Original Article Impact of Sociodemographic Patient Characteristics on the Efficacy of Decision Aids A Patient-Level Meta-Analysis of 7 Randomized Trials Megan Coylewright, MD, MPH; Megan Branda, MS; Jonathan W. Inselman, BA; Nilay Shah, PhD; Erik Hess, MD; Annie LeBlanc, PhD; Victor M. Montori, MD, MSc; Henry H. Ting, MD, MBA 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, decisional conflict, and patient involvement in SDM. To the extent that DAs increase patient knowledge and participation in SDM, they have potential to impact health disparities related to these factors. (Circ Cardiovasc Qual Outcomes. 2014;7:360-367.) Key Words: decision making ◼ decision support techniques
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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 347
Decision 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
Received August 21, 2013; accepted March 20, 2014. From the Division of Cardiovascular Diseases, Department of Medicine (M.C., H.H.T.), Knowledge and Evaluation Research Unit (M.C., M.B., J.W.I., N.S., E.H., A.L., V.M.M., H.H.T.), and Divisions of Health Care Policy and Research, Department of Health Sciences Research (N.S.), Emergency Medicine Research, Department of Emergency Medicine (E.H.), and Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine (V.M.M.), Mayo Clinic, Rochester, MN. This article was handled by Aanand D. Naik, MD, as a Guest Editor. The Editor had no role in the evaluation of the manuscript or in the decision about its acceptance. Abstract presented at American Heart Association Quality of Care and Outcomes Research meeting, Atlanta, GA, May 11, 2012. The Data Supplement is available at http://circoutcomes.ahajournals.org/lookup/suppl/doi:10.1161/HCQ.0000000000000006/-/DC1. Correspondence to Henry H. Ting, MD, MBA, Division of Cardiovascular Diseases, Mayo Clinic, 200 First St SW, Rochester, MN 55905. E-mail ting. [email protected] © 2014 American Heart Association, Inc. Circ Cardiovasc Qual Outcomes is available at http://circoutcomes.ahajournals.org
DOI: 10.1161/HCQ.0000000000000006
Downloaded from http://circoutcomes.ahajournals.org/ 360 at VA MED CTR BOISE on October 12, 2015
Coylewright et al Sociodemographics and Decision Aids 361
WHAT IS KNOWN • Shared decision making improves patient knowledge and reduces decisional conflict. • Decision aids assist in engaging patients in shared decision making. • Shared decision making is a key quality goal for improving patient-centered healthcare delivery.
Methods
WHAT THE STUDY ADDS
Characteristics of Included Trials
• Decision aids are effective among a diverse patient population, including the elderly and those with lower educational levels. sociodemographic groups is not known.12 Notably, DAs delivered during the clinical encounter are but 1 example of strategies to promote SDM. Decision support tools come in many additional formats, including videos, pamphlets, Web-based tools, interactive multimedia programs, and decision coaches. Table.
All completed randomized controlled trials from 2005 to 2011 comparing DAs with UC designed and conducted by the Knowledge and Evaluation Unit at Mayo Clinic were included. We selected only trials performed by the Knowledge and Evaluation Unit at Mayo Clinic given several important advantages of this approach: our entire analysis was based on patient-level data, and there was consistency and rigor in DA development, delivery, measurement, and follow-up. Thus, the search strategy was limited to trials within Mayo Clinic, and a systematic literature search of other types of DA was not performed. Seven unique randomized, controlled trials of DA versus UC conducted between 2005 and 2011 were eligible for the meta-analysis, with 2 similar trials combined for ease of reporting (Osteoporosis I and Osteoporosis II; Table). Institutional review board approval was obtained for all trials;
Characteristics of Included Randomized Trials of Decision Aid vs Usual Care Randomization No. of Patients Clinical Context Level
Trial
Disease State
AMI
MI
106
Inpatient stay for MI, just before discharge
Chest pain
204
Diabetes mellitus
DAD
Osteoporosis I and II
Chest Pain
Diabetes
Statin
Our research group conducted 7 randomized trials of DA versus usual care (UC) delivered by clinicians in outpatient, inpatient, and emergency department settings in suburban and rural locations; this experience provides unique access to patient-level data, including key sociodemographic variables. We sought to explore the interaction between sociodemographic factors and the efficacy of DAs on patient knowledge, decisional conflict, and patient involvement in SDM.
Type of Decision Aid
Details of Choice
Personalized Risk Assessment
Date
Locale
Patient
2009–2010
Suburban
Paper w/ pictographs
Dichotomous (take medications or not)
Yes
Emergency department visit for chest pain
Patient
2010–2011
Suburban
Paper w/ pictographs
Dichotomous (admit for observation or discharge home with follow-up)
Yes
85
Primary care clinic visit for diabetic management
Clinician
2007–2008 Suburban and rural
Plastic cards
Multiple drug options for diabetes mellitus: oral vs injectable medications
No
Diabetes mellitus/ cardiovascular primary prevention
104
Primary care clinic visit for diabetic management
Clinician
2010–2011 Suburban and rural
Plastic cards or paper pictographs (2 tools)
Multiple drug options for diabetes mellitus: oral vs injectable medications/ dichotomous (statin or not)
No/yes
Osteoporosis
179
Primary care clinic visit for osteoporosis prevention
Patient
2007–2008
Suburban
Paper pictographs
Dichotomous (bisphosphonate or not)
Yes
Cardiovascular primary prevention
93
Diabetes mellitus clinic visit for prevention of CAD
Clinician
2005
Suburban
Paper pictographs
Dichotomous (statin or not)
Yes
Two trials were combined for ease of reporting within the table: Osteoporosis I and Osteoporosis II. AMI indicates acute myocardial infarction; CAD, coronary artery disease; DAD, The Impact of Decision Aids to Enhance Shared Decision Making for Diabetes; and MI, myocardial infarction.
Downloaded from http://circoutcomes.ahajournals.org/ at VA MED CTR BOISE on October 12, 2015
362 Circ Cardiovasc Qual Outcomes May 2014 in addition, all subjects (clinicians and patients) provided informed consent. A clinician within the healthcare team (physician, nurse practitioner, physician assistant, or registered nurse) delivered the DAs to eligible patients facing an actual clinical decision. The interventions occurred in outpatient, inpatient, and emergency department settings in suburban and rural healthcare facilities in the upper Midwest; most trials were conducted in central and southeastern Minnesota. DAs displayed information about risks and benefits of pertinent options with most using risk prediction models to produce tailored estimates of risk (Figure I in the Data Supplement). Our DA format consists most often of a paperbased tool containing words, pictures, and pictographs that can be held by the clinician or patient during the shared conversation around treatment options. DAs were used during clinical encounters where there was a treatment decision to be made; the clinician agreed to participate along with their patient in the trial. Many of the encounters were videotaped, enabling measurement of patient engagement in SDM. Clinicians were provided a brief training on the data contained within the tool and suggestions for how to use the tool during the clinical visit. Patient eligibility criteria varied across trials. Eligible patients were adults expected to make a choice about a healthcare treatment or management strategy and who could provide written informed consent (ie, were able to read the consent form in English and did not have, in their clinician’s judgment, sensorial or cognitive limitations to do so). Outcome assessment was ascertained immediately after each visit by means of patient questionnaires.
Sociodemographic Variables Sociodemographic variables were collected from detailed chart reviews and patient questionnaires. Most variables (sex, education, income, and insurance status) were analyzed as categorical variables. Age, which followed a normal distribution based on the Kolmogorov–Smirnov test, was dichotomized to
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 347
Decision 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
Received August 21, 2013; accepted March 20, 2014. From the Division of Cardiovascular Diseases, Department of Medicine (M.C., H.H.T.), Knowledge and Evaluation Research Unit (M.C., M.B., J.W.I., N.S., E.H., A.L., V.M.M., H.H.T.), and Divisions of Health Care Policy and Research, Department of Health Sciences Research (N.S.), Emergency Medicine Research, Department of Emergency Medicine (E.H.), and Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine (V.M.M.), Mayo Clinic, Rochester, MN. This article was handled by Aanand D. Naik, MD, as a Guest Editor. The Editor had no role in the evaluation of the manuscript or in the decision about its acceptance. Abstract presented at American Heart Association Quality of Care and Outcomes Research meeting, Atlanta, GA, May 11, 2012. The Data Supplement is available at http://circoutcomes.ahajournals.org/lookup/suppl/doi:10.1161/HCQ.0000000000000006/-/DC1. Correspondence to Henry H. Ting, MD, MBA, Division of Cardiovascular Diseases, Mayo Clinic, 200 First St SW, Rochester, MN 55905. E-mail ting. [email protected] © 2014 American Heart Association, Inc. Circ Cardiovasc Qual Outcomes is available at http://circoutcomes.ahajournals.org
DOI: 10.1161/HCQ.0000000000000006
Downloaded from http://circoutcomes.ahajournals.org/ 360 at VA MED CTR BOISE on October 12, 2015
Coylewright et al Sociodemographics and Decision Aids 361
WHAT IS KNOWN • Shared decision making improves patient knowledge and reduces decisional conflict. • Decision aids assist in engaging patients in shared decision making. • Shared decision making is a key quality goal for improving patient-centered healthcare delivery.
Methods
WHAT THE STUDY ADDS
Characteristics of Included Trials
• Decision aids are effective among a diverse patient population, including the elderly and those with lower educational levels. sociodemographic groups is not known.12 Notably, DAs delivered during the clinical encounter are but 1 example of strategies to promote SDM. Decision support tools come in many additional formats, including videos, pamphlets, Web-based tools, interactive multimedia programs, and decision coaches. Table.
All completed randomized controlled trials from 2005 to 2011 comparing DAs with UC designed and conducted by the Knowledge and Evaluation Unit at Mayo Clinic were included. We selected only trials performed by the Knowledge and Evaluation Unit at Mayo Clinic given several important advantages of this approach: our entire analysis was based on patient-level data, and there was consistency and rigor in DA development, delivery, measurement, and follow-up. Thus, the search strategy was limited to trials within Mayo Clinic, and a systematic literature search of other types of DA was not performed. Seven unique randomized, controlled trials of DA versus UC conducted between 2005 and 2011 were eligible for the meta-analysis, with 2 similar trials combined for ease of reporting (Osteoporosis I and Osteoporosis II; Table). Institutional review board approval was obtained for all trials;
Characteristics of Included Randomized Trials of Decision Aid vs Usual Care Randomization No. of Patients Clinical Context Level
Trial
Disease State
AMI
MI
106
Inpatient stay for MI, just before discharge
Chest pain
204
Diabetes mellitus
DAD
Osteoporosis I and II
Chest Pain
Diabetes
Statin
Our research group conducted 7 randomized trials of DA versus usual care (UC) delivered by clinicians in outpatient, inpatient, and emergency department settings in suburban and rural locations; this experience provides unique access to patient-level data, including key sociodemographic variables. We sought to explore the interaction between sociodemographic factors and the efficacy of DAs on patient knowledge, decisional conflict, and patient involvement in SDM.
Type of Decision Aid
Details of Choice
Personalized Risk Assessment
Date
Locale
Patient
2009–2010
Suburban
Paper w/ pictographs
Dichotomous (take medications or not)
Yes
Emergency department visit for chest pain
Patient
2010–2011
Suburban
Paper w/ pictographs
Dichotomous (admit for observation or discharge home with follow-up)
Yes
85
Primary care clinic visit for diabetic management
Clinician
2007–2008 Suburban and rural
Plastic cards
Multiple drug options for diabetes mellitus: oral vs injectable medications
No
Diabetes mellitus/ cardiovascular primary prevention
104
Primary care clinic visit for diabetic management
Clinician
2010–2011 Suburban and rural
Plastic cards or paper pictographs (2 tools)
Multiple drug options for diabetes mellitus: oral vs injectable medications/ dichotomous (statin or not)
No/yes
Osteoporosis
179
Primary care clinic visit for osteoporosis prevention
Patient
2007–2008
Suburban
Paper pictographs
Dichotomous (bisphosphonate or not)
Yes
Cardiovascular primary prevention
93
Diabetes mellitus clinic visit for prevention of CAD
Clinician
2005
Suburban
Paper pictographs
Dichotomous (statin or not)
Yes
Two trials were combined for ease of reporting within the table: Osteoporosis I and Osteoporosis II. AMI indicates acute myocardial infarction; CAD, coronary artery disease; DAD, The Impact of Decision Aids to Enhance Shared Decision Making for Diabetes; and MI, myocardial infarction.
Downloaded from http://circoutcomes.ahajournals.org/ at VA MED CTR BOISE on October 12, 2015
362 Circ Cardiovasc Qual Outcomes May 2014 in addition, all subjects (clinicians and patients) provided informed consent. A clinician within the healthcare team (physician, nurse practitioner, physician assistant, or registered nurse) delivered the DAs to eligible patients facing an actual clinical decision. The interventions occurred in outpatient, inpatient, and emergency department settings in suburban and rural healthcare facilities in the upper Midwest; most trials were conducted in central and southeastern Minnesota. DAs displayed information about risks and benefits of pertinent options with most using risk prediction models to produce tailored estimates of risk (Figure I in the Data Supplement). Our DA format consists most often of a paperbased tool containing words, pictures, and pictographs that can be held by the clinician or patient during the shared conversation around treatment options. DAs were used during clinical encounters where there was a treatment decision to be made; the clinician agreed to participate along with their patient in the trial. Many of the encounters were videotaped, enabling measurement of patient engagement in SDM. Clinicians were provided a brief training on the data contained within the tool and suggestions for how to use the tool during the clinical visit. Patient eligibility criteria varied across trials. Eligible patients were adults expected to make a choice about a healthcare treatment or management strategy and who could provide written informed consent (ie, were able to read the consent form in English and did not have, in their clinician’s judgment, sensorial or cognitive limitations to do so). Outcome assessment was ascertained immediately after each visit by means of patient questionnaires.
Sociodemographic Variables Sociodemographic variables were collected from detailed chart reviews and patient questionnaires. Most variables (sex, education, income, and insurance status) were analyzed as categorical variables. Age, which followed a normal distribution based on the Kolmogorov–Smirnov test, was dichotomized to
