Medical Testing Sensitivity and Specificity and Examples

In healthcare, sensitivity and specificity are terms used to describe how accurate a test is. A highly sensitive test is less likely to return a false negative result; a highly specific test is less likely to return a false positive result. These terms may be used to describe how confident your healthcare provider is in your test results.

Learn about sensitivity and specificity and how they are used to select appropriate testing and interpret the results.

Sensitivity and Specificity in Medical Tests

Medical testing is an important tool that helps healthcare providers diagnose health conditions. Sometimes there is more than one potential diagnosis, and a medical test can help narrow down the possibilities.

Screening tests are another type of medical test that can help identify diseases that you may be at higher risk of developing. They are not done to diagnose a symptomatic illness, but to find a health condition that may not yet be producing symptoms.

The selection of these tests may rely on the concepts of sensitivity and specificity, which is a way to describe how accurate they are at confirming the suspected diagnosis.

• Sensitivity indicates how likely a test is to detect a condition when it is actually present in a patient. A test with low sensitivity can be thought of as being too cautious in finding a positive result, meaning it will err on the side of failing to identify a disease in a sick person. When a test’s sensitivity is high, it is less likely to give a false negative. In a test with high sensitivity, a positive is positive.
• Specificity refers to the ability of a test to rule out the presence of a disease in someone who does not have it. In other words, in a test with high specificity, a negative is negative. A test with low specificity can be thought of as being too eager to find a positive result, even when it is not present, and may give a high number of false positives. This could result in a test saying that a healthy person has a disease, even when it is not actually present. The higher a test’s specificity, the less often it will incorrectly find a result it is not supposed to. 
  

The decision to use one test over another requires careful consideration of both sensitivity and specificity. This helps healthcare providers and patients make the best decisions about testing and treatment.

Why Is Sensitivity and Specificity Important?

Not every test is useful for diagnosing a disease. A healthcare provider must carefully select the most appropriate test for an individual based on specific risk factors.

Choosing the wrong test may be useless, and a waste of time and money. It may even lead to a false positive test, suggesting a disease that is not actually present.

Healthcare professionals take into consideration the strengths and weaknesses of tests. They try to avoid any choices that might lead to the wrong treatment.

For example, in diagnosing someone with cancer, it may be important not only to have an image that suggests the presence of the disease, but a tissue sample that helps to identify the characteristics of the tumor so the right chemotherapy may be used. It would be inappropriate to solely depend on a single test that is not accurate in identifying the presence of cancer, and then start a treatment that may not really be needed.

In situations where one test is less than certain, multiple tests may be used to increase the confidence of a diagnosis.

It may seem logical that both a false negative and false positive should be avoided. If the presence of a disease is missed, treatment may be delayed and real harm may result. If someone is told they have a disease that they do not the psychological and physical toll may be significant.

It would be best if a test had both a high sensitivity and a high specificity. Unfortunately, not all tests are perfect. It can be necessary to find a balance that matches the purpose of the testing to the individual being evaluated.

Comparing Tests

The best test (or group of tests) for diagnosing a disease is called the gold standard. This may consist of the most comprehensive and accurate testing or measurements available.

When new tests are developed in research, they will be compared to the best available testing currently in use. Before being released for wider use in the medical community, the new test’s sensitivity and specificity are derived by comparing the new test’s results to the gold standard.

In some cases, the purpose of the test is to confirm the diagnosis, but some testing is also used more widely to identify people at risk for specific medical conditions.

Screening takes place when a medical test is given to a large population of patients, with or without current symptoms, who may be at risk for developing a specific disease. Some examples of proposed screening tests for potential medical conditions include but are not limited to the below:

• Breast cancer (mammography)
• Prostate cancer (prostate-specific antigen or PSA)
• High cholesterol (cholesterol panel)
• Cervical cancer (Pap smear)

Not everyone needs to be screened for colon cancer at a young age, but someone with a specific genetic condition or a strong family history may require the evaluation. It is expensive, and somewhat invasive, to do the testing. The test itself may have certain risks.

It is important to strike a balance between selecting the appropriate person to be tested, based on their risk factors and relative likelihood of having the disease, and the utility of the testing available.

Everyone is not tested for every disease. A skilled clinician will understand the pre-test probability of a specific measurement or the likelihood that a test will have an anticipated result.

Positive and Negative Predictive Value

It is appropriate for healthcare providers to consider the risks of a disease within an untested group through the lens of two additional considerations: PPV and NPV. 

Positive predictive value (PPV) is the number of correct positive results of a test divided by the total number of positive results (including false positives). A PPV of 80% would mean that eight in 10 positive results would accurately represent the presence of the disease (so-called “true positives”) with the remaining two representing “false positives.”

Negative predictive value (NPV) is the number of correct negative results a test gives divided by the total number of negative results (including false negatives). An NPV of 70% would mean that seven in 10 negative results would accurately represent the absence of the disease (“true negatives”) and the other three results would represent “false negatives,” meaning the person had the disease but the test missed diagnosing it.

PPV and NPV, combined with the frequency of a disease in the general population, offer predictions about what a broad-scale screening program might look like. 

Sensitivity and Specificity Examples

Some tests are very good at identifying certain diseases and conditions, while others are better at ruling them out. For example:

• Digital mammography has a sensitivity of 97% and a specificity of 64.5%. This means that the test will identify breast cancer in almost everyone who has it, but it may also falsely identify breast cancer in a significant number of people.
• A HbA1c test for diabetes has 32% sensitivity and 94% specificity. This means it is not very good at identifying diabetes but it is very good at ruling it out.
  

Summary

Knowing the strengths of different tests is useful for effectively identifying a disease. If a patient might have a life-threatening condition, or their potential illness has a critical window to act, it can be hard to balance the factors of timeliness, accuracy, and cost of testing.

Those who are early in medical training may not have developed the experience and skill to select appropriate testing, and this can lead to a reactive urge to over-test in order to not miss a diagnosis.

Unfortunately, the wrong test may lead down a path toward additional testing or even improper treatment. Skilled healthcare providers will be able to help a patient in need judiciously select the appropriate testing. As medical science advances, we will be able to identify risk factors and personalize testing to further expedite the process of diagnosis and optimal treatment.