When individuals become legible to private actors who can access massive amounts of personal data about their lives, it changes how people’s health can have value, and who can benefit from that value. A major incentive for datafication is to collect personal data of people to gain granular insights into their lives and experiences and make profitable predictions about them. This is possible because our personal data contains insights into who we are, what we do, and what we want (Greenwood, 2014).
Health insurers use personal data to sort, rank, and differentially charge their customers for services. Consider Kranti’s use case of Max Bupa’s collaboration with GOQii to determine insurance premiums. This is not a singular case. HDFC Ergo8 has a “Stay Active” scheme that encourages policyholders to increase the number of steps they take every day by rewarding them with discounts on their policy premium at the time of renewal, based on the average daily step count they record on their Health Jinn App throughout the policy year (“Stay Active,” n.d.). Aditya Birla uses a health assessment to assign a Healthy Heart Score to policyholders, wherein they are categorised as Red, Amber, and Green to receive discounts on premiums, based on their probability of developing heart disease (Aditya Birla Health Insurance Company Pvt. Ltd., 2016). This may not yet be a popular model in India, but the NDHM infrastructure incentivises more takers for it.
This is an excerpt from a fieldwork-based policy study by Radhika Radhakrishnan titled “Health Data as Wealth: Understanding Patient Rights in India within a Digital Ecosystem through a Feminist Approach” which can be downloaded at this link.
In these examples, proxy data such as step counts and sleep patterns are used to singularly predict a person’s health, and consequently their insurance premiums, even though there is no evidence to suggest that people who take a certain number of steps a day are less likely to make an insurance claim. The low-cost mass-market tracker devices and apps used for this data collection further put into question the accuracy of the data collected in this manner. While the insurer doesn’t directly raise premium rates, less ‘healthy’ policyholders who do not meet their targets on the app (or may not even be able to afford using a smartphone app) end up paying more than their ‘healthier’ counterparts, with health here being defined by flawed data proxies.
This affects not only individuals but also their families. In Kranti’s case, she is not herself diabetic but has a family history of diabetes, as mentioned. Because health insurance coverage is often offered at the level of families in India, insurers have a business interest in mining data about the health of families. Though insulin for diabetes is itself not covered by health insurance in India, related health conditions such as hypertension, retinopathy or diabetic foot ulcers, are covered. In Kranti’s case, insurers would now be able to predict if she is likely to get diabetes because of Kranti’s PHRs which would be linked to her family’s PHRs in the NDHM ecosystem. This would be possible even without Kranti’s explicit consent. During fieldwork, I observed that when an individual is enrolled for a Health ID, they are asked for the details of their entire family so that their families can also be enrolled, as evidenced in the following conversation with Ms. Narima, an ANM worker in a civic dispensary in Chandigarh (translated from Hindi):
[Ms. Narima]: Everyone in the family won’t come [for Health ID registration]. One family member comes with the date of birth of all members and with one family phone.
[Me]: So then you make IDs for the full family through that one person and one phone?
[Ms. Narima]: Yes.
In this way, a person’s health data gets linked to their family’s health data through names, phone numbers, addresses, or other common identifiers, irrespective of whether the family has consented to it, or is even aware of it. With all of this interlinked data available to insurers under the NDHM, they are in a powerful position to access health records of people who have not consented to share this data.
The excerpt has been posted with permission from the author who carried out the research during her employment at The Internet Democracy Project as a Researcher.
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