Intuition can be helpful when initially guiding a decision when there is little data, however, we have learned that it may not always be the correct assumptions and can lead to dangerous results. This brings me back to the start of COVID when we did not have any data on treatment therapy. Information was scarce and we had to go on our intuition using treatment guidelines for other similar infections. Then when that did not work, we went looking for drugs with mechanisms that could potentially be beneficial. Many clinicians went on their intuition because they had decades of experience. What we learned was that it was unlike other infections that we had experienced.

In healthcare, we have different levels of studies that help guide us when making decisions. Initially, we had to treat based on case reports from China and other countries that were hit first. The issue with case reports is that they are non-randomized and do not have a robust patient population. As time went on, the patients that were enrolled in randomized studies with directed antivirals and supportive care were released. Depending on the severity of the infection, risk factors, and strain, healthcare teams compared their patient data with the closest matching patient population and treated them accordingly. With thousands of data points to compare, therapy became standardized and survival increased significantly. Use of data was definitely more effective than practitioners' intuition in this scenario. Reply or response to above post?