Health data is an essential tool for modern medicine, and it is becoming increasingly digitized and integrated into electronic health records and other health information systems. However, the collection, use, and sharing of personal health data can have significant implications for patient privacy and autonomy.
Health
data is different from other forms of personally identifiable information
because it is often considered to be sensitive personal information. Health
data can include information about an individual's physical or mental health,
medical history, and genetic information. This type of information is
considered private and personal, and individuals may be hesitant to share it
due to concerns about privacy and discrimination.
Furthermore,
health data is often subject to specific legal and ethical requirements for its
collection, use, and disclosure. For example, in many countries, there are laws
that govern how health data can be collected, stored, and shared, such as the
Health Insurance Portability and Accountability Act (HIPAA) in the United
States. These laws are in place to protect the privacy and confidentiality of
health information and ensure that individuals have control over how their
health data is used.
Let us delve deeper…
Before,
health data was mostly limited to analogue medical records, which were the only
way to determine an individual's health. Patients were more likely to be
informed in such circumstances of what data about them was being gathered by
medical personnel and what was being done with this data for their treatment.
Analogue health data has historically gone through the stages of the life
cycle. For example, in order to limit the TB pandemic in India from the
mid-1980s, data has been collected on a large scale, pooled, and analyzed.
Yet,
in the age of big data, broader ways of data collecting are fueled,
particularly by allowing the acquisition of data that may not necessarily
reflect the individual’s health. An early example of this was the use of proxy
data regarding socioeconomic conditions to predict health. In this regard,
health data can now include both clinical and non-clinical information.
Clinical health data encompasses all information gathered during a clinical
encounter within traditional clinical boundaries, such as lab test results,
patient diagnosis, and so on. Self-reported health data, data from wearable
devices (step count, sleep patterns), environmental data (air quality), social
media data (user posts), behavioral data (smoking, alcohol usage, food, drugs,
sexual history), and socioeconomic data are examples of non-clinical health
data.
Other
types of data, such as a person's buying history, driving habits, and so on,
may also enter the picture. Despite such data points are not obvious indicators
of health, given the increasingly networked databases in which health data is
maintained, this data can enter health databases as well. As a result, the
lines between what types of data may be used to forecast an individual's health
have blurred.
With
the development of novel, non-clinical determinants of health, our digital
phenotypes have now expanded to incorporate data from sources other than our
immediate bodies, such as social and environmental variables. Some researchers
say that in today's digitized environments, health data may be described as any
data linked to health and well-being that is pervasive, complete, personalized,
and measurement-based, but others contend that 'all data is health data.' What
kind of data may be collected to forecast a person's health is one of the key
developments that has resulted from the use of big data in healthcare and the
concomitant disembodiment of health data.
To
a limited extent, laws governing the NDHM ecosystem recognize some non-clinical
data as health data, such as fitness tracker data. However, current policies make no attempt to address the
challenges that arise as a result of different types of health data being
treated differently within broader policy frameworks. In India, for example,
most fitness trackers are not categorized as medical devices and hence are not
subject to the same restrictions as medical devices, despite the fact that the
data they gather is used to forecast a person's health. These rules are
primarily intended to govern the manufacturing, importation, sale, and
distribution of medical equipment in the nation. This creates a governance gap,
and many novel health data applications are in danger of falling through it.
In
conclusion, personal health data is a valuable resource for modern medicine,
but it must be collected, used, and shared in a way that prioritizes patient
autonomy, privacy, and equity.
References
used,
Ada
Lovelace Institute. (2020). The data will see you now: Datafication and the
boundaries of health. Ada Lovelace Institute.
Ajana,
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Golembiewski,
E., Allen, K. S., Blackmon, A. M., Hinrichs, R. J., & Vest, J. R. (2019).
Combining nonclinical determinants of health and clinical data for research and
evaluation: rapid review. JMIR public health and surveillance, 5(4), e12846.
Kovacs,
Anja, & Jain, Tripti. (2020) Informed Consent - Said Who? A Feminist
Perspective on Principles of Consent in the Age of Embodied Data. Mumbai, Data
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Nissenbaum
H, Patterson H. (2016). Biosensing in context: health privacy in a connected
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