Shared Decision Making
Measuring Shared Decision Making
A Review of Constructs, Measures, and Opportunities for Cardiovascular
Care
Karen R. Sepucha, PhD; Isabelle Scholl, PhD
Shared Decision Making: Definition,
Framework, and Key Constructs
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One approach that supports increased patient engagement
in medical decision making is the model of shared decision
making (SDM). The concept of SDM was first described in the
1980s9,10 and is an interactive process between patients (and
family members) and the healthcare team to ensure that decisions are evidence based and patient centered. The key elements of SDM in the visit include defining and explaining the
problem, helping patients (and providers) to recognize that a
decision needs to be made, presenting options, discussing the
pros and cons (ie, the outcomes in terms of benefits and risks
and costs) of each option, eliciting patients’ goals and concerns
(ie, finding out what matters most to them), discussing the ability and self-efficacy of patients in implementing the options
under consideration, checking and clarifying the understanding, selecting a best option (or deferring decision pending test
results, second opinion, etc), and arranging follow-up.11
The goal of SDM is to ensure high-quality decisions.
Decision quality has been defined as the extent to which patients
are informed, meaningfully involved in the ­decision-making
process, and receive tests and treatments that reflect their
goals and concerns.12–14 The Figure was adapted from Sepucha
and Mulley15 and presents a simplified model of the medical
decision-making process. The circles highlight the key stakeholders, patients and families, providers, and the healthcare
organization within which they interact to make decisions. The
squares indicate the main activities: consultation(s), care being
delivered (this may be predominantly through the healthcare
system or through the patient and family in case of lifestyle
changes), and then outcomes that are experienced.
After the outcomes are experienced, 2 feedback loops
describe how information about outcomes and experiences of
patients is ideally used to improve quality of future decisions.
The top loop represents the incorporation of evidence (such as
the likelihood of different health outcomes for different patient
populations), and the bottom loop represents the incorporation
of goals and subjective experiences of patients (such as how
good or bad the different outcomes are to live with). Patient
decision aids, risk calculators, or other tools may be helpful to
translate information from these feedback loops in ways that
are accessible in routine care. Both streams of information are
needed to ensure informed, patient-centered decisions.
In the past few decades, patient-centeredness has gained
in importance,1,2 and policies to promote patient-centered
care have been increasing.3 Several different models of
­patient-centered care have been described. Although these
models vary on their specific definitions and dimensions, they
all emphasize the importance of facilitating the engagement of
patients in their own healthcare decisions.1,4,5
In some cases, facilitating patient engagement in decisions focuses on ensuring patients receive proven, effective
care and do not receive proven, ineffective care. In cardiology, these situations often correspond to American College of
Cardiology/American Heart Association class I or III recommendations with strong evidence, and the quality of decisions
can be measured by the percentage of eligible patients who
receive care that is consistent with the guidelines. Several
quality measures endorsed by the National Quality Forum for
cardiovascular care fall into these situations, such as the percentage of patients who receive β-blockers at discharge from
cardiac surgery or persistence of β-blocker usage after a heart
attack except for those patients at low risk.
However, even in situations with class I recommendations,
some questions arise in implementing these recommendations
into clinical care. For example, the high nonadherence rates
for β-blocker therapy (or statin therapy) suggest that patients
think differently about the tradeoffs between the potentially
modest survival gain and the side effects of the therapy.6,7 In
these cases, is it acceptable for patients to decline therapy, and
if so, what if any documentation might we need to be confident that the decision was informed and high quality?
As Brindis and Walsh8 highlight in their presidential address,
decisions, such as the use of statins for primary prevention,
treatment of atrial fibrillation, valve replacement (eg, the use
of tissue or mechanical valves), and use of implantable cardiac
defibrillators, are all common for which there is not 1 clear
best choice. In these situations, patients and providers need to
consider the evidence and preferences of individual patients to
select a treatment. Understanding and measuring the quality of
decisions when there are multiple appropriate options require
more than examining treatment rates.
From the Health Decision Sciences, Massachusetts General Hospital, Harvard Medical School, Boston, MA (K.R.S.); and Department of Medical
Psychology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (I.S.).
Correspondence to Karen Sepucha, PhD, Health Decision Sciences, Massachusetts General Hospital, 50 Staniford St, 9th Floor, Boston, MA 02114.
E-mail [email protected]
(Circ Cardiovasc Qual Outcomes. 2014;7:00-00.)
© 2014 American Heart Association, Inc.
Circ Cardiovasc Qual Outcomes is available at http://circoutcomes.ahajournals.org
1
DOI: 10.1161/CIRCOUTCOMES.113.000350
2 Circ Cardiovasc Qual Outcomes April 2014
The Importance of Measurement of SDM
It is important to have a means of measuring SDM to understand the effect of interventions, such as patient decision aids,
and the quality of decisions. Across the continuum outlined
in the Figure, different constructs can be measured to provide
insight into the decision-making process. These constructs are
divided into 3 broad categories:
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1.Decision antecedents or features of the patient, provider,
or organization that may influence the decision-making
process, such as the preference of patients for participation in decision making, health literacy of patients, competencies of a healthcare provider or skills in risk communication, or the availability of decision aids within a
healthcare system,
2.Decision-making process focuses on the features or behaviors in the consultations (such as level of patient involvement or engagement, type of topics covered in the
consultation, and use of decision support tools or methods), the amount and type of deliberation on the part of
patients and providers, and
3.Decision outcomes, such as decision quality, that include
knowledge and the extent to which patients receive treatments that match their goals, decision regret, and experience of patients with care.16
The work of the International Patient Decision Aids
Standards group has established consensus around the
importance of measuring the involvement of patients in the
­decision-making process and decision quality to evaluate the
effect of patient decision aids.14
For any given study, the most appropriate measures will
be determined by the purpose of the study. Those interested
in conducting research, for example, will tend to have more
detailed assessments, possibly at multiple time points, to
understand the effect of interventions or explore relationships
between different constructs. There have been several randomized studies of patient decision aids in heart disease. For
instance, the 2011 Cochrane Collaborative systematic review
of patient decision aids included 8 trials of decision aids for
cardiovascular disease.17 Three studies were about treatment
of atrial fibrillation,18–20 2 about coronary revascularization,21,22
and 3 about prevention of heart disease.23–25 Other than patient
demographics, few of these studies measured any decision
antecedents. Three used some measure of preference of a
patient for participation in decision making. About half of the
studies included some measure of the d­ ecision-making process, 4 used the Decisional Conflict Scale (DCS), and 1 used
an unnamed survey of the ­decision-making process. Most
included an assessment of ≥1 decision outcomes. None had a
Figure. A model of medical decision making that highlights different measurement constructs. SDM indicates shared decision making.
Reprinted from Sepucha and Mulley.15 Authorization for this adaptation has been obtained from the owners of the copyright in the original
work and the translation or adaptation.
Sepucha and Scholl Measuring Shared Decision Making 3
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comprehensive assessment of decision quality, but many (7 of
8 studies) assessed 1 aspect of decision quality, knowledge of
patients. Three also measured realistic expectations (or quantitative understanding of the risks and benefits of patients),
and 3 assessed satisfaction with the decision-making process.
As this subset of studies from the Cochrane review illustrate,
there is wide variation in the measures used for evaluating
patient decision aids.
Those interested in incorporating SDM in clinical care and
conducting quality improvement studies often prefer shorter
instruments that only include actionable information that can
be easily integrated into routine care. One example of this type
of use is described in Stacey et al26 for chronic pain caused by
osteoarthritis. Patients are surveyed shortly after watching a
decision aid and before heading to the specialist on a series of
health-related outcomes (functional status, pain, and quality
of life) and decision-making areas (knowledge, goals, decisional conflict, and treatment preferences). The data are then
summarized in a report that provides an assessment of appropriateness of surgery for knee or hip osteoarthritis. Clinicians
can use the report to address any knowledge gaps and ensure
that treatments are tailored to patients’ goals and preferences.
An example of such a patient report is shown in Figure 1 of
Stacey et al.26
SDM is now mentioned in regulations for patient-centered
medical homes and accountable care organizations, which has
resulted in increasing interest from healthcare administrators
for reliable and valid means to document whether it has happened.27 For example, an administrator may be interested in
understanding whether the rates of percutaneous coronary
interventions for stable angina are appropriate at their center. A process measure of SDM for this situation might be
the percentage of patients who received a decision aid before
having the procedure. An outcome measure might be the percentage of patients who reported high decision quality (eg,
the percentage of patients who met an established knowledge
threshold and stated a clear preference for percutaneous coronary intervention over medical management or bypass surgery before undergoing percutaneous coronary intervention).
Another approach that administrators might take is to use the
SDM component of the Consumer Assessment of Healthcare
Providers and Systems (CAHPS) survey, which includes
items that assess whether patients were meaningfully involved
in selecting treatments, the Patient-Centered Medical Home
version, the Accountable Care Organization version, and the
Surgical version of CAHPS include items relating to the decision-making process.28–30
Criteria for Evaluating the Quality of Measures
Several different organizations have developed frameworks
for evaluating survey instruments. One framework is the
­Consensus-Based Standards for the Selection of Health Measurement Instruments Checklist for assessing the methodological quality of studies on measurement properties, which
was developed by an international, multidisciplinary expert
group.31–33 The Consolidated Standards of Reporting Trials
(CONSORT) group has also done considerable work to develop
standards for reporting on patient-reported outcome measures,
many of which are applicable to measures of SDM.34 Table 1
gives a brief overview of important criteria from these frameworks that are relevant to SDM measurement that researchers
can use to evaluate the quality of an instrument.35–38 In the literature on health measurement scales, many different criteria
and definitions exist. Interested readers can find more details in
the books of Streiner and Norman38 or Fowler.39
Review of Key Measurement Instruments
There are many measures to assess the different aspects of
SDM. In a recent review, Scholl et al16 identified 28 instruments that have undergone psychometric testing and that
assess decision antecedents, the decision-making process, or
decision outcomes. This review concluded that most of the
existing scales showed good results on reliability and factorial
structure of the measures. However, they also found that many
measures need to be tested further, especially on aspects of
validity. Other studies have found limited reporting on measures used in SDM studies, which renders an appraisal of the
available measures difficult.16,40 An analysis of the measures
used in the 86 randomized controlled trials that are included
in the 2011 Cochrane review of patient decision aids found
that for many measures there is a lack of reporting on the performance of measurement instruments used in the studies.41
For investigators who plan to conduct research on SDM
or to incorporate SDM into clinical practice, several helpful resources on measures of SDM are listed in Table 2. In
the remainder of this section, we highlight several examples
of published measures that assess decision antecedents,
­decision-making process, and decision outcomes and discuss
some strengths and weaknesses.
For decision antecedents, 1 widely used instrument is the
Control Preference Scale that assesses the preferred role of a
patient in decision making. It was developed by Degner and
Sloan,42 and it has undergone psychometric testing in different settings and samples.43–46 Originally designed as a set of
cards, the content has been adapted for written surveys, and
the measure has been positively evaluated along several criteria, including appropriateness, face validity, responsiveness,
interpretability, precision, acceptability, and feasibility.40
In the category of measures that assess the decision-making process, we highlight 4 measures. The Observing Patient
Involvement (OPTION) Scale assesses the decision-making
process from a perspective of external observer, by rating
either a directly observed consultation or audio- or video
-recorded consultations.47 Although several studies found
good reliability of the instrument, for example, in a study on
primary care visits on cardiovascular prevention,48 it could
benefit from further testing about validity.49 One of the main
advantages of the OPTION Scale instead of other observer
scales is that it has been widely used in the research context
for the past years49 also in studies on SDM in cardiology.50
Another benefit is that the original English version has been
translated into 7 different languages (Chinese, Dutch, French,
German, Italian, Spanish, and Swedish) and thus allows crosscountry comparisons of research results.
Another instrument is the 9-item SDM Questionnaire. It has
2 versions: 1 that assesses the perspective of a patient (SDMQ-9)51 and another that assesses the perspective of a healthcare provider (SDM-Q-Doc).52 Both versions have shown
4 Circ Cardiovasc Qual Outcomes April 2014
Table 1. Checklist for Selecting a Measure35–38
Quality Criteria
Description/Definition
1. Appropriate for study
Does the instrument assess a construct that answers the research questions of the study?
2. Strong Development Process
Was there a structured development process for generating the items? Was there explicit connection to
relevant decision-making theories or conceptual frameworks? Was there any cognitive testing to ensure
that respondents interpret items and responses as intended? Was there any pilot testing in the intended
sample or setting?
3. Evidence of strong psychometric properties
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Reliability
How consistent is the measure? Does it tend to produce the same information when used in similar
conditions? Aspects of reliability include internal consistency, inter-rater reliability, and test–retest reliability
Validity
How well does the instrument measure what it is intended to measure? Commonly reported types of
validity are face validity, content validity, construct validity (eg, convergent and divergent validity, structural
validity), and discriminant validity
Objectivity
Objectivity is defined as the extent to which the administration, scoring, and interpretation of the
measurement result are independent of the rater who is in charge of these tasks
4. Evidence it is clinically meaningful
Responsiveness
Is the measure able to identify changes (eg, because of a certain intervention) of importance to patients and
clinicians?
Interpretability
Is there evidence that the scores are meaningful to clinicians and patients?
5. Evidence it is practical
Acceptability
Is the length and content acceptable to the respondent as evidenced by low missing data and high response
rates?
Feasibility
Is the instrument easy to administer, score, and interpret?
to be acceptable, reliable, and have factorial validity51,52; a
US-based study found indicators of convergent validity.53 The
patient version has been tested in a subsample of patients with
cardiovascular disease, showing appropriate psychometric
properties.51 One of the main advantages is that they are brief
tools with 9 items only. A further benefit is that the original
German version has been translated into 9 other languages
(Dutch, English, French, Hebrew, Italian, Japanese, Korean,
Persian, and Spanish) and thus also offers a possibility to compare research results across countries or to pool data.
A third instrument to measure aspects of decision process
is the DCS.54 The DCS is 1 of the most widely used measures
in studies evaluating patient decision aids.41 There are several versions of the scale available, and the original includes
16 items in 5 subscales: uncertainty, informed, values clarity,
support, and effective decision.54 The instrument has demonstrated strong psychometric properties in varied treatment
contexts and patient samples40 and has undergone psychometric testing in a subsample of patients with cardiovascular
disease.55 The DCS has also been translated into several languages (Chinese, Danish, French, German, and Spanish).
A final instrument worth mentioning is a supplemental
set of SDM items now included in several versions of the
CAHPS surveys. A screener item identifies respondents who
report making a decision about starting or stopping a medication or having a procedure. The items were adapted from the
Decision Quality Instruments (DQI; described in more detail
in the next paragraph) and are fairly generic (eg, “Did any
of your healthcare providers discuss the reasons not to have
the medication/procedure?”) and often require respondents to
Table 2. Resources for Finding Measures of Shared Decision Making
Resource
Description
Link/Reference
NCI GEM-Shared Decision Making
measures database
A project initiated by the NCI that aims to harmonize measures
of SDM.
Key constructs and measures along with formal and informal
ratings are available on the site.
www.gem-beta.org
OHRI website
The OHRI website has a section on evaluation that includes
access to instruments and scoring guides.
https://decisionaid.ohri.ca/eval.html
Massachusetts General Hospital HDSC
website
The HDSC website provides access to the decision quality
instruments and scoring guides.
http://www.massgeneral.org/decisionsciences/
CAHPS
CAHPS clinician and group surveys include items related to
shared decision making for medication decisions.
http://cahps.ahrq.gov/clinician_group/
CAHPS indicates Consumer Assessment of Healthcare Providers and Systems; GEM, grid enabled measures; HDSC, Health Decision Sciences Center; OHRI, Ottawa
Health Research Institute; NCI, National Cancer Institute; and SDM, shared decision making.
Sepucha and Scholl Measuring Shared Decision Making 5
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reflect back over a period of time (“In the last 6 months, how
often did your healthcare providers…”). CAHPS is the most
widely used patient experience survey, and incorporation of
an assessment of SDM, even if fairly general, will provide an
opportunity to gather evidence on SDM practices in primary
care and specialty providers in different settings.30 Some of the
challenges of the CAHPS survey is the retrospective sampling
and the lack of any clinical information on the actual decision.
Finally, we highlight 2 different measures that assess decision outcomes, one measuring decision quality and another
measuring decision regret. DQIs have been developed for
a range of common healthcare decisions, and they measure
knowledge and the extent to which patients receive treatments
that match their goals.12 The DQIs are decision specific and
include multiple -choice knowledge items that are used to create a knowledge score and items to assess goals of patients and
treatment received, which are then used to create a concordance
score (reflecting percentage of patients who receive treatments
that match their goals). Fourteen different DQIs are available
for download (http://www.massgeneral.org/decisionsciences/).
The DQIs for surgical treatment of breast cancer, herniated
disc, and knee and hip osteoarthritis have demonstrated strong
content and discriminant validity, as well as adequate retest
reliability, and are acceptable to patients and feasible to implement.56–58 The hip and knee osteoarthritis DQIs formed the
basis for the quality improvement project referenced earlier in
Ontario where patients are routinely screened for their knowledge and goals in advance of a specialist visit.26 One advantage
of these instruments is the rigorous development process and
strong psychometric properties; however, a disadvantage is the
decision-specific nature of the surveys, which limits their feasibility and generalizability across topics.
A final outcome measure is the Decision Regret Scale, a
5-item scale used to assess the level of distress or remorse of
patients about a decision that has been made.59 It has demonstrated strong internal consistency and has been shown to
correlate with decision satisfaction, decisional conflict, and
quality of life.60
This section provides a brief summary of some of the common measures. For those interested in more details, please see
the resources in Table 2 (eg, the Ottawa website has detailed
documents on the DCS and the Decision Regret Scale).
Furthermore, readers may be interested in the review by
Scholl et al,16 who examined measures of SDM, or the review
by Kryworuchko et al,40 who examined common primary outcome measures used in randomized trials of decision aids,
including the DCS and the Control Preference Scale.
Current Opportunities
The field of research on SDM in general and measurement
of SDM in particular has grown considerably for the past
few decades. Recently, it has received a lot of attention at
the health policy level, and as a result, the call to measure its
constructs has intensified. At this point in time, there is no
consensus on a core set of measures, and the evidence available on the performance of published measures is variable.
Efforts are underway to move toward consensus on a set of
core constructs to be measured, and in particular, the International Patient Decision Aids Standards group has emphasized
the importance of measurement of decision process and decision quality for evaluating the effectiveness of decision aids.
Whether the intervention is a decision aid, health coach, or
other tool, there is considerable support for the importance
of measuring decision quality, defined as the extent to which
patients are well informed, are meaningfully involved, and
receive treatments that match their goals.
There are several opportunities for research in the field of
SDM measurement. As in any field, there is always a need for
further evaluation of existing measures to ensure that the quality criteria described in Table 1 are fulfilled in varied settings
and samples. A core challenge for measurement of SDM is
that no gold standard exists; as a result, establishing validity
is difficult. Many evaluations of the instruments have focused
on reliability or face validity, and few have demonstrated construct or discriminant validity. For example, a small study
examining the ability of patients to categorize the role they
played in decision making (ie, passive, shared, and active)
showed little correlation between patient reports and survey
responses.61 Another important area in need of further research
is examining whether SDM is associated with changes in
the use or improvements in health outcomes. Some studies
suggest that patients who are more engaged and involved in
selecting treatments will have better adherence or perhaps less
use of invasive procedures; however, the data are limited. It
will be important to build the evidence base for these measures, particularly if they are being considered as part of largescale quality or performance measurement programs.
Further development of the conceptual issues related to
SDM is also important; for example, a fundamental tension
exists in the field between investigators who think that interventions should be designed to match preferences of patients
for involvement in decision making and those who think that
interventions should be designed to activate and increase
patients’ desire for involvement. There are also gaps in our
understanding of whether and how to incorporate different
perspectives (eg, the patients, the providers, the caregivers, and
external observers). Several current studies have shown that
assessing SDM from a perspective of external observer leads
to different results than using patient- or ­clinician-reported
measures.50,62–65 To date, no solution has been found to address
this issue, which makes it difficult to decide whose perspective to use (eg, as primary outcome in an intervention study).
Furthermore, there is the question of most appropriate timing
of the assessments (eg, should knowledge be assessed before
or after a consultation or a procedure? How long after the decision should one wait to assess decisional regret?).
Some of these issues have a particular relevance in cardiovascular disease, where decisions involve multiple specialists
and unfold over time. For example, determining decision quality for revascularization decisions can be complicated. Should
one assess knowledge before the angiography, as patients may
come out of that procedure with a stent? Which physician, primary care, cardiologist, cardiac surgeon should be responsible
for ensuring patients have an adequate understanding of their
options and that the procedure matches the patients’ goals?
How much emphasis, if any, should be given to SDM in quality metrics—for example, if a patient meets all the clinical criteria for a procedure, is that enough, or do healthcare systems
6 Circ Cardiovasc Qual Outcomes April 2014
and providers also need to document that the patient is well
informed and has clear preference for the procedure?
There is an important role to be played by investigators and
clinicians in cardiovascular disease to advance the field of
SDM. Cardiology has been at the forefront of collecting and
reporting of outcomes. However, the major cardiology registries do not include any patient-reported measures. There is
an opportunity to build on the strong commitment to capturing outcomes to include patient-reported outcomes (eg, angina
and quality of life) and aspects of decision-making process and
decision outcomes (eg, decision quality and decision-making
process) in registries. Doing so will help provide a clearer picture of the appropriateness of procedures and would enable
assessment that the patients are not only clinically appropriate for a particular procedure but also provide some assurance
that it reflects informed preferences of patients.
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Acknowledgments
We thank Eva Christalle and Evamaria Müller for help with
­copy-editing the article.
Disclosures
Dr Sepucha receives salary and grant support from the Informed
Medical Decisions Foundation. Scholl reports no conflicts.
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Key Words: decision making ◼ psychometrics
Measuring Shared Decision Making: A Review of Constructs, Measures, and
Opportunities for Cardiovascular Care
Karen R. Sepucha and Isabelle Scholl
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Circ Cardiovasc Qual Outcomes. published online May 27, 2014;
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