PatientsChartingCourse

Benefits of Shared Decision Making and Patient-Centered Care

Often the choice between competing treatment options is not so clear and requires preference-sensitive decisions. In these situations, the treatment choice should take into account an individual’s values and preferences regarding the potential outcomes. Shared decision making is a process that incorporates evidence-based medicine and requires both patients and physicians to contribute information and participate. Providing decision support and participating in patient-centered care requires skills and core competencies. Several evidence-based practices foster communication and shared decision making at the decision point, including involvement of both the patient and the physician, information sharing by both parties, and reaching an agreement about the treatment options (Charles et al., 1997).

Recent trials of decision-support systems designed to help patients under­stand their treatment options reveal that informed patient choice results in different patterns of practice from those found with patients who experience usual care. However, medical opinion rather than patient preference tends to dominate the treatment choice (Wennberg, 2002).

Replacing standard informed consent practices with a patient-centered model of informed choice involves incorporating standardized communication of evidence, including risks, benefits, alternatives, experience, and cost. These changes may lead to differences in healthcare utilization and can empower patients to make informed decisions about their care (Krumholz,­ 2010). Providing patient-centered care depends on the comprehensive training of healthcare providers, increased consumer health literacy, and the successful identification of implementation models (Edwards and Elwyn, 2009).

Use of Information Technology to Feed Forward the Right Patient Information at the Right Time

Dartmouth-Hitchcock Medical Center uses health information technology resources, such as video decision aids and electronic surveys, to collect information from patients about their health history, behaviors, and health preferences in order to provide them with feedback and decision support and facilitate clear communication at the point of care. For example, all patients with breast cancer are electronically screened for psychosocial, financial, and emotional problems (such as anxiety and depression). Using validated surveys such as the Patient Health Questionnaire 9, the computer system can immediately score the survey and identify patients who reach preset clinical thresholds. When a patient reaches that threshold, the computer automatically e-mails Dartmouth-Hitchcock’s social workers to intervene with patients identified as having a need.

In addition, our computer software systems allow us to collect information about patients’ treatment preferences to share with their physician. When patients come in for their appointments, their clinicians are armed with information focused on what is meaningful to the patients. This facilitates discussion between clinician and patient and allows for the creation of a personalized and tailored treatment plan.

Impact of Decision Aids on Patients’ Choices

A number of RCTs have shown the benefits of decision aids in supporting specific decisions. However, few trials have shown the impact on treatment choice. At Dartmouth-Hitchcock, the use of decision aids has resulted in approximately 30 percent of patients changing their initial treatment preference. In an RCT, Deyo and colleagues studied the impact of a video decision aid designed to inform patients about treatment options for back pain. The overall rate of surgery was 22 percent lower in the group viewing the video than in controls (26 percent vs. 33 percent). The researchers concluded that the video decision aid appeared to facilitate decision making and could help ensure informed consent (Deyo et al., 2000).

Summary

Patients and providers should each have the benefit of making decisions with the best available evidence. When treatments are not supported by adequate evidence or when they involve trade-offs that can variably impact a patient’s quality of life, the decision-making process should be structured in a way that supports informed patient choice, by incorporating a discussion of treatment alternatives, the best evidence available, and the patient’s personal values.

At Dartmouth-Hitchcock Medical Center, a patient’s self-reported health information is collected by computer systems. This information is synthesized, and reports are created instantaneously to feed information back to the patient about his/her health behaviors and conditions. This same software can support the provision of sophisticated decision aids when patients are facing preference-sensitive treatment choices. Additionally, information on patients’ treatment preferences—including their understanding of the key facts about the treatment and how they would value the different possible outcomes of care—can be collected and fed forward to their treating clinician at the point of care. This integration of technology, patient information, and evidence provides a framework for patient-centered care and informed choice.

EVIDENCE STANDARDS AND APPLICATION:

RIGHT CARE, RIGHT PATIENT, RIGHT TIME

Clifford Goodman, Ph.D.

The Lewin Group

Standards of evidence for what works in health care are generally appreciated as instrumental to the widely shared goal of getting the right care to the right patient at the right time. Such standards are used to support clinical practice guidelines and other best practices, formulary decisions, coverage policies, and more. These evidence standards are oriented largely toward the tenets of experimental methods and often presented in frameworks or hierarchies of study design. These hierarchies have not been static. They have evolved over the past few decades to better address the particular circumstances of healthcare decisions. However, persistent shortcomings in the practical utility of current approaches for appraising evidence, as well as certain mismatches between evidence hierarchies and the questions they are intended to help answer, suggest that these approaches and hierarchies need to be revisited and reconsidered in a broader context ranging from identification of research priorities bringing research to practice.

Evidence Hierarchies and Their Application

Evidence standards can inform and improve healthcare decisions and policies when they are used to appraise the quality of available evidence on the impacts of healthcare interventions. Although evidence hierarchies have evolved over the last two decades, they are oriented largely toward the relative strength of evidence regarding the causal effect of an intervention on particular health outcomes or other endpoints. In these hierarchies, internal validity is associated with study design, and the resulting evidence hierarchy of primary studies—i.e., those that collect original data—­typically places RCTs at the top as the “gold standard” for primary evidence. RCTs are followed, typically, by nonrandomized controlled trials, various prospective and retrospective observational (nonexperimental) studies with concurrent or historical control groups, case series, single cases, and finally (though not a form of evidence per se) expert opinion. In some hierarchies, systematic reviews and meta-analyses of RCTs, where available, are placed above RCTs (Sackett, 1989; USPSTF, 2008).

Notable variations or adaptations of this basic framework recognize that implementation of a study design, not just the nature of the study design itself, affects the quality of the evidence yielded. Accordingly, these variations account for whether a study was “well designed” by employing an acceptable means of randomization, by eliminating or minimizing other

potential biases or threats to internal validity, and by employing adequate statistical power to detect potential treatment effects; what kind of comparison is involved (e.g., “head-to-head” trials of an intervention vs. a standard of care rather than indirect comparison of the two or comparison with placebo); whether the emphasis is on clinically relevant, rather than surrogate, outcomes; and other attributes (Atkins et al., 2005; Harbour and Miller, 2001; McAlister et al., 1999; USPSTF, 2008).

Limitations of Evidence Hierarchies

Despite improvements over the years, characteristics inherent in evidence hierarchies can limit the development and appraisal of evidence necessary for informing decisions about the right care for the right patient at the right time. That there are more than 60 published evidence hierarchies signals a lack of satisfaction or consensus in the field. Apart from inconsistencies across hierarchies in the definitions and categorization of study designs (e.g., multiple and overlapping definitions of “cohort” and “quasiexperimental” studies) and in rankings of study designs (e.g., whether RCTs or systematic reviews reside at the top), most existing hierarchies are limited by the mismatch between their original use and current application and the associated overreliance on the RCT.

Evidence hierarchies are based largely on methodological principles for assessing pharmacological models of therapy, including emphasizing experimental control; placebo control groups where possible; randomized assignment; narrowly defined patient groups; blinding of patients, clinicians, and investigators; and other attributes intended to enhance internal validity. However, this approach can jeopardize external validity—that is, the generalizability of findings to patients, settings, and other circumstances of real-world care. For example, RCTs may provide clear results when assessing the effect of a drug compared with a placebo in a narrowly defined patient population with a specific health problem. However, such RCTs may not provide information relevant for those making choices among alternative therapies used in practice or for those with multiple comorbidities along with the specific health problem. Further, while RCTs and other experimental study designs are intended to address the internal validity of the causal effect of an intervention on outcomes, they have been misapplied to other types of research questions pertaining to healthcare technologies, such as the accuracy of a screening or diagnostic test, the prognostic accuracy of a test, or rare or delayed adverse effects of a therapy.

As is recognized by some evidence hierarchies, relying on study type alone for assessing the quality of evidence can be misleading. For example, poor design or implementation of a high-ranking study type may yield findings that are less valid than those from study types lower on the hier-

archy that are well designed and rigorously implemented. Further, evidence hierarchies often cannot adequately accommodate or combine results from multiple study design types, even when no single study design can answer some evidence questions.

Limitations of Randomized Controlled Trials

RCTs are usually important, and sometimes essential, components of a rigorous evidence base for demonstrating the causal effects of healthcare interventions on outcomes. However, RCTs have important limitations, including some circumstances in which underlying assumptions about them are not valid (Rawlins, 2008; Walach et al., 2006). For example, while undertaking to randomize patients to one intervention or another assumes equipoise between the two, patients and clinicians involved in RCTs often bring preferences for one or the other. Whereas RCTs assume a lack of knowledge about the merits of the interventions in questions, there may indeed be relevant evidence from other sources (e.g., phase II drug trials) that undermines the utility of the null hypothesis in experimentation. RCTs also have a preference for specificity—i.e., that only the specific effects attributable­ to an intervention are therapeutically valid. Another assumption underlying RCTs that often is not valid relates to the context independence of the effect—i.e., that there is some “true” magnitude of efficacy or a stable effect size independent of the context in which an intervention is used.

Another major weakness concerns incorrectly assuming that the findings about a therapeutic effect from an RCT are externally valid—that is, readily transferable into clinical practice—if the exclusion and inclusion criteria of the trial match the characteristics of a given patient. Although this shortcoming of RCTs is generally well recognized, its extent and significance for patient care are not well understood.

Other methodological problems with RCTs can include relying on intermediate endpoints rather than health outcomes; having inadequate statistical power or duration for assessing benefits and harms, especially those that are rare or delayed; and being unsuccessfully blinded to patients, clinicians, or other investigators. Probability theories may pose problems (especially with frequentist approaches) in the form of arbitrary selection of p-values and the difficulty of applying them to everyday patient care; the multiplicity of endpoints compared, stopping rules, and analysis of subgroups; and resistance to Bayesian approaches.

Furthermore, there are various circumstances in which RCTs can be inappropriate. For example, they may raise bioethical and legal concerns; they may be difficult or impossible to conduct for rare diseases; and they may be unnecessary for very large treatment effects or to establish causa-

tion (e.g., Heimlich maneuver, cardiac defibrillation, laser removal of port wine stains). Additionally, the substantial resources needed to conduct RCTs—large, multicenter, longitudinal trials, sometimes costing hundreds of millions of dollars—limit their broad application to the myriad clinical questions important to providing patient-centered care.

Finally, the RCT’s status as the “gold standard” for establishing the causal effect of an intervention on a health outcome can be extended ­inappropriately to other purposes. Indeed, other study designs are more suitable for answering certain evidence questions that inform many other aspects of clinical care, as summarized in Box 7-1.

Improving Evidence Hierarchies

Approaches to appraising the quality of evidence must extend beyond single hierarchies of study designs. They must consider the strengths and weaknesses of different study designs for answering specific questions, including the use of certain traditionally lower-ranked observational methods­ such as examination of large claims databases, patient registries, and electronic health records to supply evidence augmenting that derived from other designs.

BOX 7-1

RCTs Are Not Always the Best Method for

Answering Clinical Evidence Questions

For the following situation, other experimental designs may include:

•Prognosis: patient cohort studies with follow-up at uniform points in the clinical course of a disease/disorder

•Identification of risk factors for diseases, disorders, adverse events: case control studies

•Accuracy of a diagnostic test: cohort (or cross-sectional) study of index test vs. gold standard in patients at risk of having a disease/disorder

•Effectiveness of interventions for otherwise fatal conditions: nonrandomized trials, case series

•Rates of recall or procedures precipitated by false-positive screening results: cohort studies

•Complication rates from surgery or other procedures: case series

•Incidence of rare, serious adverse events potentially due to an intervention: surveillance, registries

•Safety, effectiveness of incrementally modified technologies posing no known additional risk: registries

Different Research Questions Call for Different Study Designs

As described above, evidence requirements should address the type of intervention, application, and other attributes of evidence questions. Beyond evidence hierarchies devoted to treatments, hierarchies have been developed for assessing the quality of evidence pertaining to technologies used for screening, diagnosis, and other purposes. For example, the Strength of Recommendation Taxonomy (SORT) (Table 7-2) distinguishes different levels of evidence quality for technologies used in diagnosis, treatment/prevention/­ screening, and prognosis based on study design and other methodological aspects. In particular, SORT considers the availability of evidence on

TABLE 7-2  Strength of Recommendation Taxonomy (SORT), an Evidence Hierarchy That Includes Explicit Consideration of Intervention Type and Quality of Patient-Oriented Outcomes Assessed

Level 3— Consensus guidelines, extrapolations from bench research, usual practice, other evidence opinion, disease-oriented evidence (intermediate or physiologic outcomes

only), or case series for studies of diagnosis, treatment, prevention, or screening

SOURCE: Reprinted with permission from “Strength of Recommendation Taxonomy (SORT) Patient-Centered Approach to Grading Evidence in the Medical Literature,” February 1, 2004, American Family Physician. Copyright © 2004 American Academy of Family Physicians. All Rights Reserved.

patient-oriented outcomes (morbidity, mortality, symptom improvement, cost reduction, and patient quality of life) rather than disease-oriented evidence (biomarkers and other intermediate endpoints) (Ebell et al., 2004).

Another evidence-rating approach has been developed by the Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative to support a coordinated, systematic process for evaluating genomic tests. In this instance, EGAPP developed hierarchies suitable for appraising evidence for each of three main attributes of testing: analytic validity, clinical validity, and clinical utility (Table 7-3). For analytic validity—the ability to identify correctly a gene of interest—Level 1 evidence would consist of a collaborative study using a large panel of well-characterized test samples, or summary data from well-designed external proficiency testing schemes of interlaboratory comparison programs. For appraising evidence for clinical validity—whether the gene is associated with a given disease or other phenotype of interest—Level 1 evidence would consist of well-designed longitudinal cohort studies or a validated clinical decision rule. For clinical utility—which addresses whether a test result affects clinical decision making or patient outcomes—the evidence hierarchy resembles the more traditional ones, in which Level 1 evidence would be a meta-analysis of RCTs that followed patients from testing through clinical decisions to outcomes. Level 2 evidence would be a single RCT, and so on (Teutsch et al., 2009).

Indeed, mapping evidence requirements to clinical analytical frameworks, as is done by EGAPP, the U.S. Preventive Services Task Force (USPSTF),­ and the Agency for Healthcare Research and Quality’s Evidencebased Practice Centers, illustrates how evidentiary needs can differ based on the decision flow. For example, Figure 7-2 illustrates the evidence framework for determining whether testing for CYP450 polymorphisms in adults entering therapy with selective serotonin reuptake inhibitors is useful in treatment decisions or leads to improved outcomes. Proponents of this test, which uses microarray technology to determine the genotype of a patient’s cytochrome P450 enzymes, recommend its use for guiding the selection of effective medicines. The test’s first uses have been in psychiatry (Teutsch et al., 2009). In Figure 7-2, the numbers correspond to analytical validity (2), clinical validity (3a-c), and clinical utility (4a-c), each of which could be determined using the types of evidence listed in the EGAPP hierarchy shown in Table 7-3.

The Best Scientific Evidence May Derive from a Complementary Set of Methods

The methods in the available toolkit for assessing clinical effectiveness have their respective strengths and weaknesses. Using multiple and complementary methods (Figure 7-3) can offset vulnerabilities and triangulate

TABLE 7-3  Hierarchies of Data Sources and Study Designs for the Components of Evaluation

SOURCE: Reprinted with permission from Teutsch et al., 2009.

findings—starting with results achieved with one method and replicating or augmenting them with other methods. This may constitute a powerful and comprehensive approach for developing a broader body of evidence that is helpful to guide care (Rawlins, 2008; Walach et al., 2006). For example, results of RCTs used to obtain Food and Drug Administration approval could be combined with analyses of patient registry data and longer-term follow-up of outcomes and adverse events through a cohort study to help