期刊
MEDICAL DECISION MAKING
卷 38, 期 7, 页码 881-887出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/0272989X18793637
关键词
decision making; experiment; health care rationing; identifiability; individuals and groups
Background. Whether doctors at the bedside level should be engaged in health care rationing is a controversial topic that has spurred much debate. From an empirical point of view, a key issue is whether there exists a behavioral difference between rationing at the bedside and policy level. Psychological theory suggests that we should indeed expect such a difference, but existing empirical evidence is inconclusive. Objective. To explore whether rationing decisions taken at the bedside level are different from rationing decisions taken at the policy level. Method. Behavioral experiment where participants (n = 573) made rationing decisions in hypothetical scenarios. Participants (medical and nonmedical students) were randomly assigned to either a bedside or a policy condition. Each scenario involved 1 decision, concerning either a life-saving medical treatment or a quality-of-life improving treatment. All scenarios were identical across the bedside and policy condition except for the level of decision making. Results. We found a discrepancy between health care rationing at policy and bedside level for scenarios involving life-saving decisions, where subjects rationed treatments to a greater extent at the policy level compared to bedside level (35.6% v. 29.3%, P = 0.001). Medical students were more likely to ration care compared to nonmedical students. Follow-up questions showed that bedside rationing was more emotionally burdensome than rationing at the policy level, indicating that psychological factors likely play a key role in explaining the observed behavioral differences. We found no difference in rationing between bedside and policy level for quality-of-life improving treatments (54.6% v. 55.7%, P = 0.507). Conclusions. Our results indicate a robust bedside effect in the life-saving domain of health care rationing decisions, thereby adding new insights to the understanding of the malleability of preferences related to resource allocation.
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