Impact of Sociodemographic Patient Characteristics on the Efficacy of Decision Aids

Abstract

P atients increasingly face treatment choices with comparable outcomes, and clinicians are confronting the challenge of how best to discuss alternative treatments while incorporating patients' values and preferences. One method involves shared decision making (SDM), "the process of interacting with patients who wish to be involved in arriving at an informed, values-based choice among two or more medically reasonable alternatives."1 Professional societies, including the most recent American Heart Association/American College of Cardiology guidelines, advocate the use of SDM within the clinical encounter. 2-4 Editorial see p 347Decision aids (DAs) are tools used during the clinical encounter to assist clinicians in engaging their patients in SDM. 5 The use of DAs to promote SDM addresses key quality goals for improving health care articulated by the Institute of Medicine (care that is evidence based and personalized).6 SDM with DAs is also prioritized in health policy initiatives, including specific provisions in the Affordable Care Act for SDM research, accreditation, and implementation.7 Although individual trials have suggested that DA use during the clinical encounter improves patient knowledge, reduces decisional conflict, and increases patient participation in choice, 8-11 the direction and magnitude of these effects across diverse patient Background-Decision aids (DAs) increase patient knowledge, reduce decisional conflict, and promote shared decision making (SDM). The extent to which they do so across diverse sociodemographic patient groups is unknown. Methods and Results-We conducted a patient-level meta-analysis of 7 randomized trials of DA versus usual care comprising 771 encounters between patients and clinicians discussing treatment options for chest pain, myocardial infarction, diabetes mellitus, and osteoporosis. Using a random effects model, we examined the impact of sociodemographic patient characteristics (age, sex, education, income, and insurance status) on the outcomes of knowledge transfer, decisional conflict, and patient involvement in SDM. Because of small numbers of people of color in the study population, we were not powered to investigate the role of race. Most patients were aged ≥65 years (61%), white (94%), and women (59%); two thirds had greater than a high school education. Compared with usual care, DA patients gained knowledge, were more likely to know their risk, and had less decisional conflict along with greater involvement in SDM. These gains were largely consistent across sociodemographic patient groups, with DAs demonstrating similar efficacy when used with vulnerable patients such as the elderly and those with less income and less formal education. Differences in efficacy were found only in knowledge of risk in 1 subgroup, with greater efficacy among those with higher education (35% versus 18%; P=0.02). Conclusions-In this patient-level meta-analysis of 7 randomized trials, DAs were efficacious across diverse sociodemographic groups as measured by knowledge transfer, decisio...

Peering Inside the Black Box of Patient-Centered Care

“…However, this limitation may also suggest that when decision support tools are placed within a setting known to encourage patientcentered care, sociodemographic traits previously associated with low patient engagement become less salient. As decision support tools gain acceptance in other clinical settings, it will be interesting to observe whether the findings by Coylewright et al 8 remain as valid and refreshing. Another challenge to assessing patient centeredness is that most useful measures rely on subjective ratings of care by patients.…”

“…Each of the decision support tools reviewed in this study was developed and initially implemented within the Mayo Clinic health system. This limitation may affect the external validity of the study by Coylewright et al, 8 given the selection bias of this population. However, this limitation may also suggest that when decision support tools are placed within a setting known to encourage patientcentered care, sociodemographic traits previously associated with low patient engagement become less salient.…”

“…These studies suggest that patients who are older, less educated, and of minority race are less likely to engage in shared medical decision making. 5,6 However, the contrasting results of Coylewright et al, 8 also in this issue of Circulation Cardiovascular Quality and Outcomes, suggest that differences in age, sex, education, income, and type of health insurance are not predictive of preferences for or outcomes of shared decision making. The authors conducted a rigorous systematic review and meta-analysis of decision support tools related to myocardial infarction, angina, diabetes mellitus medications, cardiovascular primary prevention, and osteoporosis care.…”

Shared Decision Making With Vulnerable Populations in the Emergency Department

“…Given these findings, decision aids could have a potentially significant impact on healthcare disparities. 61 Although decision aids are being tested in the ED setting, 62,63 none has been explicitly adapted for vulnerable patients. With a user-centered approach, a patient representative of the vulnerable population, for example, from undomiciled urban populations, could be involved in the design and testing of these tools, that at least take in consideration the specific context, culture, and language of each patient.…”

2015 ACC/AHA Focused Update of Secondary Prevention Lipid Performance Measures

“…Decision aids are one type of tool used during clinical encounters and have been shown to increase knowledge transfer to patients about personalized benefit and risk, improve patient involvement in decision making, and reduce decisional conflict . 40 Decision aids can be implemented at the point of care to promote SDM, including the choice of whether or not to initiate a statin for the next 5 to 10 years to lower cardiovascular risk (http://statindecisionaid .mayoclinic .org) . Measuring the occurrence of SDM is a developing science, and in the scenario of clinicians recommending statins for secondary prevention, potential measures to assess if SDM occurred include: 1) Does the patient know his or her personalized cardiovascular risk?…”