Background: Digital health has the potential to improve the quality of care, reduce health care costs, and increase patient satisfaction. Patient acceptance and consent are a prerequisite for effective sharing of personal health information (PHI) through health information exchanges (HIEs). Patients need to form and retain trust in the system(s) they use to leverage the full potential of digital health. Germany is at the forefront of approving digital treatment options with cost coverage through statutory health insurance. However, the German population has a high level of technology skepticism and a low level of trust, providing a good basis to illuminate various facets of eHealth trust formation.
Objective: In a German setting, we aimed to answer the question, How does an individual form a behavioral intent to share PHI with an HIE platform? We discussed trust and informed consent through (1) synthesizing the main influence factor models into a complex model of trust in HIE, (2) providing initial validation of influence factors based on a qualitative study with patient interviews, and (3) developing a model of trust formation for digital health apps.
Methods: We developed a complex model of the formation of trust and the intent to share PHI. We provided initial validation of the influence factors through 20 qualitative, semistructured interviews in the German health care setting and used a deductive coding approach to analyze the data.
Results: We found that German patients show a positive intent to share their PHI with HIEs under certain conditions. These include (perceived) information security and a noncommercial organization as the recipient of the PHI. Technology experience, age, policy and regulation, and a disposition to trust play an important role in an individual’s privacy concern, which, combined with social influence, affects trust formation on a cognitive and emotional level. We found a high level of cognitive trust in health care and noncommercial research institutions but distrust in commercial entities. We further found that in-person interactions with physicians increase trust in digital health apps and PHI sharing. Patients’ emotional trust depends on disposition and social influences. To form their intent to share, patients undergo a privacy calculus. Hereby, the individual’s benefit (eg, convenience), benefits for the individual’s own health, and the benefits for public welfare often outweigh the perceived risks of sharing PHI.
Conclusions: With the higher demand for timely PHI, HIE providers will need to clearly communicate the benefits of their solutions and their information security measures to health care providers (physicians, nursing and administrative staff) and patients and include them as key partners to increase trust. Offering easy access and educational measures as well as the option for specific consent may increase patients’ trust and their intention to share PHI.
Data-driven medicine promises better care and more efficient health care processes. Digital health information exchanges (HIEs), electronic health records (EHRs), and eHealth and mobile health (mHealth) apps have become increasingly relevant for sharing personal health information (PHI) in the past years. Countries aim to adopt and implement HIEs to improve the quality of care, reduce health care costs, und increase patient outcomes and satisfaction . Germany is no exception. In 2019, Germany passed a law approving the prescription of mHealth apps by doctors whereby the costs are covered by the German statutory health insurance. All insured people are eligible to use registered mHealth apps as part of standard care [ ]. However, uptake has been slow because of restraints from both patients and providers [ , ]. A recent study among German citizens [ ] found that almost 25% of the respondents believe that technology creates more problems than it solves, thus indicating that Germans are highly skeptical toward technology overall. This is in line with prior research on country-specific trust levels [ , ], where Germany is associated with rather low levels of trust compared to other countries.
Patient acceptance and opt-in are crucial for efficient use of HIEs (we subsume EHRs, mHealth apps, and eHealth apps under the term “HIEs” for purposes of readability). Patients need to trust that the information security measures and privacy policies of the HIE provider are sufficient to protect their PHI [, ]. Providers must explain these policies to the patient and show that they are upheld. Several studies have found that most patients have a positive attitude toward EHRs for reasons of convenience, completeness, and ease of communication [ , - ]. However, PHI is considered highly sensitive. Data breaches can potentially have significant negative consequences for the patients involved [ ]. Patients, although excited about the possibilities of EHRs [ , - ], might not fully understand the impact their sharing decisions may have. They may even be reluctant to share their PHI digitally after witnessing data breaches [ ]. Privacy concerns are the largest barrier to sharing PHI [ , ]. Trust in the safety and soundness of technological solutions has a strong impact on user opt-in [ , - ]. Backhaus [ ] described the trust of a user in a technical system as the expectation that the system will perform certain tasks based on the user’s wishes and assumptions without misusing their vulnerability caused by the execution of the process. Trust in digital health apps is strongly linked to trust in the respective health care provider [ ]. Buhr et al [ ], for example, found that Germans trust governmental institutions, such as the statutory health insurance, more than private institutions. Dhopeshwarkar et al [ ] found that patients trust physicians regarding accessing health care files. Considering these developments, patients need to become the sovereign of their own PHI [ ]. They need to be able to provide informed consent on what should be shared through HIEs and who can use PHI stored in their EHRs.
There have been multiple calls for more research on the subject, followed by an upswing in recent years . Looking at the specific case of Germany in the context of regulatory initiatives [ ] and a comparatively low trust level [ ], however, may provide additional insight into patients’ behavioral intentions [ , ] and measures that HIE providers can undertake to increase the level of trust in their solutions and processes. Our research aimed to answer the following research question: How does an individual form a behavioral intent to share PHI with an HIE platform? We contributed to the discussion of trust and informed consent in digital health in the following ways: (1) We derived a complex model of trust in an eHealth app and intent formation to share PHI based on the belief-attitude-intention framework, (2) provided initial exploratory validation of influence factors through a qualitative analysis process with interviews of German patients, and (3) developed a model of trust formation for eHealth apps.
Trust in and acceptance of eHealth apps have become a more prevalent research area in recent years due to the increasing uptake of HIEs and the rise in virtual interactions in the COVID-19 pandemic years [, , ]. Different approaches try to assess trust in and user acceptance of (health) information technology and the sharing of PHI. Consumer acceptance and use of technology is often assessed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and its extensions and adaptations [ , , ]. The model has been applied to the health care context [ , , , ] and has also been enhanced with health behavior theories, such as the Health Belief Model, protection motivation theory, and social cognitive theory [ ]. Abdelhamid [ ], for example, adapted the UTAUT model to PHI sharing with HIEs and used privacy concerns, social influence, trust in health care professionals, health concerns, and perceived usefulness as the main variables for his quantitative study. He found that all factors except for privacy concerns have a positive impact on the sharing intention. More customized sharing choices may mitigate the negative effect of privacy concerns on PHI-sharing intention.
Privacy concerns are often stated as the main barrier to sharing of PHI. They are, however, not always part of the (adapted) Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) models. The Antecedent-Privacy Concern-Outcome (APCO) model presents 1 approach for the analysis of privacy concerns [- ]. Shen et al [ ], for example, developed an eHealth trust model based on the APCO approach and suggested personality, tech-savviness, eHealth awareness, health care perception, privacy experience, demographics, and culture as antecedents of privacy concerns. The authors described trusting belief as well as policy and regulation as moderating factors. In the outcome stage, they distinguished between a privacy calculus and the final behavioral reaction.
Privacy concerns are often associated with the privacy calculus model [, - ]. Abdelhamid et al [ ], for example, presented a model with the following variables: patient activation, issue involvement, privacy concerns, trust in providers, and patient-physician relationship. They found that privacy concerns negatively affect the intention to share PHI. This can only partially be mitigated by the other variables.
Given the multifaceted nature of the concepts presented, in this study, we aimed to integrate the previous findings into a comprehensive model.
Scientific Basis of the Initial Model
Our initial model is depicted in. An overall definition of the constructs used in the initial model can be found in . We transferred the common belief-attitude-intention framework based on the theory of reasoned action [ , ] to a PHI-sharing setting. We divided our model into 3 stages: (1) belief formation, (2) attitude formation, and (3) information-sharing intent. We defined privacy concern as a belief referring to “the information the individual has about the object,” in this case the HIE [ ]. We defined trust as an attitude, that is, the “favorable or unfavorable evaluation of an attitude object” [ ]. We defined intent as “the subjective probability that the person will behave in a particular way vis-à-vis the attitude object” [ ]. Beliefs are formed based on preconditions and previous experiences [ , ], which are sometimes referred to as values [ ] or antecedents [ , ]. The antecedents of privacy concerns are depicted in the APCO model [ , , ]. We added the antecedent component to the belief-attitude-intention framework to enhance the understanding of the belief formation. Behavioral intentions are influenced not only by trust but also by an individual’s privacy calculus [ , , ], defined as the “cost-benefit analysis” of disclosing information [ , ]. We defined privacy calculus as an attitude, following the attitude definition of Stone et al [ ].
Stage 1: Belief Formation
In the belief formation stage, an individual forms a belief about privacy and related risks of sharing PHI (privacy concern) based on antecedents. An individual has certain previous experiences with sharing information and (eHealth) technology, which we subsumed under technology experience . We included an individual’s general experience with technology in the variable tech-savviness [ ]. Since eHealth is a comparatively new topic for most individuals, we can only assess the awareness of eHealth of an individual [ ]. We synthesized Shen et al’s [ ] privacy perspective and related findings of Abdelhamid [ ] and Hassandoust et al [ ] into the item information security knowledge to cover potential experiences with data breaches and measures taken to protect an individual’s PHI in the light of the discussions of data sovereignty. We followed the following definition of information security: “Information security is the protection of information from a wide range of threats. This is achieved by managing a suitable set of security controls, policies and procedures within an Information Security Management System. The goal of general InfoSec is the ‘preservation of confidentiality, integrity and availability of information’ and includes such terms as the accountability of users, authentication, non-repudiation and reliability” [ ]. Esmailzadeh [ ] and Shen et al [ ] have shown that an individual’s perception of policy and regulation influences their privacy beliefs. We included demographic factors as antecedents because studies have shown clear differences between the privacy beliefs of diverse demographic groups [ , , , ]. Finally, an individual has personality traits and dispositions, particularly a disposition to trust, that influence all interactions with the individual’s environment [ , , ], including privacy concerns, attitudes, and the intent to share or withhold PHI [ , ]. In the eHealth setting, we argued in line with Abdelhamid et al [ , ] and added health concerns and patient activation into the initial dispositions. All the aforementioned factors lead to the formation of privacy concerns related to sharing an individual’s PHI. Individuals continuously interact in their specific social environment. Studies [ , , ] have shown that social influence affects the intent to share via trust formation. We included social influence as an additional antecedent to trust and the privacy calculus, influencing the perceived utility of sharing PHI [ , ], and the emotional trust of an individual in an HIE.
Stage 2: Attitude Formation
In the attitude formation stage, an individual forms attitudes toward sharing PHI. These attitudes can be divided into the privacy calculus (see the previous section) and trust. The concept of trust has a composite definition [, , ]. The thoughts and decisions of an individual include both cognition and emotion [ ], leading to a distinction between cognitive trust and emotional trust [ , ]. Cognitive trust in eHealth has different dimensions: First, patients develop a level of cognitive trust in their health care providers, which is necessary for the initial treatment. Based on trust transfer theory, individuals may transfer this established trust to the HIE [ , , ]. To form a sharing intent through an HIE, however, individuals not only need to trust the health care institution but also have thoughts about the expertise and integrity of the HIE provider [ ]. This is often not directly associated with the health care provider. Research shows that an individual’s privacy concern influences the risk associated with sharing PHI and well as the cognitive judgment whether to trust an entity in a digital setting [ , ]. The privacy calculus assesses the perceived utility of the sharing decision and compares it to the perceived risks associated with this decision [ , ]. Individuals value sharing data if they have a perceived benefit. In the case of PHI, patients may, for example, experience better or faster treatment. They may perceive a benefit because a certain health topic has personal relevance due to a particular health concern, which we captured as issue involvement [ ]. The perceived risk refers to the loss or misuse of PHI because of, for example, data breaches and the associated perceived damages the individual incurs because of the data incident [ ].
Stage 3: Information-Sharing Intent
Finally, in the information-sharing intent stage, the individual forms a behavioral intent to share PHI with the HIE. Contrary to Esmaeilzadeh [, ], we did not differentiate between the opt-in intention and the willingness-to-share intention. Patients often do not actually have an option to opt in or out of an HIE [ ], but rather, they have choices on what to share with an HIE. We regarded the willingness to share information with an HIE as the outcome of the trust formation model. We defined the willingness to share (health) information as the intention to voluntarily disclose information about one’s (health) status to others [ ]. The complete theoretical model is depicted in .
We described the methodology along the Consolidated Criteria for Reporting Qualitative Research (COREQ) domains () [ ].
Domain 1: Research Team and Reflexivity
Interviews were conducted by 3 different interviewers who were part of the joint research project funding this research. All researchers had postgraduate degrees, while 1 also had a PhD; 1 of the researchers was female, while 2 were male. All interviewers had received prior training in conducting qualitative interviews, and all were employed at a research institution or university when conducting the interviews.
Relationship With Participants
The researchers conducting the interviews had no previous relationship with the interviewees. The participants received a 1-page introduction of the research project and its goals before agreeing to take part in the interviews. They did not have any further knowledge of the researchers other than project involvement. The researchers were involved in a common research project with the objective of developing a virtual consent assistant for informed and sovereign patient consent.
Domain 2: Study Design
The study was based on a qualitative content analysis and followed a deductive category application [, ]. Due to the exploratory nature of our study for the German system, we performed qualitative, semistructured interviews [ ] to provide a starting point for our empirical assessment.
Due to the COVID19 pandemic and associated contact restrictions, we were unable to proceed with our initial plan to recruit a variety of participants onsite at a large German university clinic. We evaluated different data-gathering strategies for their viability. We eventually recruited targeted interview candidates using a combination of purposive and convenience sampling. We selected candidates who (1) had signed a consent form for a medical procedure in the past 6 months and (2) met the rough replication of the demographics of potential app users from the existing personal networks of the researchers. The participants were approached via phone calls. Overall, we approached 25 people, of which 20 (80%) agreed to be interviewed. All participants were offered a small financial compensation (€25, or US $27) for their time. One person asked for the compensation to be donated to a worthy cause.
The interviews were conducted in German language and over the phone, whereby the participants answered the phone in their own homes. We could not assess whether there was anyone else present with them. The researchers worked out of their own offices and were by themselves. Overall, we conducted 20 qualitative user interviews.shows the details of the participants.
|Interview/participant number||Sex||Age (years)||Chronic illness present|
We developed a questionnaire for the semistructured interviews, focusing on experiences with PHI consent, digital and consent literacy, trust, and individual data-sharing preferences. The translated questionnaire can be found in. The questionnaire was developed by the researchers conducting the interviews and discussed in the project consortium. As shown in the provided questionnaire, we added a vignette as a final question in order to elicit the participants’ intent on sharing PHI based on a specific situation in line with Barter and Renold [ ]. Before the interview, the participants were asked to fill out a short questionnaire for demographic data. No repeat interviews were carried out. One interview had to be paused because the participant had to take a call, and was continued soon after. All interviews were audio-recorded, and the research team took limited field notes during the interviews. The average interview time was about 60 minutes.
Metathemes, as defined by Guest , presented themselves after coding about half the interviews, and we could assume data saturation after analyzing all 20 interviews. The transcripts were not returned to the participants for comment.
Domain 3: Analysis and Findings
The interviews were transcribed using a transcription service via commissioned data processing following all stipulated information security measures. The transcripts were imported into MAXQDA  for coding. Coding was performed independently by 2 researchers with postgraduate degrees, 1 of whom had a PhD. We revised the coding agenda and coding rules before final coding and then compared results after final coding using Cohen κ. We reached a Cohen κ value of 0.88, indicating solid interrater agreement [ ]. An overview of the constructs and definitions of the coding agenda, key illustrative quotations per code, and the number of statements coded per interview can be found in .
In this paper, we presented quotations to illustrate our findings. All interview quotations presented in the results were translated from German to English. The interviews were numbered for identification, and the position (denoted as “pos.” in the quotations) of each quotation in the transcript was marked accordingly. We presented major and minor themes in the results, and we adapted our initial model according to our exploratory findings.
We obtained a positive ethics vote from the University of Cologne (review number: 21-1271). The survey was conducted in accordance with the applicable provisions of the Data Protection Act (Art.9 para.2 letter b DSGVO). The interviewers are subject to the obligation of secrecy and are also bound to data secrecy.
Prior to conducting the interviews, the participants obtained information about study participation and a consent form. The signed consent forms are kept separately from the short questionnaire and interview results in the university clinic so that no connection can be made between the information in the short questionnaire and the consent forms. The interviews were recorded with the help of a recorder. The recorded interviews were transcribed and pseudonymized. They were processed in written and pseudonymized form only so that it is no longer possible to draw conclusions about the person or third parties. In contrast to the transcripts, the audio files created could not be sufficiently pseudonymized for technical reasons, which is why they were not further processed after the interviews. They will, however, be stored until the end of the project in October 2023 and then deleted.
The participants were thoroughly informed that their participation in the study is completely voluntary. This means that at any time and without giving reasons, they had the right to refuse to answer individual questions. They could also terminate participation in the study or withdraw their consent to participate at any time without incurring any disadvantages. In this case, all data collected up to that point (questionnaires, transcripts, audio recordings) were completely deleted. All data collected in the context of the interview study were treated confidentially, stored exclusively for scientific purposes, and used exclusively by the scientists in the project team.
The average age of the participants was 48.5 (median 43.0) years. The youngest participant was 26 years old, and the oldest was 83 years old. About 70% (n=14) of the participants were women. About 80% (n=16) had an academic degree. The sample was skewed and may have overrepresented women with higher education. We did, however, postulate that the gained insights were relevant, given the articulated need to improve the understanding of female health perceptions and behaviors [, ]. Of the 20 participants, 12 (60%; n=10, 83%, female) suffered from chronic illnesses and were more frequently in contact with health care institutions. Most participants dealt with 2-5 medical consent forms annually. In addition, 5 (42%) participants, who all reported 1 or more chronic illnesses, stated they would be confronted with 6-11 consent forms, indicating a multitude of interactions with the health care system. All participants said they use technology, mainly smartphones and laptops, for personal communication and information purposes. The most used features are search engines (n=20, 100%), email (n=19, 95%), online shopping (n=19, 95%), and online banking (n=18, 90%). Only 12 (60%) participants reported the usage of social media, and only 10 (83%) participants reported using online education formats. Furthermore, 12 (60%) participants reported their digital aptitude with a 3.5-4 score on a 5-point Likert scale ranging from little to no knowledge to expert knowledge. In contrast, participants over the age of 70 years reported their digital aptitude with an average score of 2.6.
Conceptual Model Validation
To validate the conceptual model of eHealth trust formation in, we analyzed our results along 3 stages: belief formation, attitude formation, and information-sharing intent. Generally, we found that most people show a behavioral intent to share their PHI with health care professionals digitally. One participant stated:
I am 100% convinced that the pros outweigh the cons.
[Interview 2, pos. 121]
If you can judge the risk [associated with data breaches when sharing], then, generally yes, I would share it.
[Interview 20, pos. 201]
Given the fulfillment of certain conditions, such as anonymity, participants would be willing to share their PHI with (noncommercial) medical research institutions for advancement in medical science. One participant stated:
And I think it is very important that everything that is related to [human health] is made available to science.
[Interview 11, pos. 11]
Stage 1: Belief Formation
In the belief formation stage, we found that an individual’s privacy concern is indeed influenced by previous experiences with technology (tech-savviness) and their knowledge on information security. As previously mentioned, all participants used digital technology, mainly smartphones and laptops. The median self-reported tech-savviness was 3.5 on a 5-point Likert-Scale, with 1 being low and 5 being high. A notable statement was:
I would check the possibilities suggested on my computer or phone, and then I would check settings to see what I want and don’t want, and if I don’t understand it, then [the app] I would delete it.
[Interview 16, pos. 77]
People with low tech-savviness (mostly over the age of 70 years in our sample) adopt strategies to help interact with digital technology. This was indicated in this statement:
If I need a new app or want to delete one, then someone has to do this for me.
[Interview 11, pos. 55]
The overall knowledge on information security can be classified as low to medium and heavily relies on what has been communicated by the provider and preinstalled in the used system. One participant mentioned:
Something like this is already on my phone, an antivirus program.
[Interview 10, pos. 67]
Often, people do not seem to be aware of or interested in the subject, as indicated by, for example, participant 2 (pos. 53), who “never looked into it.” People seem to assume that:
As soon as you are digital or you are transferring information, then you can’t control where it ends up and who uses it.
[Interview 12, pos. 71]
Participants with a higher level of digital competency stated:
Not every app gets a right to access things where I don’t think the app needs them.
[Interview 1, pos. 71]
There is no security measure that cannot be hacked…Because otherwise you would not be able to operate it, if it was completely secure.
[Interview 14, pos. 107]
eHealth awareness does not seem to play a predominant role.
Policy and Regulation
Information security policies and the regulatory framework for data sharing pose another antecedent to privacy concerns. Participants statements included “if it is encrypted […] then I don’t see a problem” (interview 9, pos. 137), “always using the latest standard of anonymization […] and ensure transparency” (interview 14, pos. 171), and “so that no third party can access the data, but only the person that one has consented to” (interview 16, pos. 47). When sharing data for medical research, most participants wanted to stay completely anonymous. One participant, however, stated:
I would share my data with the condition that I get informed when they find something. That would be useful for me as a prophylactic measure.
[Interview 12, pos. 145]
This indicates that complete anonymity may not always be beneficial for the data owner. We analyzed data from a German health care system, implying strict regulation on information sharing and information usage, which aids participants in feeling secure when sharing PHI. A notable statement was made by a participant who is an immigrant:
If you are here in Germany and know that everything is checked and done meticulously, then I don’t have a problem [with sharing my data]. In [the country] where I am from, you don’t know what they do with the [data]. There, I would think twice about it.
[Interview 2, pos. 17]
Participants said they are comfortable sharing PHI within Germany or the European Union (EU) but are wary about sharing PHI with institutions outside the EU, as indicated, for example, by participant 16:
I would trust [institutions] within Europe.
[Interview 16, pos. 173]
Considering demographics, age was found to be the most predominant factor influencing privacy concerns. Participants stated:
So if I was 75, then I would say, I don’t care, take my data. Because I think, ok, then hackers have my data, but what are they going to do with it? But not in my current age.
[Interview 7, pos. 133]
If you would ask someone who is 20, 30, or 40, they would give a different answer because everything happens digitally for them.
[Interview 12, pos. 39]
All other factors were barely found in the interviews.
Personality and Disposition
The final antecedent was personality and disposition. In our sample, we found evidence for the importance of disposition to trust when sharing PHI. Most participants exhibited a tendency to trust and mentioned that a base level of trust is needed in all social and digital interactions. This was indicated by statements such as:
You need to have a level of trust these days, both in technology and in relation to the digital possibilities we have today.
[Interview 15, pos. 105]
Then I have to give them the benefit of the doubt, that the information is important, and that’s what you need to have in general towards a doctor and a hospital.
[Interview 3, pos. 23]
One participant, however, stated:
This is difficult. I trust myself...I don’t trust anyone. This is based on my experience.
[Interview 10, pos. 81]
Participants were aware of the benefits of actively pursuing a healthy lifestyle (patient activation), and most stated that they try to do so, succeeding to a varying extent. Some participants mentioned a (brief) use of step counters or sleep trackers. They did not relate these statements to privacy concerns. We found evidence that showed an influence of patient activation on privacy concern formation, as indicated by the statement:
Yes, you see. Then we have a yes if I am affected myself.
[Interview 10, pos. 149]
Regarding the impact of health status on privacy concerns, we found ambivalent results. Some people with chronic illnesses were skeptical about sharing their PHI or believed it is not important, while others said they would happily share their PHI. There was no evidence that health concerns such as chronic illnesses have an influence on privacy concerns.
All participants expressed some level of privacy concern. Participants had “the feeling, that my data already is everywhere anyway” (interview 5, pos. 51) and a feeling of “overstimulation due to too much information” (interview 17, pos. 105) and being unable to control it in the first place. This was fittingly expressed by participant 13:
Yes, because I always have this remaining risk that the data could be misused.
[Interview 13, pos. 143]
This was often mentioned in relation to a level of acceptance of the matter. Most participants stated the risk of “sensitive data in the wrong hands” (interview 3, pos. 151) through data leaks or hacking. Some worried about leaking illness-related PHI to employers and the resulting discrimination due to health concerns, as mentioned by participant 7:
If someone has an illness and she applies somewhere, [then] the potential employer could find that [the person] has an illness and not hire her.
[Interview 7, pos. 13]
Other participants mentioned unwanted targeted ads or discrimination.
In addition to privacy concerns, social influence also affects trust formation. Particularly, older participants actively rely on their children and grandchildren for support in IT and sharing decisions and involve them in their decision process. Participant 12, for example, stated:
Then I would ask the younger generation.
[Interview 12, pos. 57]
Stage 2: Attitude Formation
In the attitude formation stage, we differentiated between cognitive and emotional trust as well as the privacy calculus calculation.
Overall, there was a high level of cognitive trust in medical institutions, such as hospitals, and other health care and health insurance providers:
You are willing to share your data as long as you trust the institution.
[Interview 3, pos. 117]
Because I have a base level of trust in our health care system.
[Interview 8, pos. 21]
We also found that a base level of trust is created through an in-person interaction with the treating physician or the health insurance provider, as indicated by participant 2:
If my family doctors said you need to monitor your blood pressure and I would like you to use this [app]…then I would use it.
[Interview 2, pos. 79]
However, trust in Big Pharma and the general intentions of companies using health-related data was low. Participants used large platforms, such as Facebook and Google, but tended to have reservations about their data collection and usage policies. One participant stated:
The motivation of the companies to get data is high…They surely get more information than they deserve.
[Interview 1, pos. 89]
This statement displays the influence of the person’s privacy concern on trust formation. Participants did, however, trust the expertise and integrity of, for example, the apps provided by their health insurance providers, as indicated by the following statements:
If you talk about expertise, they are all competent. Generally, I would say that everything related to health insurances and sport universities would have the highest level of expertise.
[Interview 4, pos. 107]
The health insurance app is competent because I can upload my bills.
[Interview 6, pos. 151]
Participants based their emotional trust in HIEs on their general disposition to trust and previous experiences expressed within privacy concerns, as indicated by this statement:
When the health insurance said we have this app and we would like you to use it…I thought I can do that for them…I have blind faith that the [health insurance] makes sure it is safe.
[Interview 2, pos. 71]
Depending on their dispositions and experiences, some participants showed an emotional mistrust in HIEs, such as 1 participant:
I would have a feeling, I don’t know, what types of data go where, what they can tell someone about me…I think this is too risky.
[Interview 7, pos. 149]
In addition to forming a trust attitude at this stage, participants underwent a privacy calculus, comparing the utility and associated risks of sharing PHI. Participants were more likely to opt into sharing PHI if they perceived it to be beneficial (1) for their own convenience and usability of the app, (2) for their own health, or (3) if it aids a common good of advancing medical diagnosis and treatment. One participant stated clearly, “Yes, if I benefit from it,” (interview 5, pos. 123), while mentioning the common good, “So if I share this data for research purposes, then I definitely see the benefit that you can perform research with it. And that is somehow a priority for me” (interview 5, pos. 173).
Another one said:
[I’d] have a good feeling [that] things will be a bit easier…if the data is already saved.
[Interview 8, pos. 117]
… Because then you have your whole health history and all the relevant information in one spot…I believe this has a lot of potential.
[Interview 8, pos. 41]
Yet another participant stated:
I think it is very important that data is shared between the [medical] professionals.
[Interview 11, pos. 23]
Perceived risk is mainly associated with misuse of data by third parties, as indicated by participant 3:
…Sensitive data gets into the wrong hands…That would be bad for the user.
[Interview 3, pos. 151]
Stage 3: Intent Formation
In the information-sharing intent stage, participants formed their final intent toward sharing their PHI with an HIE. To assess intent, participants received a vignette (see) and were asked for their recommendation. Most participants (n=19, 95%) displayed a positive intent to share their PHI, even given the special circumstances:
Because it will be beneficial for research on this illness […] I think you should do it, even if data is stolen.
[Interview 6, pos. 227]
In addition to the deductive themes from the model, we found that participants preferred apps that are easy to use in daily life. Further, participants preferred a specific consent solution  compared to a broad consent solution.
shows the updated eHealth trust formation model based on our interview results. The figure shows which constructs are supported by evidence within our data set and which ones are currently not supported. These results should certainly be validated with further qualitative and quantitative cross-country studies.
The objective of our study was to gain deeper insight into the issue of trust in HIEs and answer the following question: How does an individual form a behavioral intent to share PHI with an HIE platform? We contributed to the discussion of trust and informed consent in digital health in the following ways: First, we synthesized the main influence factors into a complex model of trust in HIEs. Next, we verified the influence factors through a qualitative analysis using patient interviews in the German health care setting. We showed which constructs are supported by evidence within our data set and which ones are not. Since this was an exploratory study, we did not adapt the model based on our current findings.
Our results showed that most patients generally have a positive attitude toward sharing their PHI digitally through an HIE. Our model provides a new point of view on the formation of a behavioral intention to share PHI by combining key concepts of the APCO model with a belief-attitude-intention framework and research on trust and the privacy calculus. Based on the interviews, we found that patients form a privacy concern in the belief formation stage based on antecedents, which can be divided into 4 categories: (1) demographics, (2) policy and regulation, (3) previous experiences with technology and information security, and (4) an individual’s own personality and disposition to trust. We also highlighted which factors appear more important in influencing the information-sharing intent. All participants in our sample use technology and gather their own experiences with it.
In the attitude formation stage, privacy concerns and social influence lead to the formation of trust in both cognitive and emotional terms. A base level of trust is created through in-person interactions with the treating physician or the health insurance provider. Trust is then transferred to the suggested HIE for sharing PHI. This is a crucial difference compared to trust formation in an e-commerce setting, for example, without contact with a physical party in the process. In the German health care setting, patients can choose their health care provider (within time and location restrictions). They can already develop a level of cognitive trust in the health care provider before interacting with the HIE. In addition to trust, the privacy calculus influences the intent to (not) share PHI with an HIE.
We based our model on previous empirical and theoretical research. Regarding the representativeness of our results, our sample was slightly skewed and may have overrepresented women with higher education. This may be due to increased digital health literacy [, ] and an increased interest shown by this demographic in the topic [ ]. The insights gained are relevant, given the articulated need to improve the understanding of female health perceptions and behaviors [ , ].
We did not interview people under the age of 25 years, which may have impacted the results. We did, however, capture some secondary insights into their attitudes through conversations with a younger age group mentioned by the participants. All participants displayed some level of tech-savviness, which may be due to low interest of non-tech-savvy people in the research and an unwillingness to participate.
The study did not capture real-life PHI-sharing decisions but, rather, analyzed the behavioral intent. Participants may have provided socially desirable answers, which may not be in line with their final action of sharing PHI. We did, however, assume that a positive intent will eventually lead to a positive action, that is, sharing PHI for the majority of participants .
The validation was conducted based on the results of 20 interviews with patients in Germany. To generalize the results, further qualitative and quantitative cross-country validations of the model are needed.
Comparison With Prior Work
Our model provides a new point of view on the formation of a behavioral intention to share PHI by combining key concepts of research on privacy, user acceptance, and trust. With this, we address calls for a more nuanced view on the patient perspectives concerning privacy and trust . We collected data from a country that is a front-runner for approving digital treatment options with cost coverage through statutory health insurance but at the same time has a comparatively rather low level of trust and high level of technology skepticism [ ].
With our data, we confirmed previous findings [, ] that most patients generally have a positive attitude toward sharing their PHI digitally through an HIE, even in the German health care setting [ , ]. Our data indicate that age is a predictor of privacy concerns [ ]. Older participants stated that they are happy to share their PHI, which is in line with previous findings [ ]. This could relate to a lack of understanding of what the shared PHI could be used for, which is in line with studies on digital (health) literacy [ , , ]. However, it could also reflect the need to share information in order to enable a better understanding of the information for oneself [ ]. The middle-aged participants in our study exhibited a higher level of privacy concerns. Studies [ , ] have found that adolescents and young adults exhibit fewer privacy concerns, possibly due to a limited understanding of the consequences as well. Further studies are needed to better understand the impact of age on privacy concerns, particularly for the older generation.
Our results further indicated that knowledge of and previous experiences with information security and technology might play an ambivalent role in forming privacy concerns: A higher level of knowledge could, on the one hand, decrease privacy concerns, as the individual knows which measures to take to mitigate the risk of a data breach. On the other hand, it may increase the level of privacy concerns, as the individual understands how easily data breaches can occur, even with measures in place. The latter is in line with the findings of Baruh et al .
In addition, a base level of trust is created through in-person interaction with the treating physician or the health insurance provider, which is in line with previous studies [, , , ] and poses a stark difference to non-health-related information sharing, where there is rarely an in-person interaction required.
Sharing PHI through or with HIEs has the potential to significantly improve the quality of care, patient outcomes, and satisfaction and to raise efficiencies in the health care sector. Privacy concerns and trust formation are a main pillar of successful and patient-centered introduction and usage of HIEs and EHRs. In terms of the practical implications of our study, patients generally have a high level of trust toward medical institutions and tend to be willing to share their PHI, given the fulfillment of certain antecedent conditions by HIEs providers, such as information security, risk mitigation, transparency, anonymity, and a defined group of (noncommercial) users.
Offering educational measures as well as the option for specific consent  may increase patients’ trust and their intention to share PHI. Increasing patients’ knowledge appears essential in facilitating empowerment and awareness of data sovereignty, despite the potential effect on privacy concerns. Developers of HIE solutions should, along the lines of General Data Protection Regulation (GDPR) requirements, aim to educate users (both medical and nonmedical staff as well as patients) on the implications of their choices. They should enable patients to choose sharing options based on their personal knowledge and preferences. Arguably, however, implementing privacy by design and security by design in old implementations of systems proves to be more difficult compared to new applications.
HIE providers need to clearly communicate the benefits of their solutions and information security measures to both health care providers (physicians, nursing and administrative staff) and patients in terms of convenience, health benefits, and public welfare. Health care providers are key partners of HIE developers with regard to sharing PHI. This entails that creating trusting relationships with physicians and health care staff, as well as national health organizations, is essential to increase patients’ PHI sharing. Medical professionals need to be convinced that the technology provides benefits, not only for the patient and related care activities, but also for internal service provision processes entailing time and cost savings for the practitioners. Implementing digital services must facilitate care delivery rather than producing additional work for the care provider. HIE developers should integrate care providers into their service development to better adapt their product to user needs. Another strategy may be to aim for a national rollout through a governmental organization to create a base level of trust.
In terms of usability, HIE providers should aim at making it easy for health care providers and patients to access, use, and navigate their apps. This could be done by, for example, performing early usability testing and offering access through multiple operating systems. Offering (non)monetary compensation for sharing certain types of PHI with commercial parties could create an additional incentive for partaking in commercial research, which is needed to bring medication and treatments to market.
This research was funded by the German Federal Ministry of Education and Research (BMBF) within the scope of the research project Virtual Consent Assistant for Informed and Data-Sovereign Patient Consent (ViCon; funding reference number 16SV8497). We thank Ms Carla Riese, Mr Peter Haberland, and Mr Patrick Casler for their support for this paper.
Conflicts of Interest
Overview of constructs, definitions, illustrative quotes, and frequencies.XLSX File (Microsoft Excel File), 18 KB
Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist.PDF File (Adobe PDF File), 535 KB
Questionaire (translated).DOCX File , 54 KB
- Payne TH, Lovis C, Gutteridge C, Pagliari C, Natarajan S, Yong C, et al. Status of health information exchange: a comparison of six countries. J Glob Health 2019 Dec;9(2):0204279 [https://europepmc.org/abstract/MED/31673351] [CrossRef] [Medline]
- Frey S, Kerkemeyer L. Acceptance of digital health applications in non-pharmacological therapies in German statutory healthcare system: results of an online survey. Digit Health 2022;8:20552076221131142 [https://journals.sagepub.com/doi/10.1177/20552076221131142?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub 0pubmed] [CrossRef] [Medline]
- Dahlhausen F, Zinner M, Bieske L, Ehlers JP, Boehme P, Fehring L. Physicians' attitudes toward prescribable mHealth apps and implications for adoption in Germany: mixed methods study. JMIR Mhealth Uhealth 2021 Nov 23;9(11):e33012 [https://mhealth.jmir.org/2021/11/e33012/] [CrossRef] [Medline]
- Uncovska M, Freitag B, Meister S, Fehring L. Patient acceptance of prescribed and fully reimbursed mhealth apps in Germany: an UTAUT2-based online survey study. J Med Syst 2023 Jan 27;47(1):14 [https://europepmc.org/abstract/MED/36705853] [CrossRef] [Medline]
- TechnikRadar 2022: Was die Deutschen über Technik denken. acatech – Deutsche Akademie der Technikwissenschaften. 2022. URL: https://www.acatech.de/publikation/technikradar-2022/ [accessed 2023-08-08]
- Gaskell G, Gottweis H, Starkbaum J, Gerber MM, Broerse J, Gottweis U, et al. Publics and biobanks: pan-European diversity and the challenge of responsible innovation. Eur J Hum Genet 2013 Jan;21(1):14-20 [https://europepmc.org/abstract/MED/22669414] [CrossRef] [Medline]
- Alaqra AS, Fischer-Hübner S, Framner E. Enhancing privacy controls for patients via a selective authentic electronic health record exchange service: qualitative study of perspectives by medical professionals and patients. J Med Internet Res 2018 Dec 21;20(12):e10954 [http://www.jmir.org/2018/12/e10954/] [CrossRef] [Medline]
- 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 Nov 26;7(4):e14050 [https://medinform.jmir.org/2019/4/e14050/] [CrossRef] [Medline]
- Schomakers E, Lidynia C, Ziefle M. Listen to my heart? How privacy concerns shape users’ acceptance of e-Health technologies. 2019 Presented at: 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob); October 21-23, 2019; Barcelona, Spain p. 306-311 [CrossRef]
- Griebel L, Kolominsky-Rabas P, Schaller S, Siudyka J, Sierpinski R, Papapavlou D, et al. Acceptance by laypersons and medical professionals of the personalized eHealth platform, eHealthMonitor. Inform Health Soc Care 2017 Sep;42(3):232-249 [CrossRef] [Medline]
- Hassol A, Walker JM, Kidder D, Rokita K, Young D, Pierdon S, et al. Patient experiences and attitudes about access to a patient electronic health care record and linked web messaging. J Am Med Inform Assoc 2004;11(6):505-513 [http://jamia.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=15299001] [CrossRef] [Medline]
- Bile Hassan I, Murad MAA, El-Shekeil I, Liu J. Extending the UTAUT2 model with a privacy calculus model to enhance the adoption of a health information application in Malaysia. Informatics 2022 Mar 28;9(2):31 [CrossRef]
- Schomakers E, Lidynia C, Vervier LS, Calero Valdez A, Ziefle M. Applying an extended UTAUT2 model to explain user acceptance of lifestyle and therapy mobile health apps: survey study. JMIR Mhealth Uhealth 2022 Jan 18;10(1):e27095 [https://mhealth.jmir.org/2022/1/e27095/] [CrossRef] [Medline]
- Pool JK, Akhlaghpour S, Fatehi F, Burton-Jones A. Causes and impacts of personal health information (PHI) breaches: a scoping review and thematic analysis. 2019 Presented at: PACIS 2019: 23rd Pacific Asia Conference on Information Systems; July 8-12, 2019; X'ian, China [CrossRef]
- Simon SR, Evans JS, Benjamin A, Delano D, Bates DW. Patients' attitudes toward electronic health information exchange: qualitative study. J Med Internet Res 2009 Aug 06;11(3):e30 [https://www.jmir.org/2009/3/e30/] [CrossRef] [Medline]
- 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 Sep 13;19(9):e169 [http://www.jmir.org/2017/9/e169/] [CrossRef] [Medline]
- De Santis KK, Jahnel T, Sina E, Wienert J, Zeeb H. Digitization and health in Germany: cross-sectional nationwide survey. JMIR Public Health Surveill 2021 Nov 22;7(11):e32951 [https://publichealth.jmir.org/2021/11/e32951/] [CrossRef] [Medline]
- Bhuyan SS, Bailey-DeLeeuw S, Wyant DK, Chang CF. Too much or too little? How much control should patients have over EHR data. J Med Syst 2016 Jul;40(7):174 [CrossRef] [Medline]
- Ruotsalainen P, Blobel B, Pohjolainen S. Privacy and trust in eHealth: a fuzzy linguistic solution for calculating the merit of service. J Pers Med 2022 Apr 19;12(5):657 [https://www.mdpi.com/resolver?pii=jpm12050657] [CrossRef] [Medline]
- Dhopeshwarkar RV, Kern LM, O'Donnell HC, Edwards AM, Kaushal R. Health care consumers' preferences around health information exchange. Ann Fam Med 2012;10(5):428-434 [http://www.annfammed.org/cgi/pmidlookup?view=long&pmid=22966106] [CrossRef] [Medline]
- Arfi WB, Nasr IB, Kondrateva G, Hikkerova L. The role of trust in intention to use the IoT in eHealth: application of the modified UTAUT in a consumer context. Technol Forecast Soc Change 2021 Jun;167:120688 [CrossRef]
- Backhaus N. Nutzervertrauen und –erleben im Kontext technischer Systeme: Empirische Untersuchungen am Beispiel von Webseiten und Cloudspeicherdiensten. Deutsche Nationalbibliothek. 2017. URL: https://d-nb.info/1156183804/34 [accessed 2023-08-08]
- Buhr L, Schicktanz S, Nordmeyer E. Attitudes toward mobile apps for pandemic research among smartphone users in Germany: national survey. JMIR Mhealth Uhealth 2022 Jan 24;10(1):e31857 [https://mhealth.jmir.org/2022/1/e31857/] [CrossRef] [Medline]
- Koivumäki T, Pekkarinen S, Lappi M, Väisänen J, Juntunen J, Pikkarainen M. Consumer adoption of future mydata-based preventive eHealth services: an acceptance model and survey study. J Med Internet Res 2017 Dec 22;19(12):e429 [http://www.jmir.org/2017/12/e429/] [CrossRef] [Medline]
- Hutchings E, Loomes M, Butow P, Boyle FM. A systematic literature review of health consumer attitudes towards secondary use and sharing of health administrative and clinical trial data: a focus on privacy, trust, and transparency. Syst Rev 2020 Oct 09;9(1):235 [https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-020-01481-9] [CrossRef] [Medline]
- German Federal Ministry of Health. Gesetz für eine bessere Versorgung durch Digitalisierung und Innovation: Digitale-Versorgung-Gesetz (DVG). Digital Healthcare Act – DVG. 2019. URL: https://www.bundesgesundheitsministerium.de/digitale-versorgung-gesetz.html [accessed 2023-08-19]
- Zimmermann BM, Fiske A, Prainsack B, Hangel N, McLennan S, Buyx A. Early perceptions of COVID-19 contact tracing apps in German-speaking countries: comparative mixed methods study. J Med Internet Res 2021 Feb 08;23(2):e25525 [https://www.jmir.org/2021/2/e25525/] [CrossRef] [Medline]
- Sbaffi L, Rowley J. Trust and credibility in web-based health information: a review and agenda for future research. J Med Internet Res 2017 Jun 19;19(6):e218 [https://www.jmir.org/2017/6/e218/] [CrossRef] [Medline]
- S. Bhuyan S, Kim H, Isehunwa OO, Kumar N, Bhatt J, Wyant DK, et al. Privacy and security issues in mobile health: current research and future directions. Health Policy Technol 2017 Jun;6(2):188-191 [CrossRef]
- Venkatesh, Thong, Xu. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly 2012 Mar;36(1):157-178 [CrossRef]
- Abdelhamid M. Greater patient health information control to improve the sustainability of health information exchanges. J Biomed Inform 2018 Jul;83:150-158 [https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(18)30108-4] [CrossRef] [Medline]
- Smith, Dinev, Xu. Information privacy research: an interdisciplinary review. MIS Quarterly 2011;35(4):989 [CrossRef]
- 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 Nov 13;6(11):e13306 [https://mental.jmir.org/2019/11/e13306/] [CrossRef] [Medline]
- Zhang X, Liu S, Chen X, Wang L, Gao B, Zhu Q. Health information privacy concerns, antecedents, and information disclosure intention in online health communities. Inf Manag 2018 Jun;55(4):482-493 [CrossRef] [Medline]
- 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]
- Dinev T, Hart P. An extended privacy calculus model for e-commerce transactions. Inf Syst Res 2006 Mar;17(1):61-80 [CrossRef]
- Hassandoust F, Akhlaghpour S, Johnston AC. Individuals' privacy concerns and adoption of contact tracing mobile applications in a pandemic: a situational privacy calculus perspective. J Am Med Inform Assoc 2021 Mar 01;28(3):463-471 [https://europepmc.org/abstract/MED/33164077] [CrossRef] [Medline]
- Dinev T, Albano V, Xu H, D'Atri A, Hart P. Individuals’ attitudes towards electronic health records: a privacy calculus perspective. In: Gupta A, Patel VL, Greenes RA, editors. Advances in Healthcare Informatics and Analytics. New York, NY: Springer; 2016:19-50
- Hasselgren A, Hanssen Rensaa J, Kralevska K, Gligoroski D, Faxvaag A. Blockchain for increased trust in virtual health care: proof-of-concept study. J Med Internet Res 2021 Jul 30;23(7):e28496 [https://www.jmir.org/2021/7/e28496/] [CrossRef] [Medline]
- Hill RJ, Fishbein M, Ajzen I. Belief, attitude, intention and behavior: an introduction to theory and research. Contemp Sociol 1977 Mar;6(2):244 [CrossRef]
- Stone EF, Gueutal HG, Gardner DG, McClure S. A field experiment comparing information-privacy values, beliefs, and attitudes across several types of organizations. J Appl Psychol 1983 Aug;68(3):459-468 [CrossRef]
- 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 May;125:1-12 [CrossRef] [Medline]
- Culnan MJ, Bies RJ. Consumer privacy: balancing economic and justice considerations. J Soc Issues 2023 Jul;59(2):323-342 [CrossRef] [Medline]
- Box D, Pottas D. A model for information security compliant behaviour in the healthcare context. Procedia Technol 2014;16:1462-1470 [CrossRef]
- Steijn WMP, Schouten AP, Vedder AH. Why concern regarding privacy differs: the influence of age and (non-)participation on Facebook. Cyberpsychology 2016 May 01;10(1):Article 3 [CrossRef]
- Karampela M, Ouhbi S, Isomursu M. Connected health user willingness to share personal health data: questionnaire study. J Med Internet Res 2019 Nov 27;21(11):e14537 [https://www.jmir.org/2019/11/e14537/] [CrossRef] [Medline]
- Bansal G, Zahedi FM, Gefen D. Do context and personality matter? Trust and privacy concerns in disclosing private information online. Inf Manag 2016 Jan;53(1):1-21 [CrossRef]
- Ruotsalainen P, Blobel B. Health information systems in the digital health ecosystem-problems and solutions for ethics, trust and privacy. Int J Environ Res Public Health 2020 Apr 26;17(9):3006 [https://www.mdpi.com/resolver?pii=ijerph17093006] [CrossRef] [Medline]
- McKnight DH, Choudhury V, Kacmar C. Developing and validating trust measures for e-commerce: an integrative typology. Inf Syst Res 2002 Sep;13(3):334-359 [CrossRef]
- Wirtz BW, Mory L, Ullrich S. eHealth in the public sector: an empirical analysis of the acceptance of Germany's electronic health card. Public Admin 2012;90(3):642-663 [CrossRef]
- Kee HW, Knox RE. Conceptual and methodological considerations in the study of trust and suspicion. J Confl Resolut 2016 Jul 01;14(3):357-366 [CrossRef]
- Komiak SX, Benbasat I. Understanding customer trust in agent-mediated electronic commerce, web-mediated electronic commerce, and traditional commerce. Inf Technol Manag 2004 Jan;5(1/2):181-207 [CrossRef]
- Lu Y, Yang S, Chau PY, Cao Y. Dynamics between the trust transfer process and intention to use mobile payment services: a cross-environment perspective. Inf Manag 2011 Dec;48(8):393-403 [CrossRef]
- Lowry PB, Cao J, Everard A. Privacy concerns versus desire for interpersonal awareness in driving the use of self-disclosure technologies: the case of instant messaging in two cultures. J Manag Inf Syst 2014 Dec 08;27(4):163-200 [CrossRef]
- Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care 2007 Dec;19(6):349-357 [http://intqhc.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=17872937] [CrossRef] [Medline]
- Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs 2008 Apr;62(1):107-115 [CrossRef] [Medline]
- Mayring P. Qualitative content analysis. Forum Qual Soc Res 2000 Jun;1(2):Article 20 [CrossRef]
- Flick U. An Introduction to Qualitative Research. 6th Edition. Los Angeles, CA: Sage; 2018.
- Barter C, Renold E. The use of vignettes in qualitative research. Soc Res Update 1999;25(9):1-6
- Guest G, Bunce A, Johnson L. How many interviews are enough? Field Methods 2016 Jul 21;18(1):59-82 [CrossRef]
- Kuckartz U, Rädiker S. Analyzing Qualitative Data with MAXQDA. Cham: Springer International Publishing; 2019.
- McHugh ML. Interrater reliability: the kappa statistic. Biochem Med 2012:276-282 [CrossRef]
- Bird CE, Rieker PP. Gender matters: an integrated model for understanding men's and women's health. Soc Sci Med 1999 Mar;48(6):745-755 [CrossRef] [Medline]
- Mauvais-Jarvis F, Bairey Merz N, Barnes PJ, Brinton RD, Carrero J, DeMeo DL, et al. Sex and gender: modifiers of health, disease, and medicine. Lancet 2020 Aug;396(10250):565-582 [CrossRef]
- Ploug T, Holm S. Meta consent: a flexible and autonomous way of obtaining informed consent for secondary research. BMJ 2015 May 07;350:h2146 [CrossRef] [Medline]
- Umfrage: TK-Meinungspuls - so sieht Deutschland sein Gesundheitssystem. Techniker Krankenkasse. 2021. URL: https://www.tk.de/presse/themen/gesundheitssystem/meinungspuls-2021-2105216?tkcm=aaus [accessed 2023-08-08]
- Muller SHA, van Thiel GJMW, Vrana M, Mostert M, van Delden JJM. Patients' and publics' preferences for data-intensive health research governance: survey study. JMIR Hum Factors 2022 Sep 07;9(3):e36797 [https://humanfactors.jmir.org/2022/3/e36797/] [CrossRef] [Medline]
- Barth S, de Jong MD. The privacy paradox – investigating discrepancies between expressed privacy concerns and actual online behavior – a systematic literature review. Telemat Inform 2017 Nov;34(7):1038-1058 [CrossRef]
- Lesch W, Richter G, Semler S. Daten teilen für die Forschunginstellungen und Perspektiven zur Datenspende in Deutschland. In: Richter G, Loh W, Buyx A, Graf von Kielmansegg S, editors. Datenreiche Medizin und das Problem der Einwilligung. Berlin, Heidelberg: Springer; 2022.
- Nurgalieva L, Cajander, Moll J, Åhlfeldt R, Huvila I, Marchese M. 'I do not share it with others. No, it's for me, it's my care': on sharing of patient accessible electronic health records. Health Inform J 2020 Dec;26(4):2554-2567 [https://journals.sagepub.com/doi/10.1177/1460458220912559?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed] [CrossRef] [Medline]
- Park YJ. Digital literacy and privacy behavior online. Commun Res 2011 Aug 23;40(2):215-236 [CrossRef]
- Chang YS, Zhang Y, Gwizdka J. The effects of information source and eHealth literacy on consumer health information credibility evaluation behavior. Comput Hum Behav 2021 Feb;115:106629 [CrossRef]
- Adorjan M, Ricciardelli R. A new privacy paradox? Youth agentic practices of privacy management despite "nothing to hide" online. Can Rev Sociol 2019 Feb;56(1):8-29 [CrossRef] [Medline]
- Baruh L, Secinti E, Cemalcilar Z. Online privacy concerns and privacy management: a meta-analytical review. J Commun 2017 Jan 17;67(1):26-53 [CrossRef]
- Stiftung Gesundheitswissen. Trendmonitor: Wie informieren sich die Deutschen zu Gesundheitsthemen? Erste Ergebnisse der HINTS Germany-Studie. 2019. URL: https://www.stiftung-gesundheitswissen.de/sites/default/files/pdf/trendmonitor_01.pdf [accessed 2023-08-19]
|APCO: Antecedent-Privacy Concern-Outcome|
|EHR: electronic health record|
|HIE: health information exchange|
|mHealth: mobile health|
|PHI: personal health information|
|UTAUT: Unified Theory of Acceptance and Use of Technology|
|UTAUT2: Extended Unified Theory of Acceptance and Use of Technology|
Edited by T Leung; submitted 03.08.22; peer-reviewed by N Shen, R Cochran, AS Alaqra, RM Åhlfeldt; comments to author 19.12.22; revised version received 12.02.23; accepted 31.07.23; published 30.08.23Copyright
©Julia Busch-Casler, Marija Radic. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.08.2023.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.