The trend of tracking health and well-being using digital technologies has permeated mainstream culture. The real-time monitoring capabilities, interactive decision-support algorithms and diagnostic testing features of digital health devices have drawn the interest of users everywhere, including the Global South.
Applying tools such as predictive analytics and prescriptive analytics has benefited businesses such as insurance companies and health-care providers. It has also led to unequal treatment and discrimination of individuals as consumers and recipients of health-care services, and to ill-advised decision making by clinicians and policy makers.
Our research, which was undertaken with participants largely from North America, has investigated attitudes towards the sharing of personal health data with various stakeholders within the wider health sector. It also explores alternative data approaches which could mitigate marginalization and exclusion.
Big data and discrimination
Personal health data collected through digital devices like glucose monitors and personal health trackers (such as Fitbit) and mobile devices are naturally small data. When this data become aggregated into big data sets, they also become highly valuable and profitable as knowledge resources.
Discrimination based on health data collected from self-tracking apps is rampant. Already, health information is being combined with other types of personal data to make population-wide inferences and correlations that carry value on the market of patient health data.
How does this work? Imagine yours —and other people’s —personal health data pooled together by data brokers and processed using machine learning algorithms to identify patterns and conditions that affect the overall health across groups. These patterns are then used to predict health risks and health-care costs. Combined with the data of purchasing habits, personal health data are used by insurance companies to offer customers personalized, data-driven, dynamic prices. Processed using predictive analytics, the same data could be used as the basis for health prediction discrimination.
These unfair practices further discriminate against people who are not viewed as profitable. Data-driven profiling on the basis of discriminatory attributes related to group membership such as race, gender or sexual orientation has the potential to perpetuate historical data marginalization and existing disparities by excluding vulnerable groups from health care.
Sharing personal health data
Our research explored individuals’ attitudes towards sharing of their personal health and well-being data with stakeholders within the health ecosystem. Three distinct groups of stakeholders were identified. Participants were most willing to share their personal health and well-being data with their doctors who directly provide them with pertinent services. This was followed by their families and friends, who shared high social proximity with them. The latter also reflects the motivation of sharing information for social sense-making and support.
The participants show least willingness to share data with entities within the wider health sector, such as pharmaceutical companies, the national statistics offices and multilateral health organizations such as the World Health Organization. Sharing personal health data could inform the monitoring of specific health indicators and contribute to reporting on national health and well-being. Subsequently, it could lead to the development of health interventions and policies.
We found that concerns about personal data privacy, data ownership and personal benefit affected participants’ willingness to share their personal data.
Human-centered approaches
The potential harms of the increased application of big data analytics tools and to personal health data need to be addressed. On the other hand, there is an increasing need for greater participation of individuals as data producers and users to better understand social phenomena related to health and disease. The use of data collected from individuals, however, needs to be grounded in the understanding of individuals’ preferences, human rights principles and ethical standards.
Preserving individual privacy and providing protection from potential discrimination based on sensitive health data requires putting fair, accountable and transparent algorithms in place. It also requires regulations which limit data use that might cause harm to certain individuals or groups (e.g. the use of health data to increase premiums in insurance or to deny access from specific health services).
Apps that track an individual’s personal health data need to be transparent in the ways they gather data, what they do with it and who they provide it to. Guaranteeing privacy preservation will help to foster trust among users. Users should be informed of when, how and why their data is being used, as well as of the risks associated with the external use of the data, such as data breaches. Users should also retain control over their data, with the option to opt out and to request for their data to be deleted.
Small versus big data
Alternative data approaches, including the use of Small Data could mitigate the limitations of data approaches which rely heavily on Big Data analytics. As an approach to data processing, Small Data centers the individual in collecting, analyzing and applying personal data.
Using Small Data approaches means the sociocultural context from which data is collected is considered, enabling a detailed understanding of causal relations of health and well-being problems.
Personal health data can be used to address health inequalities or disparities in quality of life. For example, personal well-being data, when pooled, can demonstrate how physiological stress is tied to societal norms and pressures rather than to individual weaknesses. A Small Data approach provides ways for the collection, analysis and application of personal health data which works towards giving individuals more meaning and insights of their data “through looking closely at others who are like us.”
Small data policies
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