Medical decision making : an international journal of the Society for Medical Decision Making
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Systematic reviews of healthcare disparities suggest that clinicians' diagnostic and therapeutic decision making varies by clinically irrelevant characteristics, such as patient race, and that this variation may contribute to healthcare disparities. However, there is little understanding of the particular features of the healthcare setting under which clinicians are most likely to be inappropriately influenced by these characteristics. This study delineates several hypotheses to stimulate future research in this area. ⋯ It is further hypothesized that certain characteristics of healthcare settings will result in higher levels of cognitive load experienced by providers (H3). Finally, it is hypothesized that minority patients will be disproportionately likely to be treated in healthcare settings in which providers experience greater levels of cognitive load (H4a), which will result in racial disparities due to lower levels of controlled processing by providers (H4b) and the influence of racial stereotypes (H4c). The study concludes with implications for research and practice that flow from this framework.
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Multicenter Study
The language of prognostication in intensive care units.
Rationale. Although misunderstandings about prognosis are common in intensive care units (ICUs), little is known about how physicians actually communicate prognostic information. ⋯ There is considerable variability in the language used by physicians to disclose prognosis, with only 20% of physicians using quantitative terms. Very few physicians checked whether families understood prognostic information. These findings may provide potential targets for interventions to improve communication about prognosis in ICUs.
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Decision making at the end of life is frequently complex and often filled with uncertainty. We hypothesized that people with limited health literacy would have more uncertainty about end-of-life decision making than people with adequate literacy. We also hypothesized that video images would decrease uncertainty. ⋯ Subjects with limited health literacy expressed more uncertainty about their preferences for end-of-life care than did subjects with adequate literacy. Our video decision aid improved end-of-life decision making by decreasing uncertainty regarding subjects' preferences, especially for those with limited literacy.
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Comparative Study
The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies.
The propensity score is a balancing score: conditional on the propensity score, treated and untreated subjects have the same distribution of observed baseline characteristics. Four methods of using the propensity score have been described in the literature: stratification on the propensity score, propensity score matching, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. However, the relative ability of these methods to reduce systematic differences between treated and untreated subjects has not been examined. ⋯ For covariate adjustment, the authors used the weighted conditional standardized absolute difference to compare balance between treated and untreated subjects. In both the empirical case study and in the Monte Carlo simulations, they found that matching on the propensity score and weighting using the inverse probability of treatment eliminated a greater degree of the systematic differences between treated and untreated subjects compared with the other 2 methods. In the Monte Carlo simulations, propensity score matching tended to have either comparable or marginally superior performance compared with propensity-score weighting.
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Mathematical and simulation models are increasingly used to plan for and evaluate health sector responses to disasters, yet no clear consensus exists regarding best practices for the design, conduct, and reporting of such models. The authors examined a large selection of published health sector disaster response models to generate a set of best practice guidelines for such models. ⋯ . Quantitative models are critical tools for planning effective health sector responses to disasters. The proposed recommendations can increase the applicability and interpretability of future models, thereby improving strategic, tactical, and operational aspects of preparedness planning and response.