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date: 22 March 2019

Shared Decision-Making for Treatment Planning in Mental Health Care: Theory, Evidence, and Tools

Abstract and Keywords

Shared Decision-Making (SDM) for treatment planning in mental health care is an interactive process through which clinicians and patients collaboratively determine the course of treatment. This chapter is divided into six sections, the first two of which provide a historical perspective on treatment planning and SDM in particular and discuss the core tenets and components of the SDM model. The third section details the key arguments in favor of SDM and summarizes the existing empirical support for the usefulness of SDM. The fourth and fifth sections focus on the components of SDM and implementing SDM in practice, and the final section proposes future directions for the field as SDM becomes more prominent in clinical care.

Keywords: shared decision-making, treatment planning, mental health, decision-making, psychotherapy

Providing high-quality mental health care requires a thoughtful treatment plan at the start of each treatment episode. Whether providing treatment for adult depression, child anxiety, or any other condition, commonly accepted standards of practice dictate establishing, at the outset of treatment, a preliminary understanding of the presenting problem, at least one treatment goal to work toward, and a treatment plan that details how the proposed intervention may achieve the selected goal (American Psychological Association Presidential Task Force on Evidence-Based Practice, 2006; American Psychological Association Task Force on Evidence-Based Practice for Children and Adolescents, 2008). Although there are many approaches to creating treatment plans, at the heart of any treatment planning model is a process to translate knowledge into decisions. Variations across treatment planning models reflect differences in how sources of knowledge are prioritized and integrated, which stakeholders use the knowledge to make decisions, and the degree to which stakeholders’ values are considered when decisions are being made. The past several decades have seen a significant change in how treatment is planned.

Shared decision-making (SDM) models of treatment planning, in which health care providers and patients1 work together to identify treatment targets and decide on a course of action, have become more prominent. On a principles-based level, having patients play an active role in their care aligns with societal values emphasizing patient empowerment and autonomy (Beauchamp & Childress, 2012) and may enhance patient satisfaction with treatment (Katon et al., 1999; Loh et al., 2007b). On an outcomes-based level, patient inclusion in the treatment planning process may increase engagement in treatment tasks (e.g., completion of therapy homework, taking medication adherently) and may also facilitate the development of more effective treatment plans. Research is beginning to provide support for each of these propositions, though the empirical evaluation of SDM models of treatment planning for psychological treatments is a nascent area of study. This is unfortunate, as SDM models are especially relevant to mental health treatments, which often require continued patient commitment to address illnesses that are chronic in nature (World Health Organization International Consortium in Psychiatric Epidemiology, 2000; Montori, Gafni, & Charles, 2006). The present chapter discusses the history and core tenets of the SDM model, the empirical support for the SDM model, applying the SDM model to clinical care, and future directions for the field as SDM is more widely disseminated and implemented.

Historical Perspective on Treatment Planning

In Western medicine, from the time of Hippocrates (~400 bc), the prevailing approach to treatment planning has been “Doctor knows best.” In fact, the first Code of Ethics of the American Medical Association advised physicians not to consider patient perspectives in medical care (American Medical Association, 1848) an advisement that persisted during the early years of psychosocial treatment development. In the traditional, paternalistic framework, the care provider is the sole expert and leader of the treatment process. It is the patient’s responsibility to report his or her symptoms accurately and adhere to the treatment regimen prescribed by the care provider (Emanuel & Emanuel, 1992; Parsons, 1951). This approach conflicted with the rise of the consumer movement in the post-World War II era, putting it at odds with growing patient demands for autonomy in health care decisions (Balint & Shelton, 1996; Lerner, 2004). As principles of patient autonomy became more prominent in the mid-twentieth century, the paternalistic model shifted to one of informed consent (Faden, Beauchamp, & King, 1986). The care provider was still considered the sole expert in the treatment process, responsible for treatment decisions, but there was now an expectation that the patient would be fully informed of the diagnosis and treatment plan, and that the patient would consent to follow the treatment plan as prescribed by the care provider. This modification was powerful in giving more information to patients and in more concretely providing patients the right to refuse compliance with an unwanted treatment plan.

The push toward patient autonomy that led to SDM was advanced further by the work of Wennberg and colleagues (e.g., Wennberg & Gittelsohn, 1973), which documented how care providers’ medical decisions for substantively similar cases varied widely, often by geographic location. This “practice variation phenomenon” suggested that, without clinical science to support a given course of action, providers’ recommendations were subjective, based on their own value systems and neglecting patient preferences. The patient-centered care model, which gained prominence throughout the 1990s partly in response to practice variation findings, supported providing care that is responsive to individual patients’ preferences, needs, and values (Institute of Medicine, 2001). This coincided with other efforts to reduce non-indicated practice variation by standardizing treatments across settings (e.g., treatment manuals, clinical practice guidelines). Since the 1990s there has been a growing push not only to target patient agreement with the treatment plan but also to have patients actively included in the treatment planning process (e.g., considering treatment options and choosing the treatment that best fits the patient’s values and goals). Thus SDM became one of the most-used of a set of terms describing the active involvement of patients in medical decision-making (Charles, Gafni, & Whelan, 1997, 1999). Although they are more commonly acknowledged and tracked, high rates of practice variation remain (Wennberg, 2011), emphasizing the need for continued work on this front.

The first definition of SDM was provided in 1982 by the President’s Commission for the Study of Ethical Problems in Medicine and Biomedical and Behavioral Research, and it focused more on the informed consent component of SDM, grounded in the bioethical principle of autonomy. In 1997, Charles and colleagues wrote a seminal paper arguing for conceptual clarity of SDM, distinguishing it from other treatment planning models and describing the key attributes of SDM. Soon thereafter, an edited book on evidence-based patient choice was published (Edwards & Elwyn, 2001). In 2001, the first International Shared Decision Making Meeting (ISDM) was held in Oxford, England (Holmes-Rovner & Rovner, 2009), and other SDM-related initiatives, such as the International Patient Decision Aid Standards (IPDAS) collaborative (O’Connor, Llewellyn-Thomas, & Stacey, 2005), founded in 2003, followed. During this time, statements of support for patient-centered care in alignment with SDM became more prominent. In the United States, proponents included the President’s New Freedom Commission on Mental Health (2003), the Institute of Medicine (2006) and the Substance Abuse and Mental Health Services Administration (SAMHSA, 2006). In 2006, Wills and Holmes-Rovner published an article focused on the integration of decision-making and mental health interventions research, arguing for the relevance of SDM to mental health care and the need to consider SDM processes when designing and evaluating interventions. In 2007, SAMHSA sponsored a meeting of experts and stakeholders in SDM and mental health, producing a detailed report (SAMHSA, 2010) on what SDM is, the practice of SDM in mental health care, the state of SDM research, and future directions. As detailed in this chapter and displayed in Table 1, there have been many recent research efforts to understand and test the impact of SDM approaches and constructs in mental health care, and an exponential increase in publication rates of SDM articles (including all areas of health care) from 1996 through 2011 (Blanc et al., 2014).

Core Tenets of the Shared Decision-Making Model

Although the article by Charles et al. (1997) has often been cited as a seminal publication on SDM, there are several key publications offering definitions of SDM (Coulter, 1997; Towle & Godolphin, 1999; Elwyn, Gray, & Clarke, 2000; Elwyn, Edwards, Kinnersley, & Grol, 2000). Makoul and Clayman (2006) published a review of the varying definitions, highlighting areas of overlap and also points of divergence. The lack of a commonly accepted definition of SDM reflects both the relative recency of the concept and also the richness of the theoretical discussions, yet it is also problematic in that it prevents consistent measurement of SDM and restricts the possibility of comparing various research programs. Researchers have been encouraged to make clear which definition of SDM they are using for a given project or publication. In the present chapter, we adopt the Charles, Gafni, and Whelan definition (1997), which states that though the application of SDM varies by setting and population, at a fundamental level, SDM requires that

  1. 1. At least two people are engaged in the decision-making process. Shared implies that the decision will be a mutual one, and at least the clinician and the patient must be included, and engaged, in the decision-making process. At times, there may be others involved as well (e.g., a treatment team, other family members), but the patient and clinician constitute the core of the SDM team. The degree to which the patient is included and the ways in which the patient participates may vary based on the patient’s desire to participate (Edwards & Elwyn, 2006).

  2. 2. Each person shares information. Transfer of knowledge is bi-directional. The patient is in the best position to share information about his or her specific symptoms, preferences, values, and goals. The clinician has expertise with the development and treatment of a given disorder. In order for an optimal decision to be reached, each participant in the decision-making process must have at least a basic understanding of the other’s perspective. Patient informational needs often differ from those of healthcare providers, and variance between patients is high, so having the ability to explain the information and answer questions is important (Feldman-Stewart et al., 2013).

  3. 3. Decisions are made collaboratively. Not only are both clinician and patient engaged in the decision-making process, the decisions are made through a process of discussion, compromise, and agreement. The degree to which clinician and patient deliberate, though, may vary based on the particular decision and decision-making context (Bunn, O’Connor, Tansey, Jones, & Stinson, 1997; Elwyn & Miron-Shatz, 2010).

The theoretical underpinnings of SDM are based on decision-making theory. Broadly, decision-making theories can be divided into those that are prescriptive (i.e., theories that identify what “good decision making” is and how to help individuals make good decisions) and those that are descriptive (i.e., theories explaining how individuals and groups actually make decisions) (Fagerlin et al., 2013). The degree to which different components of SDM are emphasized may vary based on the decision-making theory being employed. For example, the expected utility theory (Schoemaker, 1982), a prescriptive approach, defines expected utility as the product of an outcome’s probability and the value/utility of that outcome. This framework proposes that the “best” decision is one in which the expected utility is maximized. An SDM session under the expected utility theory framework would emphasize clarifying the likelihood of different outcomes for the patient, and helping the patient identify the strength of different values to determine the expected utility of each choice available to the patient. The prescriptive approaches hold promise for creating SDM protocols that maximize decision quality, but without an understanding of how decisions are actually made, it will be challenging to change clinicians’ approaches to decision-making. Descriptive theories aid in providing that understanding. For example, studying decision-making descriptively has shown that individuals tend to process information either heuristically (i.e., unconsciously and intuitively) or systematically (e.g., consciously and analytically) (Evans, 2008). This has implications for SDM models in many ways, among them, the likelihood that individuals will use heuristic processing when making personal decisions, choosing the first acceptable option rather than considering all options fully to identify the best option (see discussion in Bekker, 2009). A more comprehensive discussion of decision-making theories and their relation to SDM can be found in Bekker (2009) and Fagerlin et al. (2013).

Rationale and Empirical Support for Shared Decision-Making

Support for SDM models of treatment planning can be divided into two overarching arguments. The first, a principles-based argument, proposes that, irrespective of any potential measurable outcomes of implementing SDM, it is inherently good as a treatment planning model because it is consistent with the value society has placed on patient autonomy and patient-centered care. The second, an outcomes-based argument, proposes that SDM improves treatment processes, treatment outcomes, or both. To evaluate the outcomes-based argument, we reviewed the growing evidence base addressing the measurable effects of SDM.

The principles-based argument asserts that there is a benefit to SDM independent of any measurable SDM outcomes. Four principles have traditionally guided medical care: respect for persons/autonomy, justice, non-maleficence (do no harm), and beneficence (do good). Mental health outcomes research has focused almost exclusively on the principles of beneficence (maximizing treatment effectiveness) and non-maleficence (minimizing side effects), to the detriment of autonomy. Autonomy is exercised when one acts intentionally, with understanding, and without controlling influences that determine action (Beauchamp & Childress, 2001). Shared decision-making attempts to balance the principles of autonomy and beneficence/non-maleficence; it enables patients to exercise their autonomy and make decisions, ideally based on research evidence, that will raise the likelihood of outcomes they view as favorable and decrease the likelihood of side effects they view as aversive. By equipping patients with knowledge that empowers them to make informed decisions, SDM aims to diminish the imbalance in knowledge between clinicians and patients (Charles et al., 1997), which serves to create a clinician–patient team, working together to make decisions. Despite concern that people with mental illness may not be competent to make treatment decisions, research has shown that patients with severe mental illness can exercise autonomy (Hamann et al., 2006); in cases where competency is compromised, patients should be afforded as much autonomy as their level of competency allows. Shared decision-making ensures that treatment is collaborative, carried out with patients and not done to them (Schauer, Everett, Del Vecchio, & Anderson, 2007) and protects the self-evident right of patients to make choices about their own health—a right that research demonstrates almost all patients strongly desire (Benbassat, Pilpel, & Tidhar, 1998). The evidence shows that patients prefer having an active role (Adams, Drake, & Wolford, 2007) and report wanting more information regardless of symptom severity or age (Loh et al., 2004; O’Neal et al., 2008).

The changing international health care agenda has reflected this modern understanding of what patient autonomy entails by arguing for increasingly patient-centered care. In the United Kingdom, the Department of Health (2000) declared that the future of health care services should be designed around the patient. In 2003, the Commission for Patient and Public Involvement in Healthcare was established with the intention of acting “as a champion for patients nationally” (House of Commons Health Committee, 2003). With this establishment, the government of the United Kingdom committed itself to incorporating patient input into health care decisions and delivery of National Health Services. In 2010, a Salzburg Global Seminar convened to discuss the role of SDM in global health, releasing a statement calling on clinicians, researchers, policy makers, and patients to promote the use of SDM throughout all areas of health care (Salzburg Global Seminar, December 2011). In the United States, the National Institute of Mental Health committed itself to the incorporation of clinician and patient decision-making processes into intervention research (National Institute of Mental Health, 1999). In their 2001 report Bridging Science and Services, the Institute of Medicine echoed this priority and included patient-centered care as one of its six expressed aims for improving health and functioning of individuals in the United States. Included in their “rules for redesign,” were mandates that knowledge be freely shared, that the patient be afforded the opportunity to control personal health-care decisions, and that the health-care system accommodate varying patient preferences, and “encourage shared decision making” (Institute of Medicine, 2001). Perhaps an even more powerful endorsement was the Affordable Care Act (ACA) of 2010. Section 3506 of the ACA funds an independent entity charged with developed consensus-based standards and certifying patient decision aids to be used by federal health programs and other interested parties. The ACA further affords the Secretary of Health and Human Services with the power to fund the development of such evidence-based decision aids (Oshima Lee & Emanuel, 2013). The Patient Protection and Affordable Care Act also created the Patient-Centered Outcomes Research Institute (PCORI), a research institute intended to fund and promote comparative effectiveness research to equip decision-makers (patients, clinicians, policy makers) with evidence about diseases, disorders, and health conditions that can be prevented, diagnosed, treated, monitored, and managed (Selby, Beal, & Frank, 2012). The institute specifically emphasizes the patient’s role in assessing health care options. It is clear that many of our governments’ goals have aligned with our profession’s ethical guidelines to promote SDM in health care.

Given the increased societal and rhetorical support for SDM, it is important to test the degree to which it is empirically supported as well. Empirical support for SDM has been mixed but generally shows evidence of associations with symptom improvement, increased patient satisfaction, greater patient knowledge, better provider–patient relationships, increased patient engagement in treatment, and decreased stigma (Simon, Wills, & Harter, 2009). A selected sample of observational studies of SDM and related constructs in mental health care is displayed in Table 1. Together, these observational studies indicate that there are significant barriers to patient participation in decision-making (Simon, Loh, Wills, & Harter, 2007) and that providers vary in their inclusion of patients in SDM, with some clinicians rarely discussing patient preferences (Goossensen, Zijlstra, Koopmanschap, 2007; McCabe, Khanom, Bailey, & Priebe, 2013). Yet, patients are more adherent to treatment (Loh et al., 2007a), more satisfied (Swanson et al., 2007; Tambuyzer & Audenhove, 2013), less impaired (Butler, 2014), and experience less stigma (Butler, 2014) in the presence of greater SDM; when patients do not feel involved in clinical decisions, they often report that they would have made different decisions should they have been included (Hamann et al., 2008).

Table 1. Selected Observational Studies of Shared Decision-Making (SDM) and Related Constructs

Authors

Sample/Diagnosis

Outcomes

Results

Butler (2014)

36 parents of young children with externalizing mental health problems in primary care

Frequency of SDM; relationship of SDM with child impairment and mental health treatment stigma

More frequent SDM was associated with lower impairment and mental health treatment stigma; SDM frequency did not significantly relate to mental health problem severity

Goossensen et al. (2007)

61 adults, attending outpatient psychiatry clinic, no diagnoses specified; 8 clinicians

Recorded sessions rated with Observing Patient Involvement in Treatment Choices instrument (OPTION); patient satisfaction with clinician communication

Clinicians scored low discussing patient preferences to participate in decision-making, yet did well eliciting patient questions; patient satisfaction remained high

Hamann et al. (2008)

60 adults, schizophrenia, inpatient; 30 psychiatrists

Clinical decisions

Patients who did not feel involved reported significantly more often that they would have decided differently

Loh et al. (2007a)a

405 adults, depression, primary care

Involvement, treatment adherence, clinical outcome

Participation in treatment predicted adherence; adherence predicted clinical outcome

McCabe et al. (2013)

72 adults, schizophrenia or depression, psychiatric outpatient visits

OPTION scale ratings of clinical encounters

Negative symptoms were associated with less SDM; length of visit was not associated with SDM; psychiatrists varied significantly on patient involvement in decision-making

Simon et al. (2007)

40 adults, depression, convenience sample from outpatient, inpatient, and self-help group treatment

Perceptions of previous treatment decision-making and barriers to participation in decision-making coded from qualitative interviews with patients

Barriers included low patient perception of depression severity and need for treatment, concerns about stigma, and confusion about selecting among many different treatment options

Swanson et al. (2007)

1,317 adults, depression, primary care

Patient satisfaction with mental health care using author-developed scale

SDM and receipt of mental health care were positively associated with patient satisfaction

Tambuyzer & Audenhove (2013)

111 adults, serious and persistent mental illness, multidisciplinary care networks

Author-developed patient satisfaction scale and The Dutch Empowerment Scale

Patient involvement in care positively correlated with patient satisfaction and empowerment.

Matching superscript letters following the author name(s) and publication date indicate separate papers reporting results based on the same sample.

Table 2 includes selected studies on researcher-initiated SDM interventions (as opposed to the observational studies in Table 1). Given the close relationship between decision aids and shared decision-making, Table 2 includes interventions using decision aids in addition to more comprehensive shared decision-making practices. Decision aids, described in greater detail below, are intended to help patients participate in decision-making and have been shown to improve patient knowledge and elucidate values (Stacey et al., 2014). The evidence base suggests that SDM interventions can improve outcomes (Clever et al., 2006; Joosten, de Jong, de Weert-van Oene, Sensky, & van der Staak., 2009; Lin et al., 2005; Malm, Ivarsson, Allebeck, & Falloon, 2003), although the findings remain mixed (e.g., Stein et al., 2013; van der Krieke et al., 2013).

Table 2. Selected Studies on Shared Decision-Making Interventions

Authors

Sample/Diagnosis

Intervention

Outcomes

Results

Brinkman et al. (2013)

54 parents of children with ADHD in primary care; 7 pediatricians

Decision aids to plan for childhood ADHD treatment

OPTION scale ratings of recorded encounters; parent knowledge of ADHD treatment options

Intervention increased OPTION scores (SDM); parents were better informed about treatment options without increasing visit duration

Clever et al. (2006)

1,706 adults, symptoms of depression in primary care

Training primary care clinicians in guideline- concordant care

Frequency of guideline- concordant care; patient involvement

Greater patient involvement was associated with greater likelihood of guideline-concordant care and clinically significant improvement in depression over time

Deegan, Rapp, Holter, & Riefer (2008)

189 adults, psychotic disorders, mood disorders, and co-occurring substance use disorders

Peer-run Decision Support Center (DSC) with an Internet-based software program

Participation in intervention and patient’s opinions about treatment assessed by focus groups with medical staff, patients, case managers, and other staff

Patients were actively involved in their participation in the psychiatric consult; DSC helps them tell their “story;” agenda of psychiatric consult is more likely to focus on patient needs and desires

Hamann et al. (2006)b

107 adults, schizophrenia, inpatient

Decision aid, planning talk

Knowledge about disorder and treatment; patient involvement

Increased patient knowledge and perceived involvement; SDM feasible for most patients; no increase in consultation time

Hamann et al. (2007)b

107 adults, schizophrenia, inpatient

Decision aid, planning talk

Rehospitalization frequency; medication compliance

No clear beneficial effect on long-term outcomes; positive trend toward fewer hospitalizations in intervention group

Joosten et al. (2009)c

220 adults, substance-dependence, inpatient

Randomized to SDM intervention consisting of structured procedure to reach agreement or control condition

Substance use and addiction severity; quality of life

Both groups showed significant decreases in substance use and addiction severity and increases in quality of life; SDM intervention showed significantly better improvements in addiction severity than control group.

Joosten et al. (2011)c

212 adults, substance-dependence, inpatient

Randomized to SDM intervention consisting of structured procedure to reach agreement or control condition

Patient autonomy as measured by independent and control behavior on the Interpersonal Checklist-Revised (ICL-R)

SDM intervention was associated with increase in patient autonomy (independent behavior) and control behavior

Katon et al. (1999)

228 adults, depression, primary care

Enhanced education for patients; increased frequency of physician visits

Medication adherence; satisfaction with care; depression severity

Significant improvements in medication adherence; patients in intervention group more satisfied with care and outcome

Lin et al. (2005)

335 adults, depression, primary care

Randomized to collaborative care intervention in which patient preference guided decision on treatment plan of antidepressant medication, counselling, or both—or usual care

Health outcomes, functional status, depression severity, and measures of disability

Obtaining preferred treatment corresponded with improved treatment outcome.

Loh et al. (2007b)a

405 adults, depression, primary care

SDM training for physicians; decision board

Patient involvement, satisfaction, and depression severity

SDM led to greater patient treatment involvement and satisfaction; no effect on depression severity or consultation time

Ludman et al. (2003)

386 adults, high risk for depression recurrence/relapse, primary care

SDM regarding use of maintenance pharmacotherapy and cognitive-behavioral strategies to promote self-management and reduce depression recurrence/relapse

Self-care practices, self-efficacy for managing depression and depressive symptoms

Intervention patients had significantly greater self-efficacy for managing depression and were more likely to keep track of depressive symptoms, monitor early-warning signs, and plan for coping with high-risk situations

Malm et al. (2003)

84 adults, schizophrenic disorders, community-based programs

Integrated Care program including social network resource groups with SDM elements and communication and problem-solving training; compared to traditional community-based program

Social functioning and patient satisfaction

Patients showed more improvements in social functioning and greater satisfaction in Integrated Care program than in traditional program at a 2-year follow-up.

Simon et al. (2012)

131 adults, depression, insurees of German statutory health insurance fund

Web-based interactive decision aid

Decisional conflict, knowledge, preparation for decision-making, preference for participation, involvement in decision-making, decision regret, and adherence.

The intervention group reported significantly lower decisional conflict than those not receiving decision aid; intervention group also felt more prepared to make decisions; no significant differences in knowledge, adherence, or decision regret.

Stein et al. (2013)

1,122 Medicaid-enrolled adults receiving psychotropic medication

Decision support center and computerized support system used before medication sessions, combining psychoeducation, decision aids, and decision-making exercises

Adherence to psychotropic medication

No effect on adherence rates for psychotropic medications.

van der Krieke et al. (2013)

250 adults, psychosis, Dutch mental health institution

Web-based decision aid to help patients determine their needs and choose from available treatment options

Perceived involvement

No differences in perceived involvement between those receiving decision aid and those not; health care teams reported that tool did not fit in optimally with practice.

von Korff et al. (2003)

386 adults, depression, primary care

12-month relapse prevention program including psycho-education and SDM to plan maintenance pharmacotherapy

Social functioning and disability

Those receiving SDM showed greater improvement in social functioning than controls; no differences on Sheehan Disability Scale

Woltmann, Wilkniss, Teachout, McHugo, & Drake (2011)

80 adults, schizophrenia, bipolar, MDD, substance abuse or dependence; 20 case managers in community mental health

Electronic decision support system (EDSS) to facilitate SDM in community mental health

Case manager and patient satisfaction measures

Case managers more satisfied with care managing process in intervention group; patients in intervention group more likely to recall care plans 3 days after planning session but not more likely to be satisfied

Matching superscript letters following the author name(s) and publication date indicate separate papers reporting results based on the same sample.

Many of these interventions also lead to process benefits, including improvements in the provider–patient relationship, treatment adherence, and engagement. Cunningham and colleagues (2008) argued that, regardless of symptom improvements, SDM may affect the course of treatment by increasing a family’s engagement and compliance with a treatment they helped choose. In a sample of primary care patients with depression, patient participation in treatment planning predicted adherence. In this particular sample, participation did not directly affect clinical outcome, but 60% of the variance in clinical outcome was attributable to patient adherence and depression severity, with adherence being predicted by participation (Loh et al., 2007a). Additionally, Ludman and colleagues (2003) found that the patients participating in SDM showed significantly greater self-efficacy for managing depression and were more likely to keep track of depressive symptoms and monitor early warning signs of depression. Stacey et al. (2014) reported on the potential process benefits of incorporating patient decision aids into treatment planning. One potential mechanism of action of SDM is greater patient satisfaction. It is possible that when patients are more satisfied with the decision process or the decision outcome, they may be more likely to engage in and adhere to the treatment. In a cross-sectional analysis of SDM among patients with depression, Swanson and colleagues (2007) found that SDM was positively associated with patient satisfaction. In addition, primary care depression patients demonstrated greater satisfaction with their care when they received enhanced education and more frequent contact with their clinician as part of a collaborative care intervention (Katon et al., 1999). Thus, there is evidence to suggest that not only do patients fare better when SDM models are implemented, they are also more satisfied with the care they receive.

Components of Shared Decision-Making

To make a truly collaborative decision, a patient must have (1) adequate information, (2) an understanding of treatment-related values, and (3) awareness of their established (or forming) preferences. Based on the model introduced by Rothert and colleagues (1997) and adapted by Wills and Holmes-Rovner (2006), learning information (e.g., illness- and treatment-specific information) is the first step in decision-making. The information is then interpreted through the lens of personal values (e.g., seeking certain types of treatment success, minimizing the likelihood of specific types of negative outcomes). As patients make values-based judgments on the relevant information, they form preferences. In unilateral decision-making, these preferences are the primary drivers. In SDM, established (and ideally, informed) preferences are the patient’s contribution to the decision-making process, to be combined with provider preferences that are also based on the information available to the clinician (e.g., the evidence base, clinical training, and experience) and the clinician’s values (e.g., effective, efficient treatment). All decisions are bounded by the constraints of the treatment context, and that may be evident on the patient level (e.g., limited time to participate in treatment), the clinician level (e.g., limited training to provide a preferred treatment modality), the clinic level (e.g., limited resources to support extended, intensive treatments), and the systems level (e.g., limited insurance reimbursements for selected treatments or disorders). Preferences are also fluid and may shift for the clinician and the patient throughout the course of treatment as a patient’s circumstances and values may change, and new information (e.g., real-time effectiveness of the treatment) becomes available (Elwyn & Miron-Shatz, 2010). This is especially relevant in the treatment of chronic conditions when there will be opportunities to revisit decisions throughout the treatment period (Montori, Gafni, & Charles, 2006), which is frequently the case for mental health care.

Information

Providing information is a simple concept but a complicated task. First, providers must decide what information to provide, as it is impossible to share (or know) every detail about a given disorder or treatment. In addition, the information that providers do present to patients should be as objective as possible, with balanced information on the pros and cons of each treatment option (Abhyankar et al., 2013). The number of informational resources to facilitate making informed medical decisions continues to grow. Nevertheless, personalizing the information provided to individual patients remains a challenge, as does comparing treatments that are difficult to describe in concise terms (e.g., a psychosocial treatment compared to a medication with clear treatment tasks [swallow the pill daily], effectiveness rates, and potential side effects). Individual differences in the amount of information preferred, and the amount of information able to be understood, are likely large. Recent research has also shown that patients and providers may differ in the information they deem important and relevant to decisions (Capirci et al., 2005), which raises the question of whether patient input should start at the information-collection stage to personalize the types of information provided to each patient. Second, providers must present the information in a format that can be understood by the patient. Considerable research has demonstrated the low levels of numeracy across the population (Kirsch, Jungeblut, Jenkins, & Kolstad, 1993), in addition to research showing that the way in which mathematical concepts are presented likely biases the interpretation of findings such as success rates and degree of empirical support. Indeed, even the decision to present data qualitatively (e.g., very likely) versus quantitatively (e.g., 80% likelihood) has a significant impact on patients’ choices (Man-Son-Hing et al., 2002), and the order in which qualitative data are presented affects how patients understand the likelihood of each outcome (Bergenstrom & Sherr, 2003). Patient decision aids, described in greater detail below, represent one valuable approach to providing information, and they are often available for commonly occurring problems. Nevertheless, establishing evidence-based guidelines for providing information to patients is a needed direction in the field. Such guidelines will support clinicians in personalizing the information they give their patients. Of particular importance are evidence-based guidelines that direct clinicians on how to discuss information related to the empirical literature, as well as the information about treatment options that may be less relevant to a treatment’s efficacy but highly relevant to a patient’s treatment preferences (e.g., treatment frequency, duration, cost). In a recent study, patients entering into psychotherapy rated the degree to which treatment effectiveness was valued in relation to “common factors,” such as degree to which the patient sets the agenda for each session. The study findings showed that the value patients place on effectiveness rates relative to other considerations varies widely, with many preferring a therapeutic experience that will be less likely to result in diagnostic remission or a clinically significant symptom change.

Values

Values in treatment planning reflect the importance one assigns to different aspects of treatment (Fagerlin et al., 2013). Values can be focused on the treatment approach (e.g., “I value non-medicinal treatments,” “I value meeting with my therapist regularly,” and “I value family members working together when one member is ill”), and the appeal of different treatment options may vary across patients (Swift, Callahan, & Vollmer, 2011). Values are also often focused on treatment outcomes. “Getting better” may be a common treatment goal, but “better” likely has different meanings for different patients (Greenhalgh & Hurwitz, 1999). This may be especially true in mental health treatment, when “recovery” may mean a reduction of symptoms, improvement of functioning in the presence of symptoms, or some combination of both. In this way, personal values (e.g., being a good parent) contribute to the formulation of treatment goals (e.g., learning to tolerate distress without yelling at children). In the SDM process, it is important for clinicians to assist patients in identifying their values so that they can contribute to the treatment plan in a way that is consistent with their unique perspectives (Elwyn et al., 2006). In this way, eliciting and discussing a patient’s values directly addresses cultural considerations in patient care (Saha, Beach, & Cooper, 2008), focusing not only on what the overarching values are of a given cultural tradition to which a patient may belong but also on the unique ways in which patients interpret and apply their cultural traditions in their own lives (Gutiérrez & Rogoff, 2003).

Preferences

Applying patients’ values to the available information and treatment options will result in patient preferences, i.e., liking one treatment option more than another. Understood as such, patient preferences are constructed, as opposed to being fixed and “revealed” (Lichtenstein & Slovic, 2006). Research on patients’ treatment preferences demonstrates that (1) patients do have preferences (Swift et al., 2011; Bower, King, Nazareth, Lampe, & Sibbald, 2005; Brinkman & Epstein, 2011); (2) preferences can be assessed (Vollmer, Grote, Lange, & Walker, 2009; Berg, Sandahl, & Clinton, 2008; Jaycox et al., 2006); and (3) patient preferences may influence treatment participation and treatment outcomes (Swift et al., 2011; Dwight-Johnson, Unutzer, Sherbourne, Tang, & Wells, 2001). Swift and colleagues (2011), in a recent meta-analysis, found that when patients received their preferred treatment, they were between a half and a third less likely to drop out of therapy prematurely compared with patients who did not receive their preferred therapy approach. They also reported that patients who received their preferred treatment approach did better, on average, than patients who did not receive their preferred approach, with a small but significant effect size. Raue, Schulberg, Heo, Klimstra, and Bruce (2009) randomized adults with major depression to receive either treatment congruent or incongruent with the patients’ preferences, and they found that preference strength was significantly associated with treatment initiation and adherence, though not with depression severity or remission. In child-focused treatment, Banno and McKay (2005) found that when youths receive a treatment their caregivers prefer, session attendance improves. The applicability of preference-focused research to SDM remains unclear, however, as preferences assessed outside of an informative, guided, and collaborative process may differ in critical (yet unknowable) ways. In relation to psychological treatment, preferences may be focused on a number of aspects, including the treatment provider (e.g., preferences for a female clinician, for a clinician with a particular training background), general treatment approach (e.g., skills-based vs. supportive therapy), specific treatment techniques (e.g., mindfulness training vs. cognitive restructuring), and target outcomes (e.g., diagnostic remission vs. improved functioning in a circumscribed domain).

As an example of the interplay between values, information, and preferences, consider a patient, Sally, with Bipolar Disorder. Sally values decreased symptom interference, managing her illness independently, and non-medicinal approaches. In reviewing the evidence base with her therapist, she learns that the first-line treatment for bipolar depression is mood stabilizing and antipsychotic medications and that psychosocial interventions such as interpersonal, family-focused, or cognitive behavioral therapy can be helpful when used in combination with medication (Hirschfeld, 2010). Despite her value of trying non-medicinal approaches, the strong empirical support for pharmacological treatment persuades Sally to schedule a psychiatric appointment, consistent with her value of decreased symptom interference. She also prefers continuing to meet with her therapist for adjunctive psychosocial treatment, choosing individual cognitive-behavioral therapy over family-focused therapy. Cognitive-behavioral therapy and family-focused therapy may have similar levels of empirical support, yet individual cognitive-behavioral therapy fits better with Sally’s value of managing her illness independently.

Implementing Shared Decision-Making in Practice

How SDM is conducted varies by the clinical context. For example, the degree to which decisions are shared, and the process for sharing decisions, will change in urgent versus non-urgent care situations, with patients of different developmental levels and decision-making capacity, and in treatments where there are more versus fewer treatment options. At the heart of SDM, though, is identifying areas of overlap among clinicians, patients, and the evidence base. The evidence base (i.e., the collection of research findings regarding likely outcomes of each treatment) provides the information that enables clinicians and patients to (1) form their preferences by integrating their values with the available information, (2) select a range of acceptable treatment options, and (3) prioritize those options in accordance with patient values. Although clinicians may prefer one treatment over another, there is likely a range of treatments (or treatment configurations) within their skill set that they would be willing to provide. Similarly, patients likely prefer certain treatments over others but remain open to a range of treatment options. Through clinician–patient discussion, consideration of patient values, and awareness of the feasibility of different options, a mutually acceptable decision can be made. When overlap is limited or non-existent, alternatives are considered (e.g., the clinician provides a referral to another provider who offers the preferred treatment modality). The present section reviews the common structure of an SDM encounter and the use of decision aids to facilitate SDM.

Structure of a Shared Decision-Making Encounter

Elwyn and Charles (2009) suggest a three-stage process of how SDM is conducted, using the Charles et al. (1997) model of SDM. They argue that an SDM encounter can be divided into the exchange of information, deliberation about options, and arriving at an agreement on a decision to implement. In accordance with the components of SDM discussed above, the exchange of information is the provision of information regarding the diagnosis and treatment options by the clinician to the patient, and the provision of information regarding patient values and beliefs by the patient to the clinician (often with the assistance of the clinician). As the patient and clinician deliberate about options, they will attempt to apply the discussed values to the available information and form preferences. To arrive at an agreement on a decision to implement, the preferences of the patient and clinician (often termed “recommendations”) are compared and a compromise is sought. Although Elwyn and Charles (2009) argued for this standard sequence of SDM steps, they did not specify the length of each stage or the strategies used to accomplish the goals of each stage.

During the SDM encounter, clinicians should be mindful of potential decisional conflict. Decisional conflict may result from uncertainty regarding the choices available to the patient, especially when the choices involve risk, side effects, or when each choice contains elements that are both desired and undesired (O’Connor, 1995). Although SDM may ease decisional conflict through coaching the patient through the decision process and providing decision aids, it is also possible that providing multiple options to patients may increase decisional conflict. Fortunately, evidence suggests that providing patients with information and coaching about their options does not significantly raise (though nor does it lower) decisional conflict regarding health-related decisions (Stacey et al., 2013). If patients report experiencing heightened distress during SDM, it may be helpful to revisit the role the patient would like to play, and alter the process to accommodate the patient’s needs. For example, information may be presented in smaller amounts, and options may be considered sequentially instead of simultaneously.

Preferences may also shift during the SDM encounter, and throughout the treatment process, especially considering the long-term nature of some mental health treatment episodes to address chronic mental illness (World Health Organization International Consortium in Psychiatric Epidemiology, 2000). As patients learn more about treatment options and reflect more on how each treatment approach fits with their values, their preferences for each approach may change (Elwyn & Miron-Shatz, 2010). It is important to allow for preference changes when discussing the treatment plan, and also as treatment continues and it becomes clearer whether the treatment is working as intended. Indeed, modifying the treatment plan in response to real-time progress monitoring reflects good evidence-based practice, and the SDM process continues throughout treatment as ongoing planning decisions are made collaboratively.

Decision Aids

Patient decision aids, as defined by the International Patient Decision Aids Standards (IPDAS) Collaboration, are “tools designed to help people participate in decision-making about health care options” (International Patient Decision Aids Standards Collaboration, 2014). Whereas some decision aids focus on providing patients with relevant information about treatment options (e.g., what each treatment entails, effectiveness rates), other decision aids guide patients through a decision-making process, and many decision aids do both. Decision aids come in many formats, such as paper handouts, video tutorials, or computer-based interactive aids, and some decision aids are designed to be used prior to meeting with a clinician whereas others are designed to be used in conjunction with the clinician or a member of the clinical support staff. One notable recent decision aid format, Option Grids (Elwyn et al., 2013), provide brief summary tables that compare treatment options directly against each other and may enhance patient participation in the decision-making process. A recent review of decision aids (Stacey et al., 2014) identified 115 studies that tested their effectiveness and found strong evidence that decision aids help patients improve their knowledge and clarify what matters most to them. They also found some evidence that using decision aids helps patients develop a more accurate understanding of disorders and treatments, and leads to patients participating more in decision-making.

Given the wide variety of decision aid formats and development methods, the IPDAS Collaboration has established standards for the development and evaluation of patient decision aids (Volk, Llewellyn-Thomas, Stacey, & Elwyn, 2013b). These standards were initially formulated as a checklist of quality indicators (A. O’Connor et al., 2005) and later progressed to an instrument (the IPDAS Instrument [IPDASi]) that quantitatively assesses the quality of patient decision aids (Elwyn et al., 2009). In the years following the creation of the IPDASi, the IPDAS Collaboration identified a subset of criteria for certifying decision aids as meeting their standards (Joseph-Williams et al., 2013). As illustrated in a series of papers published as a supplement issue of BMC Medical Informatics and Decision Making (Volk, Llewellyn-Thomas, Stacey, & Elwyn, 2013a), building high quality decision aids requires multiple considerations, such as how to: disclose conflicts of interest (Barry et al., 2013), include the information patients need to know to make informed decisions, such as probabilities (Feldman-Stewart et al., 2013), best characterize the evidence base (Montori, LeBlanc, Buchholz, Stilwell, & Tsapas, 2013), balance the presentation of different treatment options (Abhyankar et al., 2013), present quantitative information so that it could be understood by patients with lower levels of education (Trevena et al., 2013), and help patients clarify their values (Fagerlin et al., 2013).

Those looking to use a decision aid in research or practice may choose to design a new decision aid or use an existing one. As indicated above, designing a new decision aid may be daunting, but the scale of the project will vary by the amount of information that needs to be shared through the decision aid, the volume of empirical evidence that needs to be summarized, and the complexity and urgency of the decisions the decision aid will facilitate. Several databases of decision aids already exist. The Ottawa Hospital Research Institute (https://decisionaid.ohri.ca/) maintains a list of decision aids sorted by health topic, as well as generic personal and family decision guides that can be used for any health decision. The Option Grids Web site (http://www.optiongrid.org) focuses on one-page treatment-comparison decision aids for a set of health issues (some of which are found on the other Web sites as well). Many hospitals and government agencies maintain a collection of decision aids as well, although their lists may be less comprehensive.

Future Directions

The study of SDM is expanding rapidly, especially in the field of mental health care, and there are many promising directions to pursue. In this section, we touch on two of the areas we see as most important: the use of SDM for youth-focused treatments and the implementation of SDM in usual care settings.

Shared Decision-Making for Youth-Focused Treatments

Whereas the research on SDM for adult mental health care is slim, that for youth mental health care is virtually non-existent, particularly when considering psychosocial treatment options (Feenstra et al., 2014). This is unfortunate, as SDM is especially relevant to youth with mental illnesses. Treatment planning in youth psychotherapy presents multiple challenges. Youths and caregivers frequently disagree with each other about the presenting problem and treatment goals (Yeh & Weisz, 2001; De Los Reyes & Kazdin, 2005); and youths and caregivers often disagree with their therapists as well (Hawley & Weisz, 2003). In addition, diagnostic profiles may not accurately reflect the most pressing problems (Weisz et al., 2011), and variations in developmental levels require personalization of treatment plans (Weisz & Hawley, 2002; Holmbeck, Devine, & Bruno, 2010). Young people live in a range of family contexts, and working with them and their caregivers raises ethical and legal issues of treatment consent and assent, especially in instances where youths and caregivers disagree (Weithorn & Campbell, 1982; Redding, 1993). Shared decision-making provides a structured approach to assist clinicians in navigating caregiver–youth disagreement and tailoring a treatment plan to accommodate a youth’s developmental status and unique family strengths and needs. Common characteristics of evidence-based approaches for youth psychotherapy (Weisz & Kazdin, 2010)—treatments lasting multiple sessions, requiring out-of-session practice—are also more appropriate for SDM, as they require increased patient commitment to comply with the treatment, similar to other chronic conditions (Montori, Gafni, & Charles, 2006). Lastly, existing research on chronic youth health problems has demonstrated that young people not only want to be involved in treatment decisions (Coyne, 2006; Kelsey, Abelson-Mitchell, & Skirton, 2007) but also have the capacity to do so (Weithorn & Campbell, 1982; Redding, 1993; Alderson, Sutcliffe, & Curtis, 2006). Some recent work has explored the use of SDM with parents alone (e.g., Brinkman et al., 2013; Westermann, Verheij, Winkens, Verhulst, & Van Oort, 2013), and the first author is currently working on an NIMH (National Institute of Mental Health)-funded grant to develop and evaluate an SDM protocol for planning psychosocial treatments with both caregivers and youths.

Shared Decision-Making in Usual Care Settings

Despite the growing societal and empirical support for SDM, the gap between recommended use of SDM and actual use of SDM remains large. For example, in primary care settings, observational studies suggest very low levels of patient involvement in depression care (Loh et al., 2006; Young, Bell, Epstein, Feldman, & Kravitz, 2008), with other studies showing lower levels of SDM in minority families seeking treatment for childhood ADHD (Brinkman et al., 2011). It remains unclear the degree to which SDM is used more broadly across mental health practitioners and primary care practitioners discussing mental health issues. It is well documented that disseminating and implementing new treatment approaches takes time (Proctor et al., 2009), with some studies suggesting a lag of 17 years between the research evaluation of a treatment and its wider implementation in non-research settings (Balas & Boren, 2000). Shared decision-making, though rooted in the now widespread practice of informed consent, has only recently been actively discussed in the context of mental health (e.g., Substance Abuse and Mental Health Services Administration, 2010). In a 2014 Cochrane review, Légaré et al. identified 39 studies reporting on interventions to increase the uptake of SDM by health care professionals, and only two were focused on mental health care providers. This is unfortunate, as there are barriers unique to disseminating and implementing SDM in mental health care. One higher level barrier is the comparatively small amount of research investigating the implementation barriers in mental health. Légaré, Ratté, Gravel, and Graham (2008) when reviewing the literature on barriers and facilitators to implementing SDM, reported that 89% of their combined sample were physicians (n = 3,231). There is a wide variety of practitioners who provide mental health services, necessitating broader study across disciplines (e.g., psychiatry, psychology, social work, nursing), incorporating barriers and facilitators unique to each setting in which mental health issues are identified and treated (e.g., primary care, school, community mental health centers). Mahone and colleagues (2011) conducted clinician focus groups on the topic of SDM in mental health, finding that clinicians cited the history of the medical model, mental health crises, lack of system support, and lack of time as primary barriers to implementing SDM. Patients, too, listed consumer competency, fears, insight, literacy, and trauma from past experiences as obstacles to engaging in SDM. Despite the perception that people seeking treatment for mental health issues may not be able to engage competently in the treatment planning process, this has been shown to be unfounded (Hamann et al., 2006). Lastly, the number of decision aids available for mental health-related decisions is relatively small.

There have, however, been encouraging steps to move SDM more widely into clinical practice. Meetings, such as the one held by SAMHSA (Substance Abuse and Mental Health Services Administration, 2010) have brought together stakeholders and encouraged communication to move SDM more widely into practice settings. There are also several Web-based resources to assist those wishing to incorporate SDM into their practices. Among these are the Ottawa Health Research Institute Implementation Toolkit (https://decisionaid.ohri.ca/implement.html), the Evidence-Based Behavioral Practice training modules (www.ebbp.org), and the Informed Medical Decisions Foundation’s SDM resources (www.informedmedicaldecisions.org). Although there have not yet been studies to test implementation strategies for SDM in mental health, there have been studies testing SDM in non-research settings. For example, Loh and colleagues (2007b) trained primary care physicians in SDM and provided patients with decisions aids for depression. They found that patient participation increased significantly beyond the level observed with the usual care control group, but differences in depression outcomes were not found. The generalizability of current SDM research remains unclear, so it will be especially important in future research efforts to evaluate the ability of clinicians to implement SDM independently with diverse populations, settings, and cultures.

Conclusions

The time is ripe for the dissemination and implementation of SDM throughout mental health care settings. The SDM model is particularly relevant to mental health, is strongly supported by societal values and governmental institutions, and is continually becoming easier to implement as the collection of SDM resources available to clinicians grows. Transitioning SDM from a special skill to a common practice remains one of the most challenging, and exciting, frontiers.

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Notes:

(1) There is ongoing debate about the appropriate term for people receiving treatment, with common options being “consumer,” “patient,” “client,” and “services user.” We have opted to use the term “patient” as our discussions of SDM and the studies we review focus on professional treatment contexts.