Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at, first published .
Foundations for Meaningful Consent in Canada’s Digital Health Ecosystem: Retrospective Study

Foundations for Meaningful Consent in Canada’s Digital Health Ecosystem: Retrospective Study

Foundations for Meaningful Consent in Canada’s Digital Health Ecosystem: Retrospective Study

Original Paper

1Centre for Complex Interventions, Centre for Addiction and Mental Health, Toronto, ON, Canada

2Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada

3College of Public Health, University of South Florida, Tampa, FL, United States

4Canada Health Infoway, Toronto, ON, Canada

Corresponding Author:

Nelson Shen, MHA, PhD

Centre for Complex Interventions

Centre for Addiction and Mental Health

60 White Squirrel Way

Toronto, ON, M6J 1H4


Phone: 1 416 535 8501


Background: Canadians are increasingly gaining web-based access to digital health services, and they expect to access their data from these services through a central patient access channel. Implementing data sharing between these services will require patient trust that is fostered through meaningful consent and consent management. Understanding user consent requirements and information needs is necessary for developing a trustworthy and transparent consent management system.

Objective: The objective of this study is to explore consent management preferences and information needs to support meaningful consent.

Methods: A secondary analysis of a national survey was conducted using a retrospective descriptive study design. The 2019 cross-sectional survey used a series of vignettes and consent scenarios to explore Canadians’ privacy perspectives and preferences regarding consent management. Nonparametric tests and logistic regression analyses were conducted to identify the differences and associations between various factors.

Results: Of the 1017 total responses, 716 (70.4%) participants self-identified as potential users. Of the potential users, almost all (672/716, 93.8%) felt that the ability to control their data was important, whereas some (385/716, 53.8%) believed that an all or none control at the data source level was adequate. Most potential users preferred new data sources to be accessible by health care providers (546/716, 76.3%) and delegated parties (389/716, 54.3%) by default. Prior digital health use was associated with greater odds of granting default access when compared with no prior use, with the greatest odds of granting default access to digital health service providers (odds ratio 2.17, 95% CI 1.36-3.46). From a list of 9 information elements found in consent forms, potential users selected an average of 5.64 (SD 2.68) and 5.54 (SD 2.85) items to feel informed in consenting to data access by care partners and commercial digital health service providers, respectively. There was no significant difference in the number of items selected between the 2 scenarios (P>.05); however, there were significant differences (P<.05) in information types that were selected between the scenarios.

Conclusions: A majority of survey participants reported that they would register and use a patient access channel and believed that the ability to control data access was important, especially as it pertains to access by those outside their care. These findings suggest that a broad all or none approach based on data source may be accepted; however, approximately one-fifth of potential users were unable to decide. Although vignettes were used to introduce the questions, this study showed that more context is required for potential users to make informed consent decisions. Understanding their information needs will be critical, as these needs vary with the use case, highlighting the importance of prioritizing and tailoring information to enable meaningful consent.

JMIR Med Inform 2022;10(3):e30986




Canadians are becoming increasingly aware of digital health tools and services to support their health and wellness and are beginning to demand that they have greater access to their data that are held within these tools and services. Those who accessed their health records reported that they were more knowledgeable, informed, and confident about the care they received [1,2]. Although there are benefits to having a wide variety of digital health tools and services available, the rapid growth of the digital health ecosystem has resulted in silos of patient data. The prospect of universally connecting digital health tools, such as patient portals, is a challenge, given the large number of data exchange protocols required to share information between all points in a patient’s journey [3]. Historically, patient portals have been implemented at the organizational level and tethered to their organizational electronic health record (EHR) system. As these portals seldom exchange information between organizations, patients may end up with multiple portals of siloed data based on the various points where they seek care [4]. As a result, many patients have fragmented, limited, or no electronic access to their personal health information (PHI), giving patients an incomplete picture of their overall health to support their health care decisions. Furthermore, the multiplicity of tools and services may provide an additional burden to patients as they will need to manage the different log-ins and privacy preferences for each one.

There are growing patient demands and expectations for web-based access to their consolidated clinical and self-generated data through a single access point, recognizing that it will make their lives better [5]. A patient access channel serves as a trusted access point, granting patients authenticated access to their PHI and digital services data within a single platform. This allows patients to manage the collection, use, and disclosure of their PHI. Patients have the right to control how their information is collected and used, which is the definition of information privacy [6]. Canadian legislative frameworks provide protection and, generally, enable individuals to limit the use and disclosure of their records to certain individuals for specific purposes [7]. Implementing a consent management system would empower users to exercise their data-sharing preferences [8,9].

Privacy Notices and Consent

Canadian legislation also requires consent for the collection, use, and disclosure of personal information and PHI; however, consent is seldom transparent or informed, leaving patients unaware of how their data are used and with minimal control over their data [10]. Given the largely unregulated commercial digital health ecosystem, digital health services are founded in a business model where user data are often sold for marketing or other purposes that the user may not be able to understand or foresee [11,12]. In these contexts, consent is illusory and a form of coercion as it does not reflect informed choice—individuals are left with the ultimatum to use or not with minimal understanding of what they are consenting to [13]. On average, privacy notices are 3964 words in length and take 18 minutes to read [14]; moreover, they are written at a postuniversity level [15]. There is an ethical imperative to improve the transparency of data use and user control of data to avoid any future exploitation by entities collecting the data [16,17].

The patient access channel offers the potential to implement consent standards that enable transparent and meaningful consent. The Office of the Privacy Commissioner of Canada Meaningful Consent Guidelines include actionable recommendations for organizations to strengthen their digital consent practices [18] by:

  1. Emphasizing key elements
  2. Allowing individuals to control the level of detail they get and when
  3. Providing individuals with clear options to say yes or no
  4. Being innovative and creative
  5. Considering the consumer’s perspective
  6. Making consent a dynamic and ongoing process
  7. Being accountable and standing ready to demonstrate compliance

Although Meaningful Consent Guidelines provide a set of heuristics to improve consent processes, they are not specific to the digital health context [18]; moreover, they are only recommendations and do not require vendor compliance. The success of digital health requires trust and transparency in data use [19-21]. With privacy and trust as 2 intertwined antecedents to technology use and data-sharing behaviors [22], where their absence negatively affects use and behaviors, it is critical to understand the patient’s expectations of privacy to foster trust, acceptance, and use.


A 2-stage stakeholder engagement project was conducted by Canada Health Infoway to explore the user consent requirements of a patient access channel and the privacy considerations of its implementation. It consisted of a pan-Canadian survey and regional stakeholder workshops across Canada [23]. The study reported here is a retrospective analysis of the survey data. The objective of this retrospective study is to provide a more granular understanding of user preferences for consent management.

Study Design

This retrospective study uses data from a cross-sectional national web-based survey conducted between October 2 and October 15, 2019, by Canada Health Infoway. This study explored how consent management preferences and information needs differ across various patient characteristics. Specifically, this study asked the following research questions (RQs):

  • RQ1: What are the data control and consent management preferences of potential patient access channel users?
  • RQ2: How do information needs differ among individuals when making an informed decision to share their health data with different individuals or entities?

Data Collection

The survey comprised a series of hypothetical vignettes and consent scenarios to solicit participants’ perspectives on the consent management service and its functionalities through a mix of closed-and open-ended questions (see Multimedia Appendix 1 for the detailed vignettes and consent scenarios). There were four sections to the survey: (1) participant characteristics, (2) intention to register for the consent management service, (3) consent management use case scenarios, and (4) demographics.

The survey was administered electronically by a Canadian marketing research firm (Leger Marketing) to its pan-Canadian web panel. Using their pan-Canadian web panel, a 20-minute web-based survey was administered to the general Canadian population, reaching across the 10 provinces. The sampling strategy focused on potential digital health service users (ie, those with frequent interactions with the health care system) and used a proportional quota sampling strategy to recruit equal proportions of adults and older adults, with quotas set at 50% for adults and 80% for older adults with at least one chronic condition. The surveys were made available in English and French. Participants were eligible to participate in the survey if they were Canadian citizens, aged ≥18 years, currently live in Canada, and were within the provincial quotas for adults and seniors with chronic conditions. The survey had a view rate of 16.67% (1666/9997) and a completion rate of 61.04% (1017/1666).



This study’s analytic frame comprises potential users of the patient access channel. Potential users were defined as participants who indicated that they would register to use a hypothetical patient access channel in the first set of vignettes. The vignette presented information about Canada Health Infoway and the functionality of the patient access channel (or gateway). Participants were then asked how likely they would register for the gateway using a 4-point Likert scale (ranging from not at all likely to very likely). Participants were also provided with an I don’t know option throughout the survey. The second vignette introduced a trust framework as the rules of operation and participation, such as policies and agreements around data sharing and how users can control their health information. It also presents information on consent management, single sign-on, and privacy safeguards. Participants were then asked how likely it was that they would register for the gateway based on their understanding of the trust framework and the availability of safeguards. Participants who answered somewhat or very likely were categorized as potential users.

User characteristics (ie, demographics and user experiences) were used as covariates in the analysis. The variables that exhibited a low frequency of response for some scale points were collapsed into categories to improve the statistical power of the analysis [24]. Sociodemographic data included sex (male and female), age (18-44 years, 45-64 years, and ≥65 years), income (>CAD $80,000 [US $62,380] and <CAD $80,000 [US $62,380]), and region (Atlantic, Central, Prairies, and West Coast). User experiences comprised health care use (high users or low users), patient engagement (engaged or not engaged), digital health user (user or nonuser), perceived quality of care (good or poor), past web-based experiences (good or poor), health care privacy experiences (good or poor), past privacy breaches (no past breach, breach resolved, or breach not resolved), perceived confidentiality of PHI (private or not private), perceived sensitivity of PHI (high sensitivity or low sensitivity), and perceived sensitivity of digital health data (high sensitivity or low sensitivity). A median cutoff was used to establish the threshold for perceived sensitivity variables as the categories had no theoretical grounding or frame of reference. Further details about the outcome variables and covariates can be found in Multimedia Appendix 2. The full survey can be found in Multimedia Appendix 3.

There are four variables of interest in this study: (1) the importance of consent management, (2) adequacy of broad consent, (3) entities with default access to user data, and (4) user information needs to make an informed decision about data sharing.

Consent Management Preferences

Participants were presented with a vignette about privacy controls and the gateway function of enabling consent directives to block or restrict access to their PHI. Participants were then asked to rate the importance of having the ability to change privacy preferences for sharing PHI on a 4-point ordinal scale (not at all important to very important).

The next vignette presented a scenario regarding broad consent, where a data recipient would receive either all or none of a particular data source (eg, medical history, laboratory records, clinical and diagnostics, and e-service data). Participants were asked to assess whether the broad access control reflected their needs or did not reflect their needs or if they did not know.

For default access, participants were presented with a scenario where they enrolled in a new digital health service and were asked to select the entities to whom they would grant default access to new sources of data. Given that they still had the ability to apply consent directives, they were asked to select the following entities to whom they would grant default access to the new source of information (ie, select all that apply): health care providers, authorized members (ie, family and friends), digital health services and tools, or none of the above (ie, grant access individually or to each group).

Information Needs

To assess user information needs for informed consent, participants were first presented a vignette on consent management, which outlined the types of PHI they may access in the gateway and introduced an access control function that allows patients to authorize access to their PHI to health care providers, family and friends, and digital service vendors. Participants were asked to select the types of information they required to make an informed decision on whether to share their data in two scenarios: sharing with friends and family (scenario 1) and sharing with digital health providers for the digital health service (scenario 2).

Participants were provided with a list of information types that are found on consent forms and privacy notices and were asked to select all that applied. The nine information types were as follows: what types of information that the digital service can access, what the digital service can do with their data, potential risks and benefits of granting access, how to ask more questions about information sharing or privacy, how to file complaints about how information is shared, functions that allow them to monitor activity, types of data access controls available, and how to revoke access.

Data Analysis

First, the frequencies and percentages of the characteristics and demographics of all potential users were reported. For RQ1, frequencies for the importance of access control, adequacy of all or none access control based on data source, and default access to PHI were shown. Logistic regression was applied to evaluate the factors associated with the adequacy of access control, whether knowing it met their needs regarding adequacy, and granting default access. In the model-building procedure, a small subset of participants was excluded from the total sample because of the limited number of observations within each cell. The number of participants and the corresponding percentages were reported for the frequency analysis. Adjusted odds ratios (ORs) and 95% CIs were reported for logistic regression results.

For RQ2, the Friedman test was used to assess the difference in the number of items selected between the 2 scenarios in terms of sharing their information. The McNemar test was also performed to check if the frequency of each item differed between the 2 scenarios. All statistical analyses were conducted using SAS software (SAS Enterprise Guide 7.1; SAS Institute Inc).

Ethics Approval

This study was approved by the Research and Ethics Board at the Centre for Addiction and Mental Health (REB#114/2020) in Toronto, Canada.

Overall Results

Of the 1017 responses, 716 (70.4%) potential users of the patient access channel were identified. The potential user characteristics can be found in Table 1. Over three-quarters were low service users (559/716, 78.1%), noncaregivers (621/716, 86.7%), engaged patients (612/716, 85.5%), and satisfied with their quality of care (609/716, 85.1%). Over half had used digital health tools previously (471/716, 65.8%) and rated their PHI (364/716, 50.8%) and digital health data as sensitive (423/716, 59.1%). Most potential users reported having positive privacy experiences on the web (535/716, 74.7%), positive health care privacy experiences (643/716, 89.8%), and trust in the confidentiality of their records in the health care system (644/716, 89.9%). The final sample size of potential users for the logistic regression model was 712.

Table 1. Characteristics of potential users (N=716).
CharacteristicValues, n (%)

Female343 (47.9)

Male369 (51.5)

Transgendera2 (0.3)

Othera1 (0.1)

PNAa,b1 (0.1)
Age (years)

18-44204 (28.5)

45-64155 (21.7)

≥65357 (49.9)

Atlantic47 (6.6)

Central418 (58.4)

Prairie145 (20.3)

West Coast106 (14.8)
Income (CAD$; US $)

<$80,000 ($62,380)401 (56)

>$80,000 ($62,380)258 (36)

PNA57 (8)
Health care use

High (>20)146 (20.4)

Low (≤20)559 (78.1)

IDKc11 (1.5)

No621 (86.7)

Yes95 (13.3)
Quality of care

Good609 (85.1)

Poor92 (12.9)

IDK15 (2.1)
Prior digital health use

Yes471 (65.8)

No245 (34.2)
Engaged patient

Yes612 (85.5)

No104 (14.5)
Sensitivity of PHId

High (≥10)364 (50.8)

Low (<10)352 (49.2)
Sensitivity of digital health data

High (≥11)383 (53.5)

Low (<11)333 (46.5)
Web-based privacy experience

Good535 (74.7)

Poor134 (18.7)

IDK47 (6.6)
Privacy breach

Yes, resolved70 (9.8)

Yes, not resolved or IDK29 (4.1)

No breach617 (86.2)
Health care privacy experiences

Good643 (89.8)

Poor51 (7.1)

IDK22 (3.1)
Confidentiality of records

Private644 (89.9)

Not private37 (5.2)

IDK35 (4.9)

aIndicates subpopulations that were excluded from the logistic regression model.

bPNA: prefer not to answer.

cIDK: I do not know.

dPHI: personal health information.

RQ1: What Are the Data Control and Consent Management Preferences of Potential Patient Access Channel Users?

Importance of Access Control

Overall, 93.8% (672/716) of the potential users believed it was important (126/716, 18%) or very important (543/716, 75.8%) to have the ability to control their privacy preferences. Further subanalyses were not conducted as the distribution of responses would not allow for the detection of differences between the options.

Adequacy of All or None Access Control Based on Data Source

Approximately 53.8% (385/716) of the potential users felt that an all or none approach based on the data source to control data access was adequate for their needs, whereas 29.2% (209/716) did not, and 17.0% (122/716) did not know.

Geographic location and income were the only factors that were significantly associated with all or none being adequate for the participant’s needs. Potential users from the Prairies were 50% less likely than those from Central Canada to feel that it was adequate (OR 0.50, 95% CI 0.32-0.78). Potential users earning >CAD $80,000 (US $62,380) or potential users that did not disclose their income were 42% and 68% less likely to find all or none adequate than low-income earners (<CAD $80,000 [US $62,380]; OR 0.58, 95% CI 0.40-0.86; OR 0.32, 95% CI 0.16-0.66). Potential users with high income were 129% more likely to know that an all or none approach would meet their needs than those with low income (OR 2.29, 95% CI 1.36-3.83). Those who used digital health tools previously were associated with a 109% increased likelihood to know that an all or none approach would meet their needs than those who did not (OR 2.09, 95% CI 1.34-3.25). The results of the logistic regression analysis can be found in Table 2.

Table 2. Comparison of the adequacy of all or none based on participant characteristics.
CharacteristicsAdequate for needs, odds ratio (95% CI)Know versus not know, odds ratio (95% CI)


Female0.77 (0.53-1.13)0.72 (0.47-1.11)
Age (years)


46-640.80 (0.48-1.34)1.09 (0.55-2.15)

≥650.74 (0.47-1.17)0.68 (0.39-1.17)
Health care use


High1.33 (0.83-2.13)0.67 (0.40-1.12)

IDKa0.99 (0.17-5.58)0.38 (0.09-1.62)


Atlantic0.54 (0.26-1.10)1.45 (0.56-3.77)

Prairie0.50 (0.32-0.78)b0.88 (0.52-1.50)

West Coast0.74 (0.43-1.25)0.59 (0.34-1.05)


Yes1.60 (0.93-2.74)2.00 (0.93-4.30)
Income (CAD $; US $)

<$80,000 ($62,380)ReferenceReference

>$80,000 ($62,380)0.58 (0.40-0.86)b2.29 (1.36-3.83)b

PNAc0.32 (0.16-0.66)b0.79 (0.40-1.55)
Engaged patient


Yes0.92 (0.53-1.61)0.86 (0.44-1.69)
Quality of care


Good0.92 (0.49-1.76)1.45 (0.73-2.87)

IDK2.18 (0.38-12.56)1.26 (0.27-5.99)
Prior digital health use


Yes0.75 (0.50-1.13)2.09 (1.34-3.25)b
Sensitivity of PHId


High0.68 (0.44-1.05)1.07 (0.65-1.77)
Sensitivity of health data


High1.25 (0.81-1.92)0.70 (0.42-1.17)
Web-based privacy experiences


Good1.27 (0.77-2.08)1.18 (0.66-2.11)

IDK1.28 (0.50-3.27)0.52 (0.21-1.25)
Past privacy breach

Not resolved or IDKReferenceReference

Resolved1.22 (0.44-3.40)1.66 (0.43-6.42)

No breaches1.53 (0.63-3.74)0.97 (0.32-2.97)
Health care privacy experiences


Good1.73 (0.81-3.71)0.54 (0.20-1.48)

IDK0.64 (0.13-3.12)0.27 (0.06-1.19)
Confidentiality of PHI

Not privateReferenceReference

Private0.95 (0.38-2.35)1.09 (0.40-2.92)

IDK1.90 (0.50-7.27)0.87 (0.25-2.99)

aIDK: I do not know.

bSignifies a significant association when compared with the reference group.

cPNA: prefer not to answer.

dPHI: personal health information.

Default Access to PHI

Most potential users would grant default access to new data that become available to their health care providers (546/716, 76.3%) or authorized members, such as family, friends, and other care partners (389/716, 54.3%). Approximately one-fifth would grant default access to their digital health service provider for use with digital health services (138/716, 19.3%). Finally, 14.8% (106/716) of the potential users would not grant default access to anyone. Factors associated with granting default access were prior digital health use, health care privacy experiences, caregiver status, sex, and perceived sensitivity of PHI (Table 3).

Prior use of digital health tools was associated with a greater likelihood of granting default access to the 3 entities as there was a 66% greater likelihood of granting default access to health care providers (OR 1.66, 95% CI 1.14-2.44), 101% greater likelihood of granting default access to authorized members (OR 2.01, 95% CI 1.43-2.81), and 117% greater likelihood of granting default access to digital health service providers (OR 2.17, 95% CI 1.36-3.46). Those with prior digital health tool use were 53% less likely to not want to grant default access to anyone (OR 0.47, 95% CI 0.29-0.74). Those with positive health care privacy experiences were 156% more likely to grant default access to health care providers (OR 2.56, 95% CI 1.24-5.29) and 70% less likely to not grant default access than those with poor experiences (OR 0.30, 95% CI 0.20-0.70).

Service providers were 142% more likely to gain default access from caregivers (OR 2.42, 95% CI 1.45-4.04) but 39% less likely to gain default access from females (OR 0.61, 95% CI 0.40-0.92). Authorized users were 34% less likely to gain default access (OR 0.66, 95% CI 0.46-0.96) from potential users who had high perceived PHI sensitivity in comparison with those with low perceived PHI sensitivity. Those with high PHI sensitivity were also 126% more likely to not grant default access to anyone (OR 2.26, 95% CI 1.30-3.93) than those with low perceived PHI sensitivity.

Table 3. Comparison of default access based on participant characteristics.
CharacteristicsOdds ratio (95% CI)

HCPaAuthorized membersDHSPbNo one


Female1.12 (0.77-1.63)0.94 (0.68-1.29)0.61 (0.40-0.92)c0.90 (0.57-1.41)
Age (years)


45-640.85 (0.51-1.43)1.31 (0.84-2.06)0.94 (0.54-1.64)1.25 (0.65-2.39)

≥650.87 (0.55-1.37)1.35 (0.92-1.99)0.90 (0.55-1.47)1.39 (0.78-2.47)
Health care use

Low (≤20)ReferenceReferenceReferenceReference

High (>20)1.33 (0.82-2.17)0.79 (0.54-1.18)0.99 (0.60-1.63)1.04 (0.58-1.85)

IDKd0.43 (0.12-1.55)0.81 (0.22-2.96)0.43 (0.05-3.73)2.16 (0.48-9.73)


Atlantic0.85 (0.41-1.73)1.13 (0.60-2.15)1.38 (0.62-3.07)0.98 (0.41-2.35)

Prairie1.31 (0.80-2.15)0.72 (0.48-1.06)0.72 (0.42-1.23)0.61 (0.32-1.16)

West Coast0.72 (0.44-1.18)0.82 (0.52-1.28)0.93 (0.52-1.65)1.68 (0.95-2.97)


Yes0.75 (0.45-1.25)1.42 (0.89-2.27)2.42 (1.45-4.04)c0.56 (0.26-1.19)
Income (CAD $; US $)

<$80,000 ($62,380)ReferenceReferenceReferenceReference

≥$80,000 ($62,380)0.89 (0.60-1.31)1.23 (0.88-1.72)1.26 (0.83-1.91)1.20 (0.74-1.95)

PNAe0.73 (0.38-1.40)0.75 (0.42-1.34)0.65 (0.26-1.63)1.77 (0.86-3.68)
Engaged patient


Yes0.95 (0.54-1.66)1.14 (0.70-1.84)0.63 (0.35-1.14)1.29 (0.63-2.65)
Quality of care


Good1.07 (0.58-1.98)0.87 (0.51-1.49)0.75 (0.39-1.46)0.79 (0.38-1.65)

IDK1.27 (0.29-5.59)0.33 (0.09-1.16)1.54 (0.36-6.56)0.75 (0.12-4.58)
Prior digital health use


Yes1.66 (1.14-2.44)c2.01 (1.43-2.81)c2.17 (1.36-3.46)c0.47 (0.29-0.74)c
Sensitivity of PHIf

Low (<10)ReferenceReferenceReferenceReference

High (≥10)0.93 (0.60-1.43)0.66 (0.46-0.96)c1.04 (0.65-1.69)2.26 (1.30-3.93)c
Sensitivity of health data

Low (<11)ReferenceReferenceReferenceReference

High (≥11)0.91 (0.59-1.40)1.16 (0.80-1.68)1.13 (0.70-1.83)0.87 (0.51-1.49)
Web-based privacy experiences


Good0.79 (0.47-1.33)1.03 (0.66-1.59)1.29 (0.73-2.27)1.42 (0.73-2.75)

IDK0.55 (0.23-1.29)0.74 (0.35-1.58)0.22 (0.04-1.07)2.48 (0.91-6.74)
Past privacy breach

Not resolved or IDKReferenceReferenceReferenceReference

Resolved1.15 (0.38-3.43)1.24 (0.49-3.11)2.23 (0.68-7.32)0.57 (0.15-2.13)

No breaches0.91 (0.36-2.31)0.91 (0.41-2.01)1.30 (0.45-3.79)0.82 (0.28-2.34)
Health care privacy experiences


Good2.56 (1.24-5.29)c1.46 (0.73-2.90)1.90 (0.67-5.35)0.30 (0.20-0.70)c

IDK3.75 (0.97-14.51)1.81 (0.53-6.19)1.48 (0.20-10.85)0.31 (0.07-1.44)
Confidentiality of PHI

Not privateReferenceReferenceReferenceReference

Private1.25 (0.55-2.83)0.88 (0.41-1.87)1.50 (0.52-4.39)1.40 (0.48-4.14)

IDK0.79 (0.26-2.38)1.52 (0.54-4.33)2.14 (0.49-9.32)2.11 (0.54-8.28)

aHCP: health care provider.

bDHSP: digital health service provider.

cSignifies a significant association when compared with the reference group.

dIDK: I do not know.

ePNA: prefer not to answer.

fPHI: personal health information.

RQ 2: Do Information Needs Differ Among Individuals When Making an Informed Decision to Share Their Health Data With Different Individuals or Entities?

Overall, 81.8% (586/716) of potential users considered sharing their data in both scenarios (ie, potential users who did not select I do not intend on sharing information with them). In scenario 1, 89.3% (639/716) of potential users considered granting access to their friends and families and required an average of 5.64 (SD 2.68) of the 9 presented information types to make that decision. In scenario 2, 85.2% (610/716) of potential users considered granting commercial service providers access to their data and required an average of 5.54 (SD 2.85) of the 9 presented information types to make that decision.

There was no significant difference in the average number of information types required between the 2 scenarios (P>.05) for potential users who considered sharing in both scenarios (586/716, 81.8%). On the basis of the frequency of selection by this subset of potential users, the ranking of the types of information differed in the 2 scenarios (Figure 1); however, there was only a significant difference in frequency for 5 of the information types (P<.05). Information about accessible data types, restricting access, and revoking was selected more frequently in scenario 1. Information about potential risks and filing complaints was selected more frequently in scenario 2.

Figure 1. Differences in information needs required to support decisions on data sharing with friends and family and commercial service providers (ranked by frequency selected; n=586). PHI: personal health information.
View this figure

Principal Findings

As society becomes increasingly interconnected, there is a corresponding patient anticipation that their PHI and digital health data can be centrally accessed through innovations such as patient access channels, all with the belief that they will make life better [5]. A core requirement critical to the adoption of these patient access channels is a consent management system, as almost all potential users value the ability to control who can access their data. This exploratory study generated some insights to consider when implementing a consent management system. First, there may be acceptance of a believed, broad all or none access control model by data source, as 53.8% (385/716) of potential users believed it was adequate for their needs, and 17% (122/716) were unsure. Second, the willingness to provide others with default access to PHI and data varied depending on the recipient. Finally, potential users required an average of approximately 6 key types of information to provide informed decisions regarding data sharing; however, the required types of information varied depending on the recipient. The 3 insights are discussed in detail in the following sections.

Data Control and Consent Management Practices

Given the complexity of implementing interoperable access control in Canada [23], a broad all or none access control at the level of the data source may be the only option in the interim for patient access channels, especially as new data sources continuously emerge [8]. If implemented within a context of a trust framework in this scenario, there may be an acceptance of broad access control as over half of the potential users believed it was adequate for their needs. This finding echoes that of Grando et al [25], where broad access control was adequate for 58% of their study participants; moreover, their study was set in the context of behavioral health—an area where PHI is often perceived as more sensitive. Similarly, and surprisingly, user perceptions of the sensitivity of PHI and digital health data were not associated with adequacy, especially as data sensitivity is commonly associated with wanting greater degrees of access control because of privacy concerns [26,27]. A possible explanation is that the sample had a high level of trust in the confidentiality of their PHI and had positive dispositions about their web-based and health care privacy. This is consistent with an emerging set of evidence showing that positive perceptions of health care, trust in health care providers, and positive past privacy experiences may result in individuals having favorable views on sharing data [28-34]. Although these studies are contrary to prior findings of patients wanting more granular control options [25,27,35], their hypothetical and exploratory nature is subject to the privacy paradox [36]—the disconnect between intentions based on privacy concerns and actual behaviors. For instance, Schwartz et al [37] provided 108 patients with the ability to restrict access to their sensitive EHR data and found that 57% provided access to all listed providers and all PHI in their EHR, and 8.6% limited access by data type to specific providers. A significant minority of participants (43%) limited access to at least one provider.

Approximately one-fifth of the potential users did not know whether broad access control would be adequate, highlighting the need to better support their decision-making. The technical aspects of sharing data may be complex and may require greater literacy to appreciate the impact of broad access control [23]. This may explain why digital health use was associated with a 109% increase in the likelihood of knowing whether it is adequate. Familiarity and experience with digital health may provide individuals with heuristics to make decisions [34]. Studies show that broad access control and consent models may be acceptable when there is transparency [28,38,39] and assurance in oversight [40] regarding how the data are used. Biobank studies have shown that there are no significant differences in the willingness to share data between various consent scenarios when participants are provided with specific information on the data that are being used [38] or if there is assurance that a governing body provides oversight on how data are being used [40]. These findings can be applied to the digital health context, as a recent survey found that 80% of Canadians are willing to share their anonymized health information as long as the privacy and security of their PHI are assured [41].

Income and region were the only demographics found to be associated with adequacy perspectives on broad access control and knowing whether broad access control was adequate. Although the association between privacy attitudes and income echoes some privacy studies in health informatics, there have often been conflicting results across studies [33]. Historically, privacy research has focused on demographic variables as predictors of privacy attitudes and behaviors; however, collective evidence signals that individual demographic variables play a minor role and provide limited insight into understanding a phenomenon [33,42]. These findings are intended to inform further explorations to support implementation decisions. For instance, there may be value in understanding the underlying factors associated with those with high incomes that support their views of inadequacy and why they are more likely to know whether broad access control reflects their needs. In terms of region, health care in Canada is administered at the provincial level, where there are variations in legislation, policies, and digital health initiatives. Only a few provinces in Canada have a centralized patient portal, and the Prairie provinces of Alberta and Saskatchewan were launching theirs at the time of this study [43-45]. Understanding how these initiatives may have affected attitudes on broad access control adequacy may inform strategies on how to improve public favorability toward broad access control.

Potential users were most willing to grant default access to their health care providers, especially those with positive health care privacy experiences. Their willingness decreased as the data recipient was further removed from the point of care. Patients generally trust their physicians and those in their circle of care to keep their data confidential; however, this trust to maintain confidentiality diminishes as the data recipient is further away from those providing care (eg, health department, researchers, and corporations) [25,27,35,46]. Canadians are generally comfortable with the sharing of their PHI through EHRs with other health care providers as they believe timely and easy access to PHI is necessary for high-quality care [47], highlighting the role of contextual relevance and issue involvement [48] in data-sharing behaviors. This assertion is supported by the finding that those with prior digital health use were 66% to 117% more likely to grant default access (entity dependent) than those who did not use digital health tools. These users may have a greater stake in using digital health tools, familiarity, and perceived benefits of sharing digital health data [28-34]. Contextual relevance also mattered for users with higher perceived sensitivity of PHI, as 126% were more likely to not give anyone default access and were less likely to grant default access to family, friends, and other supporters. These individuals may not be comfortable with default access and may want more control over how new information is shared. Sharing may depend on the purpose and whether it is a necessity; moreover, these individuals may want more control over how certain information is disclosed to close social associates as it may affect their relationships. They may want to share about it in person rather than have others find it out by default through technology [34].

The value of data sharing with digital health service providers may not be as clear as there is limited trust in service providers, especially commercial vendors [19,47]. In this study, one-fifth of potential users were willing to grant default access to service providers, of whom users with prior digital health experience and caregivers were more likely to share their data. As discussed earlier, these users may have greater perceived benefits of granting default access to data to service providers [28-34]. For caregivers, sharing data for this population may be perceived to improve the tasks and stressors associated with their caregiving roles through the development of better or improved digital health services [49]. Further understanding the rationales of those trusting and skeptical of commercial service providers will be a necessity as these providers are a growing contributor to the number and types of services provided and an important source of data for patient access channels. This understanding can inform the permitted uses outlined in the trust framework and enable informed and meaningful consent.

Information Needs

This study builds upon the Meaningful Consent Guidelines for use in the digital health context. The guidelines recommend that digital vendors emphasize four key information elements: what is collected, who has access, purpose of data collection, and potential risks. However, this study found that potential users may need 5 to 6 emphasized information elements. The additional elements of emphasis include information on monitoring access, restricting access, and revoking consent. The findings also suggest that there is a need to tailor the order of emphasized elements as they will vary depending on who is accessing the data.

This study also highlights the importance of patient engagement in ensuring that the design of consent is based on user needs rather than assumptions. For instance, presenting this consent information in clear, concise, and plain language has been advocated but seldom practiced; however, implementing this assumption is only a part of the solution. A study found that an easier to read, concise consent form neither hindered nor improved comprehension or satisfaction with the consent process among their participants [50]. In contrast, providing users with ways of customizing their experiences and consuming information is more effective [51]. User experience is an overlooked aspect that should be considered when implementing informed and meaningful consent [23,52]. To empower users to make informed choices, meaningful consent for patient access channels should be iteratively co-designed with its users to ensure that they meet their needs rather than their assumed wants [53].


This study provides preliminary insights to support future patient engagement in co-designing a consent management system and meaningful consent. However, these exploratory findings are not intended to be generalizable as there are limitations to consider. This study is a secondary analysis of a cross-sectional survey, providing a snapshot of a time point where perspectives may vary over time. This study relied on a series of vignettes to preface the questions. Multiple rounds of revisions were made with Canada Health Infoway’s communications department and the market research firm to improve clarity of complex concepts (eg, privacy, consent, and data sharing). Prompts with these concepts include languages with high readability scores, which may have influenced some responses, especially those with lower digital literacy skills [54].

There are also inherent limitations to data collection through a survey panel as it only includes people who participate in the web panels managed by the company and relies on the self-selection of participants. The web-based nature of the survey may have excluded the perspectives of individuals with limited internet access. However, approximately 94% of Canadian households currently have access to the internet [55]. The purposive sampling strategy limits generalizability to the broader Canadian population as recruitment focused on frequent users of health care and excluded the Canadian territories (ie, early adopters of a patient access channel) [56,57]. The identified users in this study may be more engaged and experienced with digital health tools, thereby perceiving greater benefits and a greater willingness to share their data. The low response rate should also be considered as it may limit the diversity and nuance of perspectives because of the information lost through the combination or omission of demographics and participant characteristics for data analysis (eg, individuals who are transgender and other identifying genders). Future public and stakeholder engagement activities will require a greater in-depth investigation in co-designing consent management for patient access channels. Recognizing the ethical transgressions in trust in health care and research of marginalized and vulnerable communities [58], future research must include more diversity in perspectives to understand how to equitably strengthen meaningful consent and consent management practices.


Providing patients with the ability to manage their consent and control access to their PHI is valued by potential users of a patient access channel. Following the Office of the Privacy Commissioner of Canada’s Meaningful Consent Guidelines, future work should continue to consider the consumer’s perspective by involving them throughout the development and implementation processes [18]. Given technological limitations, future public engagement should investigate what makes broad access control acceptable and how to communicate its implications meaningfully and transparently. Future research should also focus on understanding user requirements for consent to further adapt the Meaningful Consent Guidelines for the digital health context. Understanding how to foster patient trust and how to empower them to feel confident in their data-sharing decisions is necessary for the success of patient access channels and the realization of the transformative potential of the evolving digital health ecosystem.


Canada Health Infoway Inc., an independent not-for-profit corporation funded by the Federal Government of Canada, funded this study and covered the publication costs for this study.

NS was supported by a Canadian Institutes of Health Research’s (CIHR) Health System Impact Fellowship. This program was led by the CIHR’s Institute of Health Services and Policy Research (CIHR-IHSPR), in partnership with the Center for Addiction and Mental Health.

The authors would like to thank Heba Roble for her fresh perspective in preparing this manuscript.

Conflicts of Interest

SW and ACL (affiliated with Canada Health Infoway) were members of the research team and were involved in the study design, interpretation and manuscript development.

Multimedia Appendix 1

Consent scenarios and vignettes.

DOCX File , 19 KB

Multimedia Appendix 2

Covariate definitions and outcome variables for logistic regression.

DOCX File , 18 KB

Multimedia Appendix 3

Full survey questionnaire.

DOCX File , 141 KB

  1. Connecting patients for better health. Canada Health Infoway. 2016.   URL: https:/​/www.​​en/​component/​edocman/​resources/​3152-connecting-patients-for-better-health-2016 [accessed 2021-11-30]
  2. Connecting patients for better health: 2018. Canada Health Infoway. 2018.   URL: https:/​/www.​​en/​component/​edocman/​resources/​reports/​benefits-evaluation/​3564-connecting-patients-for-better-health-2018 [accessed 2021-11-30]
  3. Symons JD, Ashrafian H, Dunscombe R, Darzi A. From EHR to PHR: let's get the record straight. BMJ Open 2019;9(9):e029582 [FREE Full text] [CrossRef] [Medline]
  4. Sterud B. Practitioner Application: the challenges in personal health record adoption. J Healthc Manag 2019;64(2):109-110. [CrossRef] [Medline]
  5. The future of connected health care: reporting Canadians' perspective on the health care system. Canadian Medical Association. 2019.   URL: [accessed 2021-11-30]
  6. Smith HJ, Dinev T, Xu H. Information privacy research: an interdisciplinary review. MIS Q 2011;35(4):989-1015. [CrossRef]
  7. Health data privacy and access across Canada. Canada Health Infoway. 2021.   URL: https:/​/www.​​en/​patients-families-caregivers/​digital-health-learning-program/​get-familiar-with-health-data [accessed 2021-11-30]
  8. Asghar MR, Lee T, Baig MM, Ullah E, Russello G, Dobbie G. A review of privacy and consent management in healthcare: a focus on emerging data sources. In: IEEE 13th International Conference on e-Science. 2017 Presented at: e-Science '17; October 24-27, 2017; Auckland, New Zealand p. 518-522. [CrossRef]
  9. Zazaza L, Venter HS, Sibiya G. The current state of electronic consent systems in e-health for privacy preservation. In: Proceedings of the 17th International Information Security Conference. 2018 Presented at: ISSA '18; August 15-16, 2018; Pretoria, South Africa p. 76-88. [CrossRef]
  10. Canada's digital charter: trust in a digital world. Government of Canada. 2020.   URL: [accessed 2020-12-07]
  11. Martinez-Martin N, Kreitmair K. Ethical issues for direct-to-consumer digital psychotherapy apps: addressing accountability, data protection, and consent. JMIR Ment Health 2018;5(2):e32 [FREE Full text] [CrossRef] [Medline]
  12. Sharon T. When digital health meets digital capitalism, how many common goods are at stake? Big Data Soc 2018;5(2):205395171881903. [CrossRef]
  13. World Economic Forum. 2020.   URL: https:/​/www.​​reports/​redesigning-data-privacy-reimagining-notice-consent-for-humantechnology-interaction [accessed 2021-11-30]
  14. Litman-Navarro K. We read 150 privacy policies. They were an incomprehensible disaster. The New York Times. 2019.   URL: [accessed 2021-11-30]
  15. Sunyaev A, Dehling T, Taylor PL, Mandl KD. Availability and quality of mobile health app privacy policies. J Am Med Inform Assoc 2015;22(e1):e28-e33. [CrossRef] [Medline]
  16. Armstrong S. Data, data everywhere: the challenges of personalised medicine. BMJ 2017;359:j4546. [CrossRef] [Medline]
  17. Nebeker C, Torous J, Bartlett Ellis RJ. Building the case for actionable ethics in digital health research supported by artificial intelligence. BMC Med 2019;17(1):137 [FREE Full text] [CrossRef] [Medline]
  18. Guidelines for obtaining meaningful consent. Office of the Privacy Commissioner of Canada. 2018.   URL: [accessed 2021-11-30]
  19. Torous J, Roberts LW. Needed innovation in digital health and smartphone applications for mental health: transparency and trust. JAMA Psychiatry 2017;74(5):437-438. [CrossRef] [Medline]
  20. Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A'Court C, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res 2017;19(11):e367 [FREE Full text] [CrossRef] [Medline]
  21. Duggal R, Brindle I, Bagenal J. Digital healthcare: regulating the revolution. BMJ 2018;360:k6. [CrossRef] [Medline]
  22. Shen N, Strauss J, Silver M, Carter-Langford A, Wiljer D. The eHealth trust model: a patient privacy research framework. Stud Health Technol Inform 2019;257:382-387. [Medline]
  23. Shen N, Kassam I, Ilkina D, Wickham S, Carter-Langford A. Meaningful digital consent in Canada: recommendations from pan-Canadian consent management workshops. Healthc Q 2022;24(4):40-47. [CrossRef] [Medline]
  24. DiStefano C, Shi D, Morgan GB. Collapsing categories is often more advantageous than modeling sparse data: investigations in the CFA framework. Struct Equ Model 2021;28(2):237-249. [CrossRef]
  25. Grando MA, Murcko A, Mahankali S, Saks M, Zent M, Chern D, et al. A study to elicit behavioral health patients' and providers' opinions on health records consent. J Law Med Ethics 2017;45(2):238-259 [FREE Full text] [CrossRef] [Medline]
  26. Serrano KJ, Yu M, Riley WT, Patel V, Hughes P, Marchesini K, et al. Willingness to exchange health information via mobile devices: findings from a population-based survey. Ann Fam Med 2016;14(1):34-40 [FREE Full text] [CrossRef] [Medline]
  27. Caine K, Hanania R. Patients want granular privacy control over health information in electronic medical records. J Am Med Inform Assoc 2013;20(1):7-15 [FREE Full text] [CrossRef] [Medline]
  28. Esmaeilzadeh P. The impacts of the perceived transparency of privacy policies and trust in providers for building trust in health information exchange: empirical study. JMIR Med Inform 2019;7(4):e14050 [FREE Full text] [CrossRef] [Medline]
  29. Esmaeilzadeh P, Mirzaei T. Comparison of consumers' perspectives on different health information exchange (HIE) mechanisms: an experimental study. Int J Med Inform 2018;119:1-7. [CrossRef] [Medline]
  30. Li T, Slee T. The effects of information privacy concerns on digitizing personal health records. J Assn Inf Sci Tec 2014;65(8):1541-1554. [CrossRef]
  31. Maiorana A, Steward WT, Koester KA, Pearson C, Shade SB, Chakravarty D, et al. Trust, confidentiality, and the acceptability of sharing HIV-related patient data: lessons learned from a mixed methods study about Health Information Exchanges. Implement Sci 2012;7:34 [FREE Full text] [CrossRef] [Medline]
  32. Walker DM, Johnson T, Ford EW, Huerta TR. Trust me, I'm a doctor: examining changes in how privacy concerns affect patient withholding behavior. J Med Internet Res 2017;19(1):e2 [FREE Full text] [CrossRef] [Medline]
  33. Shen N, Bernier T, Sequeira L, Strauss J, Silver MP, Carter-Langford A, et al. Understanding the patient privacy perspective on health information exchange: a systematic review. Int J Med Inform 2019;125:1-12. [CrossRef] [Medline]
  34. Shen N, Sequeira L, Silver MP, Carter-Langford A, Strauss J, Wiljer D. Patient privacy perspectives on health information exchange in a mental health context: qualitative study. JMIR Ment Health 2019;6(11):e13306 [FREE Full text] [CrossRef] [Medline]
  35. Soni H, Grando A, Aliste MP, Murcko A, Todd M, Mukundan M, et al. Perceptions and preferences about granular data sharing and privacy of behavioral health patients. Stud Health Technol Inform 2019;264:1361-1365. [CrossRef] [Medline]
  36. Acquisti A, Brandimarte L, Loewenstein G. Privacy and human behavior in the age of information. Science 2015;347(6221):509-514. [CrossRef] [Medline]
  37. Schwartz PH, Caine K, Alpert SA, Meslin EM, Carroll AE, Tierney WM. Patient preferences in controlling access to their electronic health records: a prospective cohort study in primary care. J Gen Intern Med 2015;30 Suppl 1:S25-S30 [FREE Full text] [CrossRef] [Medline]
  38. Kaufman DJ, Baker R, Milner LC, Devaney S, Hudson KL. A survey of U.S adults' opinions about conduct of a nationwide precision medicine initiative® cohort study of genes and environment. PLoS One 2016;11(8):e0160461 [FREE Full text] [CrossRef] [Medline]
  39. Esmaeilzadeh P. The effect of the privacy policy of Health Information Exchange (HIE) on patients’ information disclosure intention. Comput Secur 2020;95:101819. [CrossRef]
  40. Sanderson SC, Brothers KB, Mercaldo ND, Clayton EW, Antommaria AH, Aufox SA, et al. Public attitudes toward consent and data sharing in biobank research: a large multi-site experimental survey in the US. Am J Hum Genet 2017;100(3):414-427 [FREE Full text] [CrossRef] [Medline]
  41. Consulting Canadians on the future of their health system: a health dialogue. Canada Health Infoway. 2020.   URL: https:/​/www.​​en/​component/​edocman/​resources/​reports/​3850-a-healthy-dialogue-executive-summary [accessed 2021-11-30]
  42. Gerber N, Gerber P, Volkamer M. Explaining the privacy paradox: a systematic review of literature investigating privacy attitude and behavior. Comput Secur 2018;77:226-261. [CrossRef]
  43. Avdagovska M, Stafinski T, Ballermann M, Menon D, Olson K, Paul P. Tracing the decisions that shaped the development of MyChart, an electronic patient portal in Alberta, Canada: historical research study. J Med Internet Res 2020;22(5):e17505 [FREE Full text] [CrossRef] [Medline]
  44. Basky G. Some provinces still delay access to health records via patient portals. CMAJ 2019;191(48):E1341 [FREE Full text] [CrossRef] [Medline]
  45. New website allows Saskatchewan residents to access their personal health information anywhere, anytime. Government of Saskatchewan. 2019.   URL: [accessed 2021-11-30]
  46. Hunter IM, Whiddett RJ, Norris AC, McDonald BW, Waldon JA. New Zealanders' attitudes towards access to their electronic health records: preliminary results from a national study using vignettes. Health Informatics J 2009;15(3):212-228 [FREE Full text] [CrossRef] [Medline]
  47. Canadian digital health survey: what Canadians think. Canada Health Infoway. 2020.   URL: https:/​/www.​​en/​component/​edocman/​resources/​reports/​benefits-evaluation/​3856-canadian-digital-health-survey-what-canadians-think [accessed 2021-11-30]
  48. Abdelhamid M, Gaia J, Sanders GL. Putting the focus back on the patient: how privacy concerns affect personal health information sharing intentions. J Med Internet Res 2017;19(9):e169 [FREE Full text] [CrossRef] [Medline]
  49. Lindeman DA, Kim KK, Gladstone C, Apesoa-Varano E. Technology and caregiving: emerging interventions and directions for research. Gerontologist 2020;60(Suppl 1):S41-S49 [FREE Full text] [CrossRef] [Medline]
  50. Grady C, Touloumi G, Walker AS, Smolskis M, Sharma S, Babiker AG, INSIGHT START Informed Consent Substudy Group. A randomized trial comparing concise and standard consent forms in the START trial. PLoS One 2017;12(4):e0172607 [FREE Full text] [CrossRef] [Medline]
  51. Beskow LM, Friedman JY, Hardy NC, Lin L, Weinfurt KP. Developing a simplified consent form for biobanking. PLoS One 2010;5(10):e13302 [FREE Full text] [CrossRef] [Medline]
  52. Is "Meaningful Consent" a contradiction in terms?: three design jams seek the answer. Office of the Privacy Commissioner of Canada. 2021.   URL: https:/​/www.​​en/​opc-actions-and-decisions/​research/​funding-for-privacy-research-and-knowledge-translation/​real-results/​rr-index/​jam-intro/​ [accessed 2021-11-30]
  53. Adam MB, Minyenya-Njuguna J, Karuri Kamiru W, Mbugua S, Makobu NW, Donelson AJ. Implementation research and human-centred design: how theory driven human-centred design can sustain trust in complex health systems, support measurement and drive sustained community health volunteer engagement. Health Policy Plan 2020;35:ii150-ii162 [FREE Full text] [CrossRef] [Medline]
  54. McKnight DH, Choudhury V, Kacmar C. Developing and validating trust measures for e-commerce: an integrative typology. Inf Syst Res 2002;13(3):334-359. [CrossRef]
  55. Access to the internet in Canada, 2020. Statistics Canada. 2020.   URL: [accessed 2021-11-30]
  56. Mák G, Smith Fowler HS, Leaver C, Hagens S, Zelmer J. The effects of web-based patient access to laboratory results in British Columbia: a patient survey on comprehension and anxiety. J Med Internet Res 2015;17(8):e191 [FREE Full text] [CrossRef] [Medline]
  57. Leonard KJ, Casselman M, Wiljer D. Who will demand access to their personal health record? A focus on the users of health services and what they want. Healthc Q 2008;11(1):92-96 [FREE Full text] [CrossRef] [Medline]
  58. O'Sullivan L, Crowley R, McAuliffe É, Doran P. Contributory factors to the evolution of the concept and practice of informed consent in clinical research: a narrative review. Contemp Clin Trials Commun 2020;19:100634 [FREE Full text] [CrossRef] [Medline]

CIHR: Canadian Institutes of Health Research
EHR: electronic health record
OR: odds ratio
PHI: personal health information
RQ: research question

Edited by C Lovis; submitted 04.06.21; peer-reviewed by J Shaw, C Schmit; comments to author 07.08.21; revised version received 17.12.21; accepted 31.01.22; published 31.03.22


©Nelson Shen, Iman Kassam, Haoyu Zhao, Sheng Chen, Wei Wang, Sarah Wickham, Gillian Strudwick, Abigail Carter-Langford. Originally published in JMIR Medical Informatics (, 31.03.2022.

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 Medical Informatics, 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.