Afterlives of Data by Ebeling Mary F. E.;

Author:Ebeling, Mary F. E.;
Language: eng
Format: epub
Publisher: University of California Press

ALL DATA ARE CREDIT (AND HEALTH) DATA

The three largest credit bureaus make inferences about a person’s health based on data contained within the bureaus’ own financial information products. Customer car ownership, fast food purchases, medical debt, financing, and credit use for healthcare are types of information embedded within these data products (Wei et al. 2016). Analysts make inferences about a person’s class or access to healthy food based on consumer and credit data, and these insights are sold to healthcare providers to make better sense of a patient’s social context and what it might mean for their health. But this also means that a patient is no longer solely under the medical industry’s gaze, but is made legible and subject to marketing and the debt society’s surveillance (Lazzarato 2015; Waldby 1998). Without a credit history score, like the FICO score, or with a “bad” score, patients face further marginalization in healthcare settings because of their debt, purchasing behaviors, or even their financial and real assets.12 For those whom the healthcare or banking industries already marginalize due to their immigration status, gender, race, or class, this profiling can have dire consequences for their health and negatively impact virtually all aspects of their lives.

In the hands of commercial interests, all data can be used to make inferences about health, even in the context of a covered entity that would be considered private. Patient health information produced in a doctor’s office and protected under HIPAA can be exactly the same data collected from our smartphones but not protected under HIPAA. The privacy of data is thus context dependent (Nissenbaum 2011; Scholz 2016). Information that may be regulated for privacy in one setting is fair game in another (Schwartz 2009). Previously, with paper-based healthcare and records systems, these contexts were easier to control, ensuring that patient information produced in a doctor’s office would not be (easily) reidentifiable in other, nonmedical contexts. But as data now swims in a vast digital ocean, these contextual boundaries have virtually disappeared.

The consumer profiles that credit bureaus hold will undoubtedly include data about an individual’s health, types of medical services obtained, and the amount of medical debt that is in collections after 180 days of nonpayment. For patients dealing with expensive medical diagnoses, such as cancer, these debts can reach the hundreds of thousands of dollars and will be reflected in the score, resulting in what is called medical financial toxicity. Since this information is about a patient’s medical debt burden, it is still considered to be traditional financial information. But by utilizing “fringe alternative data,” from consumer, social media, and web tracking sources, credit bureaus score customers through the application of behavioral predictive analytics, and they can make inferences about a person’s health and medical information, either based on HIPAA-regulated information or through consumer data gleaned outside of HIPAA protections (Robinson + Yu 2014).

Since credit bureaus hold massive amounts of personal identifying and nonfinancial data on most Americans, virtually all the identifiers that constitute PHI overlap with data the credit reporting companies hold.

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