner M, Clark C, et al. A brief, multifaceted, generic intervention to improve blood pressure control and reduce disparities had little effect. Ethn Dis 2016 Dec 21;26(1):27-36 [FREE Full text] [CrossRef] [Medline]
  35. Nilsson M, Rasmark U, Nordgren H, Hallberg P, Skönevik J, Westman G, et al. The physician at a distance: the use of videoconferencing in the treatment of patients with hypertension. J Telemed Telecare 2009;15(8):397-403. [CrossRef] [Medline]
  36. Bernocchi P, Scalvini S, Bertacchini F, Rivadossi F, Muiesan M. Home based telemedicine intervention for patients with uncontrolled hypertension--a real life non-randomized study. BMC Med Inform Decis Mak 2014 Jun 12;14:52 [FREE Full text] [CrossRef] [Medline]
  37. Wakefield BJ, Koopman RJ, Keplinger LE, Bomar M, Bernt B, Johanning JL, et al. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes. Telemed J E Health 2014 Mar;20(3):199-205 [FREE Full text] [CrossRef] [Medline]
  38. Millery M, Shelley D, Wu D, Ferrari P, Tseng T, Kopal H. Qualitative evaluation to explain success of multifaceted technology-driven hypertension intervention. Am J Manag Care 2011 Dec;17(12 Spec No):SP95-S102 [FREE Full text] [Medline]
  39. Bosworth HB, Olsen MK, Dudley T, Orr M, Goldstein MK, Datta SK, et al. Patient education and provider decision support to control blood pressure in primary care: a cluster randomized trial. Am Heart J 2009 Mar;157(3):450-456. [CrossRef] [Medline]
  40. Vedanthan R, Blank E, Tuikong N, Kamano J, Misoi L, Tulienge D, et al. Usability and feasibility of a tablet-based Decision-Support and Integrated Record-keeping (DESIRE) tool in the nurse management of hypertension in rural western Kenya. Int J Med Inform 2015 Mar;84(3):207-219 [FREE Full text] [CrossRef] [Medline]
  41. Shaw RJ, Kaufman MA, Bosworth HB, Weiner BJ, Zullig LL, Lee SD, et al. Organizational factors associated with readiness to implement and translate a primary care based telemedicine behavioral program to improve blood pressure control: the HTN-IMPROVE study. Implement Sci 2013 Sep 08;8:106 [FREE Full text] [CrossRef] [Medline]
  42. DeAlleaume L, Parnes B, Zittleman L, Sutter C, Chavez R, Bernstein J, et al. Success in the Achieving CARdiovascular Excellence in Colorado (A CARE) home blood pressure monitoring program: a report from the Shared Networks of Colorado Ambulatory Practices and Partners (SNOCAP). J Am Board Fam Med 2015;28(5):548-555 [FREE Full text] [CrossRef] [Medline]
  43. Kim H, Ahn ME, Choi YA, Choi EH, Kim DW, Shin SG, et al. Fifteen-year experience with telemedicine services in Gangwon province in Korea. Healthc Inform Res 2015 Oct;21(4):283-291 [FREE Full text] [CrossRef] [Medline]
  44. O'Connor PJ, Magid D, Sperl-Hillen J, Price D, Asche S, Rush W, et al. Personalised physician learning intervention to improve hypertension and lipid control: randomised trial comparing two methods of physician profiling. BMJ Qual Saf 2014 Dec;23(12):1014-1022 [FREE Full text] [CrossRef] [Medline]
  45. Holbrook A, Pullenayegum E, Thabane L, Troyan S, Foster G, Keshavjee K, et al. Shared electronic vascular risk decision support in primary care: computerization of Medical Practices for the Enhancement of Therapeutic Effectiveness (COMPETE III) randomized trial. Arch Intern Med 2011 Oct 24;171(19):1736-1744. [CrossRef] [Medline]
  46. Frias J, Virdi N, Raja P, Kim Y, Savage G, Osterberg L. Effectiveness of digital medicines to improve clinical outcomes in patients with uncontrolled hypertension and type 2 diabetes: prospective, open-label, cluster-randomized pilot clinical trial. J Med Internet Res 2017 Dec 11;19(7):e246 [FREE Full text] [CrossRef] [Medline]
  47. Abdullah A, Liew SM, Hanafi NS, Ng CJ, Lai PS, Chia YC, et al. What influences patients' acceptance of a blood pressure telemonitoring service in primary care? A qualitative study. Patient Prefer Adherence 2016;10:99-106 [FREE Full text] [CrossRef] [Medline]
  48. Czaja SJ, Lee CC, Arana N, Nair SN, Sharit J. Use of a telehealth system by older adults with hypertension. J Telemed Telecare 2014 Jun;20(4):184-191 [FREE Full text] [CrossRef] [Medline]
  49. Chen MJ, Chen KY, Chiang SJ, Lee JS, Ernest, Yu W, et al. E-010 the community-based telehealth care model for hypertension in Taipei. J Hypertens 2011;29:e12. [CrossRef]
  50. Cottrell E, McMillan K, Chambers R. A cross-sectional survey and service evaluation of simple telehealth in primary care: what do patients think? BMJ Open 2012;2(6) [FREE Full text] [CrossRef] [Medline]
  51. Bodenheimer T, Chen E, Bennett HD. Confronting the growing burden of chronic disease: can the US health care workforce do the job? Health Aff (Millwood) 2009;28(1):64-74. [CrossRef] [Medline]
  52. Huff LS, Zittleman L, DeAlleaume L, Bernstein J, Chavez R, Sutte C, et al. What keeps patients from adhering to a home blood pressure program? J Am Board Fam Med 2011;24(4):370-379 [FREE Full text] [CrossRef] [Medline]
  53. Watson AJ, Singh K, Myint- U, Grant RW, Jethwani K, Murachver E, et al. Evaluating a web-based self-management program for employees with hypertension and prehypertension: a randomized clinical trial. Am Heart J 2012 Oct;164(4):625-631. [CrossRef] [Medline]
  54. Rogers EM. Diffusion of Innovations. New York: Free Press; 1995.
  55. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Q 2003 Dec;27(3):425-427 [FREE Full text] [CrossRef]
  56. Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int J Med Inform 2017 Dec;101:75-84. [CrossRef] [Medline]
  57. Holden RJ, Karsh BT. The technology acceptance model: its past and its future in health care. J Biomed Inform 2010 Feb;43(1):159-172 [FREE Full text] [CrossRef] [Medline]
  58. van Gemert-Pijnen L, Kelders SM, Kipp H, Sanderman R. EHealth Research, Theory and Development: A Multi-Disciplinary Approach. Abingdon, UK: Routledge; 2018.
  59. Schur CL, Sutton JP. Physicians in medicare ACOs offer mixed views of model for health care cost and quality. Health Aff (Millwood) 2017 Dec 01;36(4):649-654. [CrossRef] [Medline]
  60. Ryan AM, Krinsky S, Maurer KA, Dimick JB. Changes in hospital quality associated with hospital value-based purchasing. N Engl J Med 2017 Dec 15;376(24):2358-2366 [FREE Full text] [CrossRef] [Medline]

AMA: American Medical Association
BP: blood pressure
CINAHL: Cumulative Index to Nursing and Allied Health Literature
DHT: digital health technology
EMBASE: Excerpta Medica database
MeSH: medical subject heading
RCT: randomized controlled trial
ROI: return on investment
UTAUT: unified theory of acceptance and use of technology

Edited by N Bruining; submitted 20.08.18; peer-reviewed by Y Hendriks, X Shen; comments to author 19.10.18; revised version received 13.12.18; accepted 16.01.19; published 26.03.19


©Ramya Sita Palacholla, Nils Fischer, Amanda Coleman, Stephen Agboola, Katherine Kirley, Jennifer Felsted, Chelsea Katz, Stacy Lloyd, Kamal Jethwani. Originally published in JMIR Cardio (, 26.03.2019.

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