Health Insurers: Relationship-Specific Investments For Tailored Benefits

Health insurance companies make money by charging premiums and investing the insurance premium payments. They generate revenue in two ways: charging premiums in exchange for insurance coverage, and then reinvesting those premiums into interest-generating assets. Insurers also diversify risk by pooling the risk from customers and redistributing it across a larger portfolio.

Insurers use relationship-specific investment to maximise their profits. They do this by collecting personal data on their clients, such as race, marital status, TV habits, and social media activity, and using it to predict their health costs. This information is then fed into algorithms that predict how much a client's healthcare could cost them. Insurers can then use this information to adjust their premiums accordingly.

Health insurers use relationship-specific investment to determine health insurance premiums based on an individual's risk factors

Health insurance companies base their business models on assuming the financial risk of a covered event on behalf of an individual. They generate revenue by charging premiums in exchange for insurance coverage and then reinvesting those premiums into interest-generating assets. Insurers also diversify risk by pooling the risk from customers and redistributing it across a larger portfolio.

A key task for insurers is to price the risk of an event occurring and charge an appropriate premium for assuming that risk. This analysis is called underwriting. If an insurance company offers a policy with a conditional payout, it must assess the risk that the policy might be triggered and a claim payout occurs. From there, the insurer must determine the level of risk it is willing to assume and the premium amount to charge the customer.

Health insurance premiums are determined based on several factors, including age, sex, location, health status, and coverage levels. The premium is the price of the policy, typically charged as a monthly cost. The policy limit is the maximum amount an insurer will pay for a covered loss under a policy, and the deductible is a specific amount the policyholder must pay out of pocket before the insurer pays a claim.

In recent years, health insurers have come under scrutiny for using personal data to predict health costs and set premiums. Insurers and data brokers use data such as race, marital status, TV watching habits, and online shopping habits to predict health costs. This practice has raised concerns about privacy, accuracy, and potential discrimination.

They collect data on race, marital status, TV habits, and social media activity to predict healthcare costs

Health insurance companies collect data on race, marital status, TV habits, and social media activity to predict healthcare costs. This data is used to feed algorithms that predict how much a person's healthcare could cost the insurance company. This data is collected with little to no public scrutiny and, according to patient advocates and privacy scholars, runs counter to the insurance industry's allegiance to patients' medical privacy.

Race is one of the data points used by health insurance companies to predict healthcare costs. While the collection of data on race by health insurance companies is not common and not standardised, it is still used by some companies. For example, the UnitedHealth Group has collected the socioeconomic data, including race, of 150 million Americans going back to 1993.

Marital status is another data point used by health insurance companies to predict healthcare costs. While it is commonly collected as part of an intake to a healthcare facility, it has the potential to change over time and may not be updated in a timely manner. Natural language processing techniques can be used to extract marital status from clinical notes with high accuracy.

TV habits are also collected by health insurance companies to predict healthcare costs. This information can be used to identify social and economic factors that may impact a person's health. For example, a person who watches a lot of TV may be at a higher risk of health issues such as obesity or heart disease.

Social media activity is another data point used by health insurance companies to predict healthcare costs. Companies such as Optum have filed patent applications to gather what people share on social media platforms and link this material to the person's clinical and payment information. This information can be used to identify potential health risks and predict healthcare costs.

This data is used to create algorithms that predict health risks and set insurance prices

Health insurance companies use personal data to create algorithms that predict health risks and set insurance prices. This data includes race, marital status, TV habits, education level, and social media activity. Insurers use this information to identify potential health issues in their clients and offer them appropriate services. While companies claim that this data is not used to set prices, there is evidence that it is being used to discriminate against certain groups, such as low-income minorities and people with mental health issues.

Insurers also take into account factors such as age, sex, location, health status, and coverage levels when setting health insurance premiums. The premium is the price of the policy, usually paid monthly. A higher premium is often charged to those with a higher risk of making a claim. For example, an individual with a history of reckless driving and several expensive cars will likely pay more for an auto policy than someone with a safe driving record and a cheaper car.

The use of personal data by health insurance companies is controversial, as it raises concerns about privacy and the potential for discrimination. Patient advocates argue that using unverified and error-prone "lifestyle" data to make medical assumptions could lead to improper pricing and discrimination. Additionally, the Health Insurance Portability and Accountability Act (HIPAA) only protects medical information, leaving personal data vulnerable to being used by insurers.

Health insurers use relationship-specific investment to diversify their business and increase growth

Health insurance companies, like other insurance companies, make money by charging premiums and investing the premium payments. They also diversify their business and increase growth by investing in other areas.

UnitedHealth Group, for example, owns Optum, which provides information- and technology-enabled health services, including OptumRx pharmacy benefits management (PBM) services. Optum is a major growth driver for UnitedHealth Group, and it plans to acquire the home health provider LHC Group for $5.4 billion, which will further diversify its business.

CVS Health is another example of a health insurance company that has diversified its business. In addition to its health insurance business, CVS Health runs CVS Caremark, one of the largest pharmacy benefits management companies in the country.

Health insurance companies also diversify by investing premium payments in interest-generating assets, such as Treasury bonds, high-grade corporate bonds, high-yield savings accounts, and certificates of deposit (CDs). This helps them generate income and increase growth.

By diversifying their business and investing in interest-generating assets, health insurance companies can reduce risk, increase growth, and better serve their customers.

They can also use the data to identify patients who need help with medication or transportation to medical appointments

Health insurance companies are increasingly using data analysis to identify patients who may need additional support. By collecting and analyzing data on patients' socioeconomic characteristics and behaviours, insurers can predict health risks and costs. This information can then be used to offer targeted interventions and support to those who need it most.

For example, patients who have recently changed their name may be flagged as newly married and offered support to manage the costs of a potential pregnancy. Similarly, patients who are identified as low-income or minorities may be offered additional support, as data suggests they are more likely to live in unsafe neighbourhoods with increased health risks.

In addition to demographic information, health insurers also analyze patients' social media presence and online shopping habits. This allows them to identify patients who may be at risk of mental health issues, such as depression, and offer them the necessary support and resources. For instance, a woman who purchases plus-size clothing may be considered at risk for depression and offered access to mental health services.

While some may argue that this use of data is an invasion of privacy, health insurers maintain that it is necessary to spot health issues in their clients and ensure they receive the services they need. By analyzing data on patients' lifestyles and behaviours, insurers can identify those who may need help with medication adherence or transportation to medical appointments. This proactive approach can improve patient outcomes and reduce overall healthcare costs.

Frequently asked questions