How are patient characteristics relevant for physicians' clinical decision making in diabetes? An analysis of qualitative results from a cross-national factorial experiment
Introduction
Variations in medical practice have been widely documented and are a linchpin in explanations of health disparities. Social scientific and epidemiological researchers have observed variations in disease prevalence and medical practices across (Rubin, Peyrot, & Siminerio, 2006) and within countries (Millett et al., 2007), and for conditions ranging from coronary heart disease (Popescu, Vaughan-Sarrazin, & Rosenthal, 2007) to schizophrenia (Kelly et al., 2006). These differences hold for various aspects of clinical decisions including diagnosis, ordering tests (Popescu et al., 2007), selecting medications (Grant et al., 2007), asking questions, writing prescriptions, giving lifestyle advice and making referrals (McKinlay et al., 2006).
Investigations into the predictors of practice variation have focused largely on patient and provider characteristics. King and Kerr (1996) note that research into heart disease has been gender biased and “gender blind” research has resulted in questionable treatment regimens and sub-optimal care for women (Pinn, 2003). Similarly, patient race has been shown to be a significant predictive factor in a number of treatment decisions and outcomes (Schulman et al., 1999), including diabetes (Harris, 2001). Older patients have been found to receive both delayed treatment and fewer diagnostic interventions (Gatsonis, Epstein, Newhouse, Normand, & McNeil, 1995), fewer prevention drugs (Stafford & Singer, 1996), and fewer prescriptions that are known to be effective (Soumerai et al., 1997). In terms of socioeconomic status (SES), Scott et al. (1996) found that physicians were more likely to order further tests and less likely to prescribe medications for high SES patients compared with their lower SES counterparts. Provider attributes such as gender (Britt et al., 1996, Collins et al., 1995) and level of experience (Bach et al., 2004, Collins et al., 1995) have similarly been shown to be significant predictors of variability in clinical decisions.
In sociology, there is a long tradition of examining the nature of medical practice and the process of clinical decision making. For example, much attention has been allocated to uncertainty and risk inherent in medical work, and how providers are socialized to manage such uncertainty (Bosk, 1979, Fox, 1957, Light, 1972, Sharpe and Fadin, 1998). Some researchers have argued that uncertainty is so pervasive and inherent in medical work that medical error cannot be readily separated from the work itself, claiming that “mistakes are an indigenous feature of the work process as it unfolds” (Paget, 1988). In the context of diabetes care, physicians constantly face uncertainty as they must try to ascertain how closely patients will follow treatment regimens in order to prescribe treatment regimens that will be maximally effective in terms of lowering glucose levels without leading to hypoglycemia (Lutfey, 2003, Lutfey, 2005). Methodologically and theoretically, sociologists have used ethnographic and conversation analytic approaches to examine in detail the in situ practices of medical work and how they operate in actual practice settings (Heritage & Maynard, 2006). Such studies have shed light on how physicians make attributions about the causes of illness (Gill, 1998), manage authority in the delivery of diagnoses (Perakyla, 1998); and the delivery of bad news (Maynard, 2003).
At the same time, a sizeable literature has developed in social psychology and economics focused on how physicians process information during patient-provider encounters, including how prejudice, stereotyping, and uncertainty can affect assessments of patients and decisions about their treatment (Balsa and McGuire, 2001, Institute of Medicine, 2003, van Ryn and Burke, 2000). For example, van Ryn and Burke (2000) suggests that racial differences may stem from providers evaluating black patients more negatively than whites as a result of negative stereotyping. By contrast, Balsa and McGuire (2001) suggest that the problem is one of white physicians having difficulty making sense of minority patients' symptom presentation and relying on statistical averages of their previous experience with people from that group (a process they term “statistical discrimination”). Others have suggested that interaction between race-concordant doctor–patient dyads might differ from race-discordant pairs, possibly reflecting underlying differences in attitudes or communication (Cooper et al., 2003).
Substantively, these bodies of work are related to the present topic insofar as they are concerned with the processes underlying clinical decision making and potential sources of bias that lead to the aggregate associations observed in a variety of domains. Relative to the large epidemiologic literature concerned with demographic predictors of medical practice variation, however, we still know relatively little from a social science perspective about the mental reasoning processes involved when physicians are assessing patients, and how they come to see various patient characteristics as relevant to their work. We build on and extend previous work by using open-ended think-aloud data from a cross-national videotaped vignette experiment to examine which patient characteristics physicians identify as relevant for their clinical decision making and why they are important. Below, we describe in detail how physicians articulated the relevance of patient characteristics for their treatment decisions in a case of diagnosed diabetes. As detailed in the next section, the experimental design of our study provides the unique opportunity to examine how physicians' explanations vary even when the presentation of the case is identical across vignettes.
Section snippets
Data and methods
A factorial experiment was used to simultaneously measure the effects of: (a) patient attributes (age, gender, race and socioeconomic status); (b) physician characteristics (gender and years of clinical experience); and (c) features of the healthcare system in three countries (United States, United Kingdom, and Germany) on medical decision making for two common medical problems, pre-diabetes and diagnosed diabetes with an emerging complication. A full factorial of 24 = 16 combinations of patient
Patient demographic characteristics
Bayesian reasoning about diagnostics would suggest that patient characteristics (age, race, and gender) should figure prominently in physician CDM insofar as they provide important information about prior probability that a given patient will experience a given condition or problem (e.g., epidemiologic base rates). For example, among U.S. adults over the age of 20 years, there is a 9.6% diabetes prevalence, but this figure increases to 20.9% for adults over the age of 60 and is also higher for
Conclusion
Previous research on clinical decision making shows that physicians' diagnostic and treatment decisions vary according to patient characteristics, physician attributes, and the countries in which they are practicing. Less is known about the decision making route by which providers arrive at these endpoints and why they make such varied decisions. The present study helps fill such gaps in our knowledge by examining open-ended interview data from a large vignette-based factorial experiment to
Acknowledgements
This research was supported by grant #DK66425 from the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). We appreciate technical support from Elizabeth Mason, Carol Link, and Rebecca Shackelton.
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