Medical care
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Evidence-based medicine is increasingly expected in health care decision-making. The Centers for Medicare and Medicaid have initiated efforts to understand the applicability of Bayesian techniques for synthesizing evidence. As a case study, a Bayesian analysis of clinical trials of implantable cardioverter defibrillators was undertaken using patient-level data not typically available for analysis. ⋯ Bayesian meta-analyses based on literature surveys can effectively inform coverage decisions. Bayesian modeling for endpoints such as mortality can elucidate treatment effects over time. The Bayesian approach used in a sequential manner over time can predict results and help assess the utility of future clinical trials.
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The Washington State workers' compensation agency funded a coverage with evidence development study to evaluate spinal cord stimulation (SCS) for chronic back and leg pain after spine surgery (failed back surgery syndrome). We previously published the study outcomes. We now report results from a second patient cohort; study costs; and industry, provider, and payer responses. ⋯ Coverage with evidence development studies may yield important information not apparent from randomized clinical trials concerning long-term risks and benefits of a therapy in clinical practice for specific subpopulations, but are likely to be met with criticism from interested parties.
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Despite ethical implications, there are anecdotal reports of health practitioners withholding services from patients who do not pay their bills. We surveyed physicians about their attitudes and experiences regarding nonpaying patients. ⋯ A majority of primary care practitioners responding to our survey would be willing to withhold medical care from patients who do not pay their bills; some have actually done so despite ethical and legal mandates to the contrary. Physicians should be educated about the importance of the patient-physician relationship and their ethical obligations to patients.
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An evaluation of the effect of a healthcare intervention (or an exposure) must consider multiple possible outcomes, including the primary outcome of interest and other outcomes such as adverse events or mortality. The determination of the likelihood of benefit from an intervention, in the presence of other competing outcomes, is a competing risks problem. Although statistical methods exist for quantifying the probability of benefit from an intervention while accounting for competing events, these methods have not been widely adopted by clinical researchers. ⋯ CSH is the fundamental measure of outcome in competing risks problems. It is appropriate for evaluating treatment effects in the presence of competing events. Results of CSH analysis for primary and competing outcomes should always be reported even when EFS or CIF approaches are called for. EFS is appropriate for evaluating the composite effect of an intervention, only when combining different endpoints is clinically and biologically meaningful, and the treatment has similar effects on all event types. CIF is useful for evaluating the likelihood of benefit from an intervention over a meaningful period. CIF should be used for absolute risk calculations instead of the widely used complement of the Kaplan-Meier (1 - KM) estimator.
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Health-related quality of life (HRQL) assessment is frequently used in comparative effectiveness research, but low-literacy patients are often excluded. Appropriately translated and user-friendly HRQL measures are essential to ensure inclusion of low-literate and non-English-speaking patients in comparative effectiveness research. ⋯ This study demonstrates the feasibility of this multimedia touch screen program for low-literacy patients. The program will provide opportunities to evaluate the effectiveness of interventions in more diverse patient populations.