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The increasing integration of digital health tools into care may result in a greater flow of personal health information (PHI) between patients and providers. Although privacy legislation governs how entities may collect, use, or share PHI, such legislation has not kept pace with digital health innovations, resulting in a lack of guidance on implementing meaningful consent. Understanding patient perspectives when implementing meaningful consent is critical to ensure that it meets their needs. Consent for research in the context of digital health is limited.
This state-of-the-art review aimed to understand the current state of research as it relates to patient perspectives on digital health consent. Its objectives were to explore what is known about the patient perspective and experience with digital health consent and provide recommendations on designing and implementing digital health consent based on the findings.
A structured literature search was developed and deployed in 4 electronic databases—MEDLINE, IEEE Xplore, Scopus, and Web of Science—for articles published after January 2010. The initial literature search was conducted in March 2021 and updated in March 2022. Articles were eligible for inclusion if they discussed electronic consent or consent, focused on the patient perspective or preference, and were related to digital health or digital PHI. Data were extracted using an extraction template and analyzed using qualitative content analysis.
In total, 75 articles were included for analysis. Most studies were published within the last 5 years (58/75, 77%) and conducted in a clinical care context (33/75, 44%) and in the United States (48/75, 64%). Most studies aimed to understand participants’ willingness to share PHI (25/75, 33%) and participants’ perceived usability and comprehension of an electronic consent notice (25/75, 33%). More than half (40/75, 53%) of the studies did not describe the type of consent model used. The broad open consent model was the most explored (11/75, 15%). Of the 75 studies, 68 (91%) found that participants were willing to provide consent; however, their consent behaviors and preferences were context-dependent. Common patient consent requirements included clear and digestible information detailing who can access PHI, for what purpose their PHI will be used, and how privacy will be ensured.
There is growing interest in understanding the patient perspective on digital health consent in the context of providing clinical care. There is evidence suggesting that many patients are willing to consent for various purposes, especially when there is greater transparency on how the PHI is used and oversight mechanisms are in place. Providing this transparency is critical for fostering trust in digital health tools and the innovative uses of data to optimize health and system outcomes.
Digital health refers to the use of IT, services, and processes to support health care delivery [
The growing importance of and interest in integrating AI and other digital health tools into care raises questions on how to protect patient privacy. Although the public is supportive of investments in these technologies [
Patient willingness to share PHI to support advancements in health care is improved when there is transparency, especially when the information regarding the collection, use, and disclosure of their PHI is clearly stated [
State-of-the-art reviews are intended to summarize emerging trends and synthesize insights from the most current literature [
A structured literature search was developed and deployed in 4 electronic databases: MEDLINE, IEEE Xplore, Scopus, and Web of Science. The initial literature search was conducted in March 2021 and updated in March 2022. Primary peer-reviewed articles published after January 2010 were included in the search. The search strategy was first developed using Medical Subject Headings and keywords in MEDLINE and then translated to the other databases. Key search terms used in each database included
The search results were uploaded to Covidence, a literature screening and data extraction tool. In total, 2 reviewers (NS and IK) piloted the extraction tool with the first 50 articles and independently screened the titles and abstracts for relevancy afterward. Any discrepancies or conflicts were discussed and resolved. A total of 3 reviewers (IK, DI, and HB) independently assessed the full text of all relevant articles. Articles were flagged for discussion if it was unclear whether they met the inclusion criteria. In total, 2 authors (IK and JK) repeated these steps when updating the search.
Articles were included if they met all the following criteria: (1) described an eConsent or consent process, design, or development; (2) focused on patients’ perspectives, preferences, acceptance, or behaviors; (3) were set in a digital health or health IT context; and (4) described the use of digital health or health IT or digital PHI for health care delivery, health research or analytics, or consumer use. Study eligibility was not limited by study design, thereby allowing for the inclusion of various study designs. Only studies published in English were eligible for inclusion. Studies that did not meet the inclusion criteria were excluded from the review.
A standardized data extraction form was developed on REDCap (Research Electronic Data Capture; Vanderbilt University), a secure software database for storing research data [
The extracted study characteristics included study title, year of publication, country of origin, sample size, sample source (eg, research, biobank or patient participant, and national or regional sample), study context or setting, study method, and study design and objectives. Data on consent models were extracted and categorized based on the seminal eConsent model framework by Coeira and Clarke [
A qualitative content analysis [
A detailed overview of the included studies is presented in
The search strategy yielded 3133 unique citations. Following the screening process, 2.39% (75/3133) of the articles were eligible for extraction. The flowchart of the search selection process can be found in
Article selection flowchart. HIT: health IT.
Of the 75 studies included in this review, 58 (77%) were published within the last 5 years (between 2017 and 2022), and 48 (64%) were conducted in the United States. Most studies were conducted within a clinical care (33/75, 44%) or research context (29/75, 39%) where the study sample of focus was primarily research, biobank, and patient participants (54/75, 72%). Nearly half of the studies (36/75, 48%) used quantitative research methods, commonly using cross-sectional surveys (35/75, 47%) to collect data. The primary purpose of most studies was to understand participants’ willingness to share PHI (25/75, 33%) and participants’ perceived usability and comprehension of an eConsent platform (25/75, 33%). More than half of the studies (39/75, 52%) focused on participants’ hypothetical views on digital health consent. A total of 21% (16/75) of the studies described the design or development of an eConsent platform. In total, 27% (20/75) of the studies described the implementation, usability, or evaluation of an existing eConsent platform. Further details about the study characteristics are shown in
Study characteristics (N=75).
Characteristic | Studies, n (%) | |
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2010-2013 | 4 (5) |
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2014-2016 | 13 (17) |
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2017-2019 | 26 (35) |
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2020-2022 | 32 (43) |
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United States | 48 (64) |
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England | 6 (8) |
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Ireland | 3 (4) |
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Sweden | 3 (4) |
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Germany | 3 (4) |
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Othera | 12 (16) |
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Clinical care | 33 (44) |
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Research | 29 (39) |
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Consumer innovations | 13 (17) |
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Willingness to share PHIb | 25 (33) |
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Usability and user comprehension | 25 (33) |
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Willingness to participate | 12 (16) |
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Consent information needs | 6 (8) |
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eConsentc design and implementation | 7 (9) |
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Quantitative | 36 (48) |
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Mixed methods | 23 (31) |
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Qualitative | 14 (19) |
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Multimethods | 2 (3) |
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Cross-sectional survey | 35 (47) |
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Focus groups | 11 (15) |
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Randomized controlled trial | 7 (9) |
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Interviews | 4 (5) |
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Multiple methods | 12 (16) |
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Other | 6 (8) |
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Research, biobank, or patient | 54 (72) |
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General populationd | 19 (25) |
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Knowledge users | 2 (3) |
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No | 57 (76) |
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Yese | 18 (24) |
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Yes, they are developing one | 16 (21) |
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Yes, there is one that exists | 20 (27) |
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None used | 39 (52) |
aCountry of origin: countries categorized as
bPHI: personal health information.
ceConsent: electronic consent.
dGeneral population can be further divided into studies that focus on a national population (8/75, 11%) or on a regional-, provincial-, or state-level population (11/75, 15%).
eSample subgroup analysis:
Spectrum of consent models reported (N=75).
Type of consent | Frequency, n (%) |
Broad open—existing data and PHIa do not require additional consent for use | 11 (15) |
Broad controlled—if consent is provided, data will only be used by approved investigators | 4 (5) |
Broad tiered, menu, or meta—consent process allows participants to select the types of research for which their PHI can be used | 3 (4) |
Dynamic consent—consent process allows participants to set and change their consent preferences through a secure platform | 8 (11) |
General denial—consent is required by participants on a per-use basis | 2 (3) |
Multiple consent models reported | 7 (9) |
Consent model not reported | 40 (53) |
aPHI: personal health information.
In total, 9% (7/75) of the studies compared various consent models to understand whether a specific model increased participants’ comprehension of what they were consenting to or made them more willing to consent. Within these studies, patient consent model preferences varied; however, in most studies (5/7, 71%), participants preferred granular, informative, and transparent consent choices [
The remaining 29% (2/7) of the studies found that participants preferred
In total, 91% (68/75) of the studies described the patient perspective or experience with digital health consent. These studies were further categorized according to their primary purpose (as described in
In nearly half (15/31, 48%) of the studies, comprehension was assessed by comparing how different consent media such as eConsent or paper-based consent affected participants’ understanding of the consent information. A total of 73% (11/15) of the comprehension studies found that user comprehension improved when an eConsent medium was used. Participants in a randomized controlled study reported a greater understanding of most aspects of the consent notice in an eConsent platform than in a paper-based consent form [
Improvements in user comprehension of consent notices were attributed to the overall user satisfaction with and usability of eConsent systems. These systems were described as easy to use, well organized, and more engaging [
Among 19% (6/31) of the studies, mixed results were identified when assessing the amount of information and information elements that individuals require when reviewing consent notices. Beskow et al [
In total, 18% (12/68) of the studies described participants’ willingness to consent to take part in an initiative or intervention (eg, research, clinical care, consumer digital health innovation, and biobank). Of the 12 studies, 5 (42%) found that most participants were willing to consent to participate. However, willingness to consent was contingent on several factors. Most often, willingness to consent depended on who their PHI would be shared with, where many participants were less trusting of entities outside their circle of care. For instance, the more trusting the participants were in their health care provider, the less control they required over their PHI [
Participant concerns related to the privacy of their PHI and the perceived sensitivity of their PHI also hindered their willingness to participate. In a focus group study [
Participation in the aforementioned initiatives was dependent on a variety of sociodemographic factors. A total of 42% (5/12) of the studies examined the effect of sociodemographic characteristics on willingness to consent to participate, finding that age [
A total of 37% (25/68) of the studies explored participants’ willingness to consent to sharing their PHI for clinical care, research, biobanks, precision medicine initiatives, and consumer innovations. Generally, study participants were willing to share their PHI under certain conditions. For example, participants expressed greater comfort and willingness to share their PHI with health care providers, academic researchers, and not-for-profit organizations [
A lack of information and transparency surrounding PHI-handling practices hindered participants’ willingness to provide consent. Unwillingness was most often attributed to a lack of information on the anonymization, aggregation, or deidentification of PHI [
Several antecedents that either supported or hindered consent decisions were identified in the studies. Specifically, past health care and privacy experiences and health care perceptions influenced willingness to consent. Weidman et al [
Across the studies, health care perceptions and consent decisions were often driven by altruistic beliefs [
It was also found that expectations regarding consent varied with sociodemographic factors and digital literacy. A UK study found that racialized participants with less education and lower digital literacy were more likely to prefer to be asked for explicit consent before their deidentified health records were accessed [
Consent in digital health is contextually driven such that it is often dependent on who is using or accessing one’s PHI, how their PHI will be used, and for what purpose. The findings of this review underscore the context dependency of consent as there were mixed results on patient perspectives on consent models and willingness to share their PHI. For instance, broad consent models may be acceptable in specific study contexts. In contrast, consent models that provided patients with more control were favored in others (ie, broad tiered, menu, or meta and dynamic consent). Similarly, patient willingness to share their PHI, consent behaviors, perceptions, and preferences varied by study. Given this variance, enabling individuals to make informed choices based on their contexts is critical. At the most rudimentary level, individuals require specific and easily comprehensible information on who their PHI is being shared with, for what purpose their PHI will be used, and how the privacy and security of their PHI will be ensured [
Trust is central to individual consent behaviors, preferences, and perceptions. Willingness to consent often depended on the entity collecting PHI, where most individuals were comfortable sharing PHI with their health care providers, health care organizations, and academic researchers. Comfort in sharing PHI declined with recipients outside the individual’s circle of care, particularly with commercial or for-profit entities. There is a growing body of evidence highlighting individuals’ significant discomfort in sharing their PHI with commercial and for-profit entities, primarily because of a lack of trust in these entities [
In contrast, those with poor health care experiences and awareness of commercial entities misusing PHI were less willing to share their PHI [
Although many individuals were concerned about the potential risks of consenting and the confidentiality of their PHI, in several studies, the benefits outweighed the potential risks [
The need to modernize consent processes for the digital age is widely recognized as legislation has not kept pace with the rapidly evolving digital health environment [
Interestingly, this review also found that the type of consent model may have little relevance to participants’ decisions to consent [
In terms of transparency, there was mixed evidence on the best practices for presenting information on consent forms. The mixed evidence is characterized by a dichotomy, where some assert that more detailed information better supports consent decisions [
This review provides insights into patient consent preferences in the digital health context. Given the rapid adoption and integration of digital health technologies in clinical care settings, it is unsurprising that many of the included studies were published within the last 5 years (2017 to 2022; 58/75, 77%). The summative findings of this review present the current state of patient consent preferences and emerging consent practices in the digital health context. Currently, most studies focus on collecting and using electronic PHI and EHR data for biobanks and research initiatives. Few studies (6/75, 8%) focused on understanding patient preferences, behaviors, and perspectives on consent in AI (ie, precision medicine, machine learning, and deep learning). As AI becomes more pervasive in clinical predictions and diagnosis, treatment recommendations and decision support, and consumer health innovations, additional research is needed to explore individuals’ consent preferences and experiences in these contexts.
Consistent with state-of-the-art reviews, this study highlighted gaps in digital health consent research. Although the included studies explored patient or public consent preferences, many (40/75, 53%) failed to clearly outline the type of consent model used. Reporting the consent model is essential as it provides greater context to the study findings, especially concerning individual perceptions. As with privacy research [
When considering how health equity has become increasingly important in health care research [
Finally, the findings of this review highlight important considerations for designing consent in the digital health era. The design of meaningful consent processes must be rooted in co-design approaches, transparent practices, and integrated knowledge translation. As echoed by the OPC Meaningful Consent Guidelines [
There are some limitations to consider, most of which are related to the state-of-the-art review methodology [
Consent is an increasingly important issue in the rapidly evolving digital health ecosystem. Implementing meaningful consent may be a complex endeavor as consent preferences and behaviors will vary based on context; however, this review found that most patients are willing to consent to share their PHI given the right circumstances. Suppose that the desired outcome is to use one’s PHI to develop, sustain, and enhance digital health innovations. In such cases, individuals must be provided with transparent information about the purpose of the collection and use of their PHI and the potential benefits, whether direct or indirect, of consenting to share their PHI. In addition to transparency, information must be customizable, allowing readers to tailor the granularity of detail to their individual needs. By enabling meaningful and informed consent, organizations can foster greater trust in their digital health solutions. Furthermore, to understand how to facilitate meaningful and informed consent in various contexts, patients and the public must be engaged in the design, development, and implementation of consent processes and notices for digital health initiatives. By doing so, consent practices in the digital health context will not simply act as a proxy for choice but will also be able to fulfill the notion of contextual integrity such that they account for individual interests and preferences in specific social contexts.
Literature search strategy.
Overview of the included studies.
artificial intelligence
electronic consent
electronic health record
Office of the Privacy Commissioner
personal health information
Research Electronic Data Capture
This work was supported by Canada Health Infoway, an independent, not-for-profit organization funded by the federal government. NS was supported by the Canadian Institutes of Health Research Health System Impact Fellowship. This program was led by the Canadian Institutes of Health Research Institute of Health Services and Policy Research in partnership with the Centre for Addiction and Mental Health.
None declared.