November 5, 2021 - Podcast

Episode 199—Improving medical decision making, and treating cancer symptom clusters

Doctors often have to make complicated clinical decisions under serious time pressure regarding patient care. How do they decide what to do? Decades of research shows that humans frequently rely on mental shortcuts, called heuristics, to determine the best course of action quickly, says Helen Colby, an assistant professor in the Indiana University Kelley School of Business in Indianapolis who studies health decision making. Most of the time, she says, heuristics save time and resources and produce pretty good outcomes. But in some situations, heuristics can lead to suboptimal decisions. Writing in the journal Science, Colby and a colleague note that it’s common, even among experienced doctors, for a prior patient’s experience to influence care for the next patient. However, that assumes that two patients cared for by the same physician consecutively would be highly similar, which is not typically the case. When lives are on the line, Colby says, any improvement in decision making can have life-saving consequences. She and her colleague offer several suggestions to help physicians overcome reliance on heuristics. First, doctors should be taught to recognize the role of heuristics in decision making. Also, more research and clinical efforts need to focus on designing and testing decision aids that are beneficial to patients and user-friendly to physicians. Like all experts, Colby says, doctors are human, and helping them make better decisions that help us can lead to better health care for all.

In other news, cancer patients undergoing chemotherapy often experience multiple symptoms related to the disease and its treatment at the same time, such as fatigue, pain, depression, and anxiety. Typically, doctors treat these symptoms individually. Now a new study by Regenstrief Institute and Indiana University researchers has examined these cancer symptom clusters and when they occur during treatment to better understand how such clusters interact with each other and develop strategies to counter them. The research team studied data on symptoms taken from electronic health records of patients who had breast or colorectal cancer. Symptom clusters were identified based on severity and combination of symptoms. Using algorithms they developed, the researchers found that symptom clusters were not the same for breast cancer patients and colorectal cancer patients. They also noted that symptom clusters varied at different times following chemotherapy. Breast cancer patients had slightly more symptoms than colorectal cancer patients during the first year after chemotherapy, while colorectal cancer patients had slightly more depression 48 to 54 months after chemotherapy. The researchers also were able to identify some symptom linkages -- for example, if a colorectal cancer patient had no fatigue symptom, they were also unlikely to experience depression. The researchers say the study’s methods can be generalized beyond breast and colorectal cancer to analyze symptom clusters of other chronic diseases and can provide critical information to a patient’s care team to find the right personalized treatment.