Health risk assessments (HRAs) are a source of valuable data for health and wellness professionals looking to understand—and improve—the health of the population they serve.
How does an HRA gather information? By going directly to the individual. After all, only an individual will know certain details of their health habits and lifestyle such as how stressed they feel, how many hours they sleep, or whether they eat a lot of red meat.
Let’s dig into value of self-reported data, and the practices you can put in place to ensure the data your HRA collects is valid and reliable.
What is self-reported data?
Self-reported data is collected when individuals directly respond to questions about their own health status and behaviors. HRAs collect this type of data.
Self-reported data complements biometric or clinical data a healthcare provider may gather. And while the empirical data that clinicians obtain—such as blood pressure or lab results—are good indicators of a person’s current health, only the individual can provide details on other aspects of their wellbeing, such as their mental health status or lifestyle habits that play a significant role in their future health. This type of data is not as easily available in medical charts or claims data. You need self-reported data.
How reliable is self-reported data?
It’s important to acknowledge that all data, whether it’s collected from an individual or not, may contain bias or errors. When you’re asking people about their health habits, there is room for bias, especially when that person may be concerned about how their answers will be used. There is also room for error in personal responses—how accurately do you think you could represent the amount of exercise you get in the past month?
But if you overlook this type of data, you lose out on a critical dataset in your arsenal that can give you additional insights and layers of understanding when it comes to your population’s health. It’s also one of the only reliable ways to collect lifestyle information, which is critical to understanding not only a person’s history of health, but their future health.
How can an HRA minimize bias when collecting quality, reliable self-reported data?
There are many ways an HRA will account for bias or errors in self-reported data. A high-quality HRA is able to increase the accuracy of the data it collects with the way questions are worded, the way the HRA is administered, even in how the HRA is promoted. Here are few good places to start:
- Address social desirability bias. This is the tendency of survey respondents to answer questions in a way that is viewed favorably by others. An example: HRA participants may be worried that their responses may disqualify them from healthcare coverage. Minimize this type of bias by providing participants a safe, private environment to complete the assessment, assure them that the data is kept secure, inform them of how the data will be used and shared, and let them know their responses are protected by law.
- Address recall issues. Consider how far back you’re asking respondents to recall information that pertains to a large stretch of time. Asking about a health or lifestyle habit over a long period of time—a year, for example—yields less reliable than asking them to recall something that happened last week. In fact, one study found that a recall duration of one month or less holds about a 90% accuracy rate.
- Avoid assessment fatigue. A quality HRA will be designed to ask the minimum number of questions in the simplest way possible. For example, Wellsource HRAs use dynamic branching logic, so they are only shown questions that align with their health and lifestyle based on their answers to previous questions.
What’s the bottom line? Self-reported data gives valuable insights into population health—if you know how to best collect and analyze it.
Gathering quality health and lifestyle data requires self-reported data in one way or another. The trick is to recognize the limits of this type of data and take measures to minimize bias and error. A high-quality HRA will help bring greater objectivity to self-reported data.
Learn more about this topic in our most recent guide, “Value of Self-Reported Population Health Data: Tips for Gathering Reliable, Actionable Insights.”
