Interventions that have incorporated one or more elements of the CCM have had beneficial effects on clinical outcomes and processes of care for patients, and the results were consistent across a variety of chronic illnesses. Our estimated pooled effect size estimates, although small-to-moderate-sized,(60
) are also broadly consistent with those reported in prior meta-analyses.(24
) Interventions directed at diabetes care, for example, led to a 0.30%-0.47% reduction in hemoglobin A1c. Managed care organizations may realize benefits from even smaller reductions in mean population values for continuous risk factors such as lipid levels and hemoglobin A1c. For example, the European Prospective Investigation of Cancer and Nutrition (EPIC-Norfolk) estimated that a population reduction of 0.2% in hemoglobin A1c could reduce the prevalence of men with high HbA1c levels (5%-6.9%) from 79% to 57% and reduce excess mortality by 10%.(61
) We found that interventions directed at congestive heart failure led to a 5.6-6.7 point improvement in the Chronic Heart Failure Questionnaire, slightly less than the 7-9 point difference that is regarded as a minimal clinically important difference on that scale.(62
In terms of quality of life, the evidence was mixed, with the asthma and diabetes studies showing no benefit. It has been well-established that condition-specific quality of life scales are more sensitive to changes in clinical status than are generic measures of quality of life. Most of the studies included in our meta-analyses used condition-specific quality of life scales (see the Evidence Table
). For some conditions, one might reasonably expect disease-specific interventions to have a more direct effect on clinical outcomes and processes of care than on quality of life. Improvements in diabetes care, for example, are focused on preventing long-run microvascular complications beyond the end-point of the studies we examined,(63
) with less focus on improving short-run quality of life. We speculate that our meta-analyses might have yielded different results, for example, had we used a quality of life measure that is more sensitive to the short-run benefits of improved glycemic control.(64
The CCM elements most responsible for these benefits could not be determined from the data. Effects appeared to be somewhat stronger for Delivery System Design and Self-Management Support, although Decision Support had significant beneficial effects on processes. The other elements of the CCM may be critical infrastructure to providing high quality chronic care but are more difficult to test scientifically. For example, leadership support may be necessary to promote and sustain higher quality, but randomizing managed care organizations to receive changes in leadership support is clearly infeasible. It is no wonder that these elements have not had many scientific trials. The fact that such linkages are hard to study scientifically does not mean they are unimportant. Instead, they are supported by common sense and reports from successful organizations.
The CCM has been promoted as a unified package. Evidence that interventions with multiple components do better than single components (66
) has been interpreted as supporting synergistic effects in which the whole is bigger than the sum of the parts. Some components of the model, such as building an electronic patient registry, may facilitate other components and reduce their costs. We found that single interventions were quite successful. In post hoc
analyses, we attempted to identify whether there may be some advantage to having more components, but that advantage was never statistically significant and does not appear to be more than additive.
One limitation of our work is that the studies in our sample only incorporated elements of the CCM and were not designed to test the entire CCM package.(11
) The RAND/UC Berkeley Improving Chronic Illness Care Evaluation (ICICE) is nearing completion, and it is the first independent and controlled evaluation of the effects of implementing the CCM as a whole. Organizations signed up for the Institute of Healthcare Improvement’s Collaboratives to improve care for specific conditions (16
) and worked together to learn about the CCM and about how to make organizational changes to improve quality of care. The design of the ICICE has been published,(17
) and results from the evaluation are posted at http://www.rand.org/health/ICICE/
as they become available. Despite the large scale of the evaluation -- 24 organizations with both intervention and control sites, and 12 organizations with intervention sites only -- the number of participating organizations was too small to determine which components of the CCM were most critical to success. The organizations’ characteristics and what they did differed in many ways, many times more than the number that could be studied statistically.
A second limitation is that the use of meta-analytic methods necessarily forces what are likely complex, multivariate interventions into a narrow linear framework. In this meta-analysis we aggregated results across conditions and across interventions. We attempted to investigate the sources of variation between studies, but we were unable to explain much of it. We were also unable to assess interactions between CCM element and type of chronic illness. For example, a Clinical Information Systems intervention featuring physician reminders may be particularly effective for improving care for one type of chronic illness but not for other types, and a pooled analysis would not identify the interaction. A related limitation is that we were unable to assess the intensity of implementation in the study interventions.(19
) Perhaps the interventions we studied were successful because doing trials requires energy and commitment to the intervention concept. This energy may be an important component to initial success that is hard to transfer. If there is significant variation in the intensity of implementation of these elements across studies, simplified comparisons by the presence or absence of these elements may mask important between-study differences. And finally, we focused our data collection on selected outcomes. We needed to do so in order to aggregate across studies, recognizing that interventions may have had different effects on other outcomes and processes of care. However, the outcomes we selected were reported in a large number of studies and likely reflect outcomes of interest to managed care organizations. A final limitation is that we used an unconventional search strategy by relying on prior meta-analyses as the primary substrate for identifying our sample of studies. Doing so may have introduced unpredictable biases, but we also systematically searched the MEDLINE database and the Chronic Care Bibliography(43
) to identify more recently published studies.
Despite these limitations, our meta-analysis shows that interventions that contain one or more elements of the CCM can improve outcomes and processes for several chronic illnesses of interest to managed care organizations. How to transfer the gains from these efficacy studies into the chaotic real world of health care is a different but equally important issue.