The Camden group (and others) are now exploring models involving more complete designs for providing care. In the Camden Coalition trial, our results suggest that existing systems poorly serve the complex needs of the coalition’s patients. Since we learn more from RCTs than just the impact of an intervention on a single outcome, finding no effect doesn’t mean the end of the road. These results offer an important lesson: We wouldn’t have accurately measured the intervention’s impact if we hadn’t done a randomized controlled trial. These results tell us that the improvements we saw in the intervention group were the result of regression to the mean, not the coalition’s program. But as we report in this week’s New England Journal of Medicine, we saw the same decline in hospital use among those in the control group. When we looked at patients in the intervention group, the results of the Camden Coalition’s program looked very encouraging: Participants in this group visited the hospital about 40% less in the six months after the intervention. That way, the outcomes observed in the control group would tell us what would have happened over time in the intervention group in the absence of the program. Randomization ensured that, at the start of the program, these two groups were similar. We randomly assigned patients who were eligible and who consented to participate to receive either the coalition’s program or status quo care. To learn what its program was doing - and innovate based on the findings - it partnered with our research team to conduct a randomized controlled trial (RCT). A spate of high-profile studies have reported dramatic reductions in health care spending from programs designed to keep super-utilizers out of the hospital through various means, such as coordinating their outpatient care and coaching them on managing their conditions and medications.Īs a data-driven, learning organization, the coalition did not want to rest on its considerable laurels. That is because this 1% of patients account for almost 25% of all U.S. health care system, the very highest-cost patients - known as super-utilizers - have been a focus of attention. That concept has important implications for health care policy today, one of which is that more health policymakers and health care researchers should use randomized evaluations to avoid problems of regression to the mean in estimating the effects of policies. He dubbed this regression to the mean - when something measured as extreme in a first instance is likely to be measured as less extreme later on. In the late 19th century, English polymath Sir Francis Galton noted that tall parents often had kids shorter than they were, while short parents often ended up with taller kids. Exclusive analysis of biotech, pharma, and the life sciences Learn More
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