In the preimplementation period, the proportion of trauma patients prescribed risk-appropriate VTE prophylaxis was significantly higher for black (70.1%) than white (56.6%) patients (P=0.025). After implementation, prescription of risk-appropriate prophylaxis significantly increased for all patients [black (84.5%) and white (85.5%)], and there were no differences between racial groups (P=0.99) (Fig. 1A).
Before implementation, the proportion of male trauma patients prescribed risk-appropriate VTE prophylaxis was significantly higher (69.5% vs. 55.1%, P=0.045). After implementation, compliance increased significantly for both male (85.7%) and female (81.2%) patients and there were no differences between groups (P=0.078) (Fig. 2A).
Between racial groups, black internal medicine patients were younger (54.0 vs. 58.1 y, P<0.001) and less frequently male (48.6% vs. 54.5%, P=0.012). In the preimplementation period, significantly more white than black patients (68.6% vs. 61.0%, P=0.017) had at least 1 major VTE risk factor (Table 2). White patients were younger in the preimplementation group (60.4 vs. 56.9 y, P=0.004). Fewer black (54.7% vs. 61.0%, P=0.034) and white (68.6% vs. 55.0%, P<0.001) patients presented with a major risk factor in the postimplementation period (Table 3).
Before implementation, significantly more black patients were prescribed risk-appropriate VTE prophylaxis than white patients (69.5% vs. 61.7%, P=0.015). After implementation, compliance increased significantly for both black (91.8%) and white (88.0%) patients and there were no differences between races (P=0.082) (Fig. 1B). There were no differences in risk-appropriate VTE prophylaxis prescription between sexes, before or after implementation (Fig. 2B).
The intended purpose of this QI intervention was to improve the care for all hospitalized patients rather than specifically targeting subgroups of patients who were receiving suboptimal care. We recognize that eliminating disparities in providing best-practice VTE prevention was an unintended consequence of this intervention. However, disparities elimination falls under the general umbrella of QI and has been a goal of health information technologies. Previous studies of QI interventions that have been shown to lessen or eliminate disparities focused narrowly on certain patient populations, such as patients with diabetes42 or myocardial infarction,43 or patients with cancer undergoing surgery.44 Our findings demonstrate the power of broadly applied QI interventions targeting all hospitalized patients and represent another beneficial consequence of QI efforts in health care.
It remains unclear which specific factors are most strongly associated with health care disparities and may influence disparities in real-world decision making. One possible explanation is that black patients are known to have undiagnosed comorbidities and be at risk for cardiovascular complications,45 including VTE. Providers who were making clinical decisions entirely independently, without the use of a standardized mandatory decision support tool, may have chosen to prescribe more aggressive VTE prophylaxis regimens for these black patients to overcompensate for these issues. Providers may not necessarily believe that all patients require VTE prophylaxis and this misguided, subconscious calculation of the risk benefit ratio did not favor prescribing VTE prophylaxis for white patients. A recent study using an Implicit Association Test, and a series of clinical vignettes applied to first-year medical students, showed an overall preference for white individuals but the clinical vignette responses were not associated with patient race.11 Although these findings are important insights to clinician perceptions, they represent simulated decision making in controlled environments rather than real-world clinical decisions for actual hospitalized patients. However, our study demonstrates that a well-integrated CCDS tool transcends those factors, regardless of the causal pathway, and is capable of modifying the decisional behavior that may create health care disparities by reducing the impact of bias.
Data show that black patients more commonly receive lower-quality care than white patients1,46 and efforts to reduce health care disparities have often failed. Therefore we were somewhat surprised to find that white hospitalized patients were less likely to be prescribed risk-appropriate VTE prophylaxis during the preimplementation period. However, previous studies have demonstrated better outcomes for black patients compared with white patients undergoing kidney dialysis47 or survival after trauma.48 Similar to what has been reported in these studies, age or clinical condition may be confounding variables, which requires further exploration in a larger data set. Another potential explanation was identified in a study in which black hospitalized patients rated their interaction with prescribers as less participatory than whites.49 Consequently, it is possible that shared decision making between white patients and prescribers resulted in suboptimal VTE prophylaxis.
We recognize that our study has several limitations. First, we were not able to evaluate variation among individual types of clinicians (ie, physicians, nurse practitioners), limiting our ability to evaluate the impact of experience on health care disparities. However, our findings were well-conserved across 2 very different clinical services indicating that these disparities are neither random nor attributable to select prescribers within a single clinical service. Second, our limited sample size did not allow for multivariable analysis to elucidate other associations with the observed disparity. Third, our results were demonstrated using only a single evidence-based practice (VTE prophylaxis) at a single academic medical center. Nevertheless, the CCDS intervention eliminated disparities among a diverse group of medical and surgical patients, proving its effectiveness in a “real-world” setting. Finally, there were differences in the number of patients who presented with major VTE risk factors. However, risk-appropriate prophylaxis is determined on an individual patient basis, so these differences should not have affected the decision-making process.
When designing CCDS tools to impact provider behavior, it is important to consider how the tool will be integrated into the clinical and decisional workflow.50 Our mandatory CCDS tool focuses clinician attention on completing a task and forces VTE risk assessment for every patient. Passive CCDS tools that do not require provider action have been shown to be less effective at impacting provider behavior51 and will likely have less impact on eliminating disparities in care delivery.
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