Confirmation Bias in Health Care Leaders

Confirmation Bias in Health Care Leaders

Confirmation bias is the habit and tendency of an individual to interpret, favor, and recall information in ways that will confirm their preexisting expectations and beliefs. ( Westerwick & Polavin,2020).  Health care leaders have been given an enormous role in making evidence-based and wise decisions in our healthcare facilities. The decisions they make each day affect the lives of the health care workers and the lives of health clients and the community. With this responsibility on their shoulders, they sometimes favor what they believe in, making it very difficult for them to make the right decisions. As such, we can see that confirmation bias distorts decision making based on evidence; it is currently prevalent in our society, including our health facilities.

Confirmation bias is a severe problem and very difficult to overcome because, generally, people actively search for information that will confirm what they believe. People put lesser weight to information that does not favor them and a lot of value in any information that will support their expectations. People tend to interpret the information handed to them in a way that will confirm their preferred hypothesis even if there is a different way that the information could be construed that is contradictory to their view. People tend to forget contradictory information or incorrectly remember it; hence only retain the information that favors what they believe. (Del Vicario & Althubaiti, 2016).People do not want to either know or find out that they are or have been wrong about something; they always want to feel and find that they are right about everything. People will always try to prove that their preexisting hypothesis is correct rather than find out whether it is true or false. That makes them disapprove of any other information that they encounter that is contradictory to their hypothesis.

There are several incidences that I have witnessed in the hospitals that involve confirmation bias. One of them was a scenario when a young health client came to the clinic, experiencing severe chest pains for the previous two days. The clinical officer on duty had recently missed diagnosis on another (old) patient who had an aortic dissection and was still upset about it. Aortic dissection was at the forefront of his mind even though our new patient had no clinical signs that would support the diagnosis and is also very rare in young people. The clinical officer was very concerned about it, regardless. He requested a CT scan to be performed on our young patient ‘just in case’ exposing her to ionizing radiation for no good reason. Even after the CT scan was done and resulted proved negative for aortic dissection, it was still difficult for the clinician to think of another diagnosis for our young patient.

Human beings unconsciously jump into conclusions about things at hand, basing their judgment on preexisting information or encounter. That is why the clinician made a wrong diagnosis for a new patient based on a previous patient’s missed diagnosis. To avoid this, we should reflect on our decision-making history, whether we did rush or not, then make time for slow and precise decision-making. Sometimes we are overconfident in our beliefs and opinions hence make unrealistic decisions based on those hunches. To avoid that, we should revisit our information sources to see if they are fact-based and check whether the information is systematic.  Most people expect past encounters and events to influence the future; this is dangerous in decision making. To avoid this, we should look at things and trends from different perspectives and angles. We need to be open-minded, think deeply, clearly, and accurately before making a decision. Look at the precision, consistency, and relevance of every thought in decision making.

References

Knobloch-Westerwick, S., Mothes, C., & Polavin, N. (2020). Confirmation bias, ingroup bias, and negativity bias in selective exposure to political information. Communication Research, 47(1), 104-124.

Del Vicario, M., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (201Althubaiti, A. (2016).

Information bias in health research: definition, pitfalls, and adjustment methods. Journal of multidisciplinary healthcare, 9, 211.7). We are modeling confirmation bias and polarization: scientific reports, 7, 40391.