Heuristic Thinking in Patient Care

Abstract

This paper studies heuristic thinking and cognitive bias using a natural experiment from the field. The setting for the study is a set of acute care hospitals, where we examine the care process and discharge decisions for individual patients. Determining a patient’s suitability for discharge is cognitively taxing, calling for the decision maker to draw on up-to-date clinical expertise and detailed information. We postulate that bounded rationality in decision making can lead the care provider to substitute clinical readiness for discharge -- a more cognitively complex attribute, with the observed days spent at the hospital -- a more easily accessible heuristic. Identifying the use of the heuristic is challenging, as patient readiness for discharge is often correlated with the time spent at the hospital. Our identification strategy, motivated by regression discontinuity design, exploits a discontinuity associated with admission process at midnight. Based on over 177,000 patient discharges spanning 8 years, we find support for the use of the heuristic - 19.2% (95% CI [14.6%, 23.8%]) of patients incur an increased length of stay by a day on average due to the heuristic. We also find that the reliance on the heuristic is associated with over-treatment; post-midnight patients on average cost $512 (95% CI [$100, $923]) more but without any corresponding improvement in healthcare outcome. A counterfactual analysis shows that eliminating the heuristic lowers the bed capacity needed to maintain the same patient throughput. For example, for a hospital unit in our study with bed capacity of 106, eliminating the heuristic would allow the hospital to maintain the same throughput with 1.61 fewer average beds. These findings have a number of implications for policy formulation and managerial decision making.