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What Can Experiments Tell Us About Clinical Decision Making?
*Carol L. Link, New England Research Institutes 
John B. McKinlay, New England Research Institutes 

Keywords: experiments, observational studies

Variations in clinical decision making, such as discrimination by patient gender or race/ethnicity, are often examined using observational studies of massive data sets (e.g. claims data). However, results are potentially confounded by the varied details of each case. An experiment in which patient characteristics are systematically varied (but not medical details) allows cause and effect conclusions to be drawn with smaller samples. Data come from five factorial experiments: four concerning the diagnosis of disease (coronary heart disease (CHD) (two experiments), depression, and diabetes) and one concerning the management of a case of diabetes with emerging neuropathy in which 256-384 randomly selected primary care physicians viewed video vignettes of patients. The patient factors were experimentally varied (gender, age, race/ethnicity and socioeconomic status (depicted by dress and current or former occupation)) with 16-24 different vignettes per experiment. Physicians were stratified by country, gender, and experience (measured by year of graduation from medical school).

Properly designed experiments have four advantages over observational studies: (1) Estimates are un-confounded (e.g. between race/ethnicity and socioeconomic status which is impossible in observational studies); (2) Cause and effect can be assessed rather than only associations; (3) They are cost efficient due to smaller sample size requirements; and (4) Observed covariates can be added to the analysis. We found that physicians were inconsistent in their attention to base rates of disease. Consistent with base rates, their certainty for a CHD diagnosis was lower for younger women (p <.01), and the probability of a diabetes diagnosis was lower in Whites compared to Blacks (p<.05). They were inconsistent with base rates in that their certainty of a depression diagnosis did not vary by gender and their probability of a diabetes diagnosis did not vary by age or socioeconomic status. When looking at observed physician characteristics, we found (consistently across studies) that women would ask more questions. We also found that women and less experienced physicians would offer more lifestyle advice. By experimentally manipulating patient characteristics we find significant non-medical (patient characteristics) influences on clinical decision making with a relatively small number of physicians. We can also find consistent (observational) physician effects.