Do Health Insurance Companies Use Personal Information to Set Rates?
Health & Safety | health insurance | Insurance | Insurance Claims
By Adler Giersch Staff
August 13, 2018
An ongoing investigation by NPR and ProPublica has raised troubling questions about some of the tactics used by health insurance companies to maximize profits. Over the past several years, the health insurance industry, wholly funded by consumers through taxes and insurance premiums, have begun partnering with data brokers and marketing organizations to gather personal details about hundreds of millions of Americans, which could be used to determine insurance premium rates and coverage plans.
Information being collected by the health insurance industry includes a person’s education level, marital status, race, gender, place of residence, net worth, credit history, TV watching habits, data stored on fitness tracking devices, medical history, prescription drug use and criminal history. This information is available in the publicdomain and not protected by privacy laws such as the Health Insurance Portability and Accountability Act (HIPAA), which only protects medical information. Insurance companies and data brokers are harvesting personal data through many sources, including electronic medical records, social media and internet searches, court filings, property records, questionnaires, marketing surveys, and a wide array of applications completed by consumers (think: bank loan or credit card applications, college admissions applications, and utility accounts), unless a specific law prohibits access to it.
Insurers say they use this information to identify health issues in their policy holders, so they can help them get the care they need. But it seems far more likely that these companies use the information to set premium rates and limit the types of coverage offered to what they consider “high risk” individuals who are more likely to run up medical bills. Also concerning is the possibility that using unconfirmed, error-prone lifestyle data points could lead to inaccurate assumptions and result in unfairly priced policies.
From our standpoint, drawing conclusions from data that is wholly unrelated to a person’s actual health will lead to bias and inequitable pricing, and will result in vulnerable groups being charged more for much-needed insurance coverage, creating even more of a barrier to access to medical care to those who really need it.
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