BOX 6-3
New Methods for Randomized Clinical Trials:
Point-of-Care Clinical Trial

One new method for conducting experimental research is the point-of-care clinical trial. These trials currently are being conducted at the Boston Veterans Affairs Health Care System, with similar trials being proposed or conducted at other locations (Vickers and Scardino, 2009). The method entails using an electronic health records system to conduct randomized controlled trials by automatically flagging patients who have a choice between competing treatments. If patients do not express a preference, they are asked whether they would be willing to participate in a trial and if so, are randomly assigned to a treatment protocol. The electronic health record system records outcome data and automatically calculates the effectiveness of the treatment protocols. Disadvantages of such trials are that they do not allow for a control group and can be used only for treatments that are already approved for standard care. This type of trial has started being applied to consideration of competing methods for insulin administration (a sliding scale versus a weight-based regimen) for blood sugar control (Fiore et al., 2011).

these new methods are designed to reduce the expense and effort of conducting research, improve the applicability of the results to clinical decisions, improve the ability to identify smaller effects, and be applied when traditional methods cannot be used.

In addition to new research methods, advances in statistical analysis, simulation, and modeling have supplemented traditional methods for conducting trials. Given that even the most tightly controlled trials show a distribution in patient responses to a given treatment or intervention, new statistical techniques can help segment results for different populations. Further, new Bayesian techniques for data analysis can separate out the effects of different clinical interventions on overall population health (Berry et al., 2006). With the growth in computational power, new models have been developed that can replicate physiological pathways and disease states (Eddy and Schlessinger, 2003; Stern et al., 2008). These models can then be used to simulate clinical trials and individualize clinical guidelines to a patient’s particular situation and biology; this approach thus holds promise for improving health status while reducing costs (Eddy et al., 2011). As computational power grows, the potential applications of these simulation and modeling tools will continue to increase. Despite the opportunities afforded by new research methods, several challenges must be addressed as these methods are improved. One such challenge for the clinical research enterprise is keeping pace with the introduction of new procedures,



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