The Importance of Relevance: Willingness to Share eHealth Data for Family Medicine Research

Color coded health data: factors related to willingness to share health information in South Asian community members in Canada

Introduction

Data unavailability poses multiple challenges in many health fields, especially within ethnic subgroups in Canada, who may be hesitant to share their health data with researchers. Since health information availability is controlled by the participant, it is important to understand the willingness to share health information by an ethnic population to increase data availability within ethnocultural communities.

Methods

We employed a qualitative descriptive approach to better understand willingness to share health information by South Asian participants and operated through a lens that considered the cultural and sociodemographic aspect of ethnocultural communities. A total of 22 in-depth interviews were conducted between March and July 2020.

Results

The results of this study show that health researchers should aim to develop a mutually beneficial information-sharing partnership with communities, with an emphasis on the ethnocultural and socio-ecological aspects of health within populations.

Conclusion

The findings support the need for culturally sensitive and respectful engagement with the community, ethically sound research practices that make participants feel comfortable in sharing their information, and an easy sharing process to share health information feasibly.

Testing the Effectiveness of an Animated Decision Aid to Improve Recruitment of Control Participants in a Case-Control Study: Web-Based Experiment

BACKGROUND

Participation in case-control studies is crucial in epidemiological research. The self-sampling bias, low response rate, and poor recruitment of population representative controls are often reported as limitations of case-control studies with limited strategies to improve participation. With greater use of web-based methods in health research, there is a further need to understand the effectiveness of different tools to enhance informed decision-making and willingness to take part in research.

OBJECTIVE

This study tests whether the inclusion of an animated decision aid in the recruitment page of a study website can increase participants' intentions to volunteer as controls.

METHODS

A total of 1425 women were included in a web-based experiment and randomized to one of two experimental conditions: one in which they were exposed to a simulated website that included the animation (animation; n=693, 48.6%), and one in which they were exposed to the simulated website without the animation (control; n=732, 51.4%). The simulated website was adapted from a real website for a case-control study, which invites people to consider taking part in a study that investigates differences in purchasing behaviors between women with and without ovarian cancer and share their loyalty card data collected through 2 high street retailers with the researchers. After exposure to the experimental manipulation, participants were asked to state (1) their intention to take part in the case-control study, (2) whether they would be willing to share their loyalty card for research, and (3) their willingness to be redirected to the real website after completing the survey. Data were assessed using ordinal and binary logistic regression, reported in percentages (%), adjusted odds ratio (AOR), and 95% confidence intervals.

RESULTS

Including the animation in the simulated website did not increase intentions to participate in the study (AOR 1.09; 95% CI 0.88-1.35) or willingness to visit the real study website after the survey (control 50.5% vs animation 52.6%, AOR 1.08; 95% CI 0.85-1.37). The animation, however, increased the participants' intentions to share the data from their loyalty cards for research in general (control 17.9% vs animation 26%; AOR 1.64; 95% CI 1.23-2.18).

CONCLUSIONS

While the results of this study indicate that the animated decision aid did not lead to greater intention to take part in our web-based case-control study, they show that they can be effective in increasing people's willingness to share sensitive data for health research.

Health Consumer Engagement, Enablement, and Empowerment in Smartphone-Enabled Home-Based Diagnostic Testing for Viral Infections: Mixed Methods Study

Background

Health consumers are increasingly taking a more substantial role in decision-making and self-care regarding their health. A range of digital technologies is available for laypeople to find, share, and generate health-related information that supports their health care processes. There is also innovation and interest in home testing enabled by smartphone technology (smartphone-supported home testing [smart HT]). However, few studies have focused on the process from initial engagement to acting on the test results, which involves multiple decisions.

Objective

This study aimed to identify and model the key factors leading to health consumers’ engagement and enablement associated with smart HT. We also explored multiple levels of health care choices resulting from health consumer empowerment and activation from smart HT use. Understanding the factors and choices associated with engagement, enablement, empowerment, and activation helps both research and practice to support the intended and optimal use of smart HT.

Methods

This study reports the findings from 2 phases of a more extensive pilot study of smart HT for viral infection. In these 2 phases, we used mixed methods (semistructured interviews and surveys) to shed light on the situated complexities of health consumers making autonomous decisions to engage with, perform, and act on smart HT, supporting the diagnostic aspects of their health care. Interview (n=31) and survey (n=282) participants underwent smart HT testing for influenza in earlier pilot phases. The survey also extended the viral infection context to include questions related to potential smart HT use for SARS-CoV-2 diagnosis.

Results

Our resulting model revealed the smart HT engagement and enablement factors, as well as choices resulting from empowerment and activation. The model included factors leading to engagement, specifically various intrinsic and extrinsic influences. Moreover, the model included various enablement factors, including the quality of smart HT and the personal capacity to perform smart HT. The model also explores various choices resulting from empowerment and activation from the perspectives of various stakeholders (public vs private) and concerning different levels of impact (personal vs distant).

Conclusions

The findings provide insight into the nuanced and complex ways health consumers make decisions to engage with and perform smart HT and how they may react to positive results in terms of public-private and personal-distant dimensions. Moreover, the study illuminates the role that providers and smart HT sources can play to better support digitally engaged health consumers in the smart HT decision process.

Factors Associated With Willingness to Share Health Information: Rapid Review

Background

To expand research and strategies to prevent disease, comprehensive and real-time data are essential. Health data are increasingly available from platforms such as pharmaceuticals, genomics, health care imaging, medical procedures, wearable devices, and internet activity. Further, health data are integrated with an individual’s sociodemographic information, medical conditions, genetics, treatments, and health care. Ultimately, health information generation and flow are controlled by the patient or participant; however, there is a lack of understanding about the factors that influence willingness to share health information. A synthesis of the current literature on the multifactorial nature of health information sharing preferences is required to understand health information exchange.

Objective

The objectives of this review are to identify peer-reviewed literature that reported factors associated with health information sharing and to organize factors into cohesive themes and present a narrative synthesis of factors related to willingness to share health information.

Methods

This review uses a rapid review methodology to gather literature regarding willingness to share health information within the context of eHealth, which includes electronic health records, personal health records, mobile health information, general health information, or information on social determinants of health. MEDLINE and Google Scholar were searched using keywords such as electronic health records AND data sharing OR sharing preference OR willingness to share. The search was limited to any population that excluded health care workers or practitioners, and the participants aged ≥18 years within the US or Canadian context. The data abstraction process using thematic analysis where any factors associated with sharing health information were highlighted and coded inductively within each article. On the basis of shared meaning, the coded factors were collated into major themes.

Results

A total of 26 research articles met our inclusion criteria and were included in the qualitative analysis. The inductive thematic coding process revealed multiple major themes related to sharing health information.

Conclusions

This review emphasized the importance of data generators’ viewpoints and the complex systems of factors that shape their decision to share health information. The themes explored in this study emphasize the importance of trust at multiple levels to develop effective information exchange partnerships. In the case of improving precision health care, addressing the factors presented here that influence willingness to share information can improve sharing capacity for individuals and allow researchers to reorient their methods to address hesitation in sharing health information.

Digital Natives' Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study

Background

Artificial intelligence (AI) has shown potential to improve diagnostics of various diseases, especially for early detection of skin cancer. Studies have yet to investigate the clear application of AI technology in clinical practice or determine the added value for younger user groups. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation of AI into clinical practice, while at the same time, representing the future generation of skin cancer screening participants.

Objective

We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile apps for skin cancer diagnostics. We evaluated preferences and relative influences of concerns, with a focus on younger age groups.

Methods

We recruited participants below 35 years of age using three social media channels—Facebook, LinkedIn, and Xing. Descriptive analysis and statistical tests were performed to evaluate participants’ attitudes toward mobile apps for skin examination. We integrated an adaptive choice-based conjoint to assess participants’ preferences. We evaluated potential concerns using maximum difference scaling.

Results

We included 728 participants in the analysis. The majority of participants (66.5%, 484/728; 95% CI 0.631-0.699) expressed a positive attitude toward the use of AI-based apps. In particular, participants residing in big cities or small towns (P=.02) and individuals that were familiar with the use of health or fitness apps (P=.02) were significantly more open to mobile diagnostic systems. Hierarchical Bayes estimation of the preferences of participants with a positive attitude (n=484) revealed that the use of mobile apps as an assistance system was preferred. Participants ruled out app versions with an accuracy of ≤65%, apps using data storage without encryption, and systems that did not provide background information about the decision-making process. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information in the decision-making process. Maximum difference scaling analysis for the negative-minded participant group (n=244) showed that data security, insufficient trust in the app, and lack of personal interaction represented the dominant concerns with respect to app use.

Conclusions

The majority of potential future users below 35 years of age were ready to accept AI-based diagnostic solutions for early detection of skin cancer. However, for translation into clinical practice, the participants’ demands for increased transparency and explainability of AI-based tools seem to be critical. Altogether, digital natives between 18 and 24 years and between 25 and 34 years of age expressed similar preferences and concerns when compared both to each other and to results obtained by previous studies that included other age groups.

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma.

Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire.

Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91–0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85–0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35–0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92–0.97] to its use as assistance system for physicians. In sub-group analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians.

Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.