Author: Katherine Homann
Innovations allow increased amounts of data and information to influence clinical decision making; however, how do constraints like cognitive biases impact use and adoption of such technologies? Conducting a review of the scientific literature regarding cognitive biases and application of decision support reveals opportunities for impact and adoption of such tools to improve clinical care. This talk will thus discuss: 1) Common cognitive biases and limitations that hinder clinical care. 2) Existing applications of clinical decision support within medical care related to cognitive biases. 3) Successes and challenges faced by applying clinical decision support to avoid cognitive bias and improve quality of care. Research suggests a range of decision types and methods are applied in clinical care, with the Emergency Department providing a good area for analysis due to its high density of decision making and range of skills (1). The type of cognitive biases in the Emergency Department and other areas of medical practice are well studied (1,2). The impact of cognitive bias is now significant enough to be increasingly taught in medical education (3). Cognitive biases not only impact treatment decisions, but data gathering and application of evidence--typically without clinicians being aware of their bias (2). Decision support technologies have thus been proposed as one way to remove such bias. Many industries (such as aviation, business and others) have documented successes with decision support technologies: it is suggested similar tech can be applied to health care. (4) Research of decision support in health care suggests improvement in outcomes (5), which could possibly be tied to decreasing cognitive biases. However, new types of cognitive bias can be introduced via decision support such as “automation bias” (6). Awareness of these risks and challenges of cognitive biases in the face of clinical decision support help inform means to better apply data and technology to health care decisions to improve outcomes. References: (1) Croskerry, P (2002). Achieving quality in clinical decision making: cognitive strategies and detection of bias. Academic Emergency Medicine. 9:1184-1204. (2) Bornstein and Emler (2001). Rationality in medical decision making: a review of the literature on doctors’ decision making biases. Journal of Evaluation in Clinical Practice. 7(2): 97-107. (3) Reilly, Ogdie, Feldt and Myers (2013). Teaching about how doctors think: a longitudinal curriculum in cognitive bias and diagnostic error for residents. BMJ Quality and Safety. 0:1-7. (4) Wu, Davis and Bell (2012). Advancing clinical decision support using lessons from outside of healthcare: an interdisciplinary systematic review. BMC Medical Informatics and Decision Making. 12: 90-99. (5) Garg et al. (2005). Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 293 (10):1223-1238. (6) Goddard, Roudsari, Wyatt (2011). Automation bias: a systemic review of frequency, effect mediators, and mitigators. J Am Medical Informatics Association. 19: 121-127.