Besides improving quality, integrating comparative effectiveness research (CER) into practice may also provide an outlet to help cut costs. To do so, CER must address a population of patients who soak up the most healthcare dollars: those with multiple chronic conditions. These patients account for more than 80 percent of Medicare costs and focusing on this population can help correct these cost disparities.
“CER is meant to include representative populations and healthcare providers, to examine treatment effects within various subpopulations, and to compare interventions head to head,” wrote Mary E. Tinetti, MD, and Stephanie A. Studenski, MD, of the Yale School of Medicine in New Haven, Conn., and University of Pittsburgh School of Medicine and VA Pittsburgh Healthcare System in Pittsburgh, respectively, in a June 22 perspective published in New England Journal of Medicine. “Heterogeneity among patients with multiple chronic conditions complicates each of these features.”
Tinetti and Studenski offered that CER must include large, diverse populations, monitor harms and benefits, examine homogeneous subpopulations and focus on better health outcomes.
CER goals will be undertaken by the newly funded Patient-Centered Outcomes Research Institute. However, for patients with multiple comorbidities, Tinetti and Studenski said that identifying representative study populations will be a “daunting task.
“To isolate treatment effects, researchers must consider myriad personal and provider characteristics that are associated with the likelihood of either receiving the study treatments or achieving the target health outcomes,” the authors wrote.
Tinetti and Studenski said that the heterogeneity of treatment effects, which results from variability in patients' initial level of risk, will complicate CER. “Perhaps the most fundamental question is how to define benefit or harm when multiple conditions coexist and multiple treatments are compared,” the authors offered. Because primary outcomes tend to be disease-specific (i.e., stroke prevention), the authors said that this method won’t work when treatments in patients with multiple chronic conditions are being compared.
“Treatments that are effective for one disease may exacerbate other diseases or adversely affect overall health. The likelihood of such mixed benefits and harms increases as the number of coexisting conditions mounts,” the authors offered.
However, Tinetti and Studenski offered that CER will accelerate the movement toward outcome-driven decision making, reimbursement and quality assessments. To make this shift work, there will need to be more of a focus on “universal” outcomes. “Focusing on them would ensure that both harms and benefits of treatments are compared,” the authors wrote.
Lastly, the authors provided several tips for enhancing CER’s applicability to patients with multiple chronic conditions. The steps are as follows:
- Include heterogeneous populations with multiple chronic conditions in sufficient numbers to measure benefits and harms of interventions;
- Develop and implement risk-stratification models and report harms and benefits according to risk strata;
- Examine universital health outcomes that are relevant across diseases (e.g., function, symptoms burden, activity, survival and active life expectancy);
- Account for health transitions over time;
- Employ analytical methods that account for biases such as confounding by indication (i.e., when the indication for treatment is related to the risk of the outcome those with greater disease severity are more likely to receive a treatment and more likely to have bad outcomes regardless of treatment);
- Evaluate longer-term changes in benefits and harms of treatments as patients age and acquire additional conditions;
- Compare usual care or disease-guideline-driven care with single interventions that affect multiple conditions and models of care; and
- Evaluate disease pairs.