Data brokers for health care

Data brokers are tasked collecting data from people, building a particular type of profile on that person, and selling it to companies (Angwin, 2014; Beckett, 2014; Tsesis, 2014). The data brokers main mission is to collect data and drop down the barriers of geographic location, cognitive or cultural gaps, different professions, or parties that don’t trust each other (Long, Cunningham, & Braithwaite, 2013). The danger of collecting this data from people can raise the incidents of discrimination based on race or income directly or indirectly (Beckett, 2014).  Some prominent data brokers are Acxiom, Google, and LexisNexis (Tsesis, 2014).

When it comes to companies, it is unknown which data is being collected from people and sold to other companies (Beckett, 2014).  According to Tsesis (2014), the fourth amendment of the constitution does not apply here, due to the nature on how the data is collected and correlated by data mining of third party entities.  So what kind of data do they have?  Current data brokers do have data obtained from company’s people shop at or credit/loans applied for like: names, address, contact info, demographics, occupation, education level, parents’ names, children’s names, gender of the person’s children, hobbies, purchases, salary, and other data that is unknown (Beckett, 2014).

Sensitive data is protected from these commercial data brokers, like medical records, doctor-patient conversations (Beckett, 2014). This is due to HIPAA (Health Insurance Portability and Accountability Act of 1996), which helps de-identify patient data. However, there is always ways around this.  Companies and health insurances are buying online search data, allergy data, dieting data and are correlating it other data to build a health profile on the person (Beckett, 2014). There is a benefit of having in-house data brokers in hospitals where data is stored in silos (Long et al., 2013):

  • Brokers can bring specialized subject matter expertise to connect distributed data for improving patient care and improve healthcare service efficiency.
  • Brokers can help reduce redundant data held in silos.
  • Brokers can increase access to heterogeneous knowledge, though gathering and increasing tacit knowledge. This type of knowledge is derived from a different groups or networks thus the knowledge is a different source of new information.
  • Brokers efforts can help generate innovations.

However, data collected and correlated by data brokers could still be completely wrong as proven with the credit score information from the three big credit agencies (Angwin, 2014; Beckett, 2014). If the data is wrong in totality or partially, it can draw the wrong conclusions on the person and if it is used for discrimination just compounds the problem.  Long et al. (2013), concluded that brokers even in the field of healthcare are an expensive endeavor and as the primary gatekeeper of data they could be overwhelmed. Angwin (2014), compiled a list of 212 commercial data brokers, with about 92 of them allowing for opting-out.  Tsesis (2014), stated that many scholars in the field of privacy are advocating for a person’s right to opt-out of data they did not give consent to be collected. This suggests that with the current law and regulations, data collection and correlation to a person’s profile is currently unavoidable, yet there is a chance that some of that data is wrong, to begin with.

References

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