Shared decision making for patients with type 2 diabetes: a randomized trial in primary care.

BACKGROUND: Patient-centered diabetes care requires shared decision making (SDM). Decision aids promote SDM, but their efficacy in nonacademic and rural primary care clinics is unclear. METHODS: We cluster-randomized 10 practices in a concealed fashion to implement either a decision aid (DA) about starting statins or one about choosing antihyperglycemic agents. Each practice served as a control group for another practice implementing the other type of DA. From April 2011 to July 2012, 103 (DA=53) patients with type 2 diabetes participated in the trial. We used patient and clinician surveys administered after the clinical encounter to collect decisional outcomes (patient knowledge and comfort with decision making, patient and clinician satisfaction). Medical records provided data on metabolic control. Pharmacy fill profiles provided data for estimating adherence to therapy. RESULTS: Compared to usual care, patients receiving the DA were more likely to report discussing medications (77% vs. 45%, p<.001), were more likely to answer knowledge questions correctly (risk reduction with statins 61% vs. 33%, p=.07; knowledge about options 57% vs. 33%, p=.002) and were more engaged by their clinicians in decision making (50. vs. 28, difference 21.4 (95% CI 6.4, 36.3), p=.01). We found no significant impact on patient satisfaction, medication starts, adherence or clinical outcomes, in part due to limited statistical power. CONCLUSION: DAs improved decisional outcomes without significant effect on clinical outcomes. DAs designed for point-of-care use with type 2 diabetes patients promoted shared decision making in nonacademic and rural primary care practices. TRIAL REGISTRATION: NCT01029288.

The impact of decision aids to enhance shared decision making for diabetes (the DAD study): protocol of a cluster randomized trial.

BACKGROUND: Shared decision making contributes to high quality healthcare by promoting a patient-centered approach. Patient involvement in selecting the components of a diabetes medication program that best match the patient's values and preferences may also enhance medication adherence and improve outcomes. Decision aids are tools designed to involve patients in shared decision making, but their adoption in practice has been limited. In this study, we propose to obtain a preliminary estimate of the impact of patient decision aids vs. usual care on measures of patient involvement in decision making, diabetes care processes, medication adherence, glycemic and cardiovascular risk factor control, and resource utilization. In addition, we propose to identify, describe, and explain factors that promote or inhibit the routine embedding of decision aids in practice. METHODS/DESIGN: We will be conducting a mixed-methods study comprised of a cluster-randomized, practical, multicentered trial enrolling clinicians and their patients (n = 240) with type 2 diabetes from rural and suburban primary care practices (n = 8), with an embedded qualitative study to examine factors that influence the incorporation of decision aids into routine practice. The intervention will consist of the use of a decision aid (Statin Choice and Aspirin Choice, or Diabetes Medication Choice) during the clinical encounter. The qualitative study will include analysis of video recordings of clinical encounters and in-depth, semi-structured interviews with participating patients, clinicians, and clinic support staff, in both trial arms. DISCUSSION: Upon completion of this trial, we will have new knowledge about the effectiveness of diabetes decision aids in these practices. We will also better understand the factors that promote or inhibit the successful implementation and normalization of medication choice decision aids in the care of chronic patients in primary care practices. TRIAL REGISTRATION: NCT00388050.

Making sense of diabetes medication decisions: a mixed methods cluster randomized trial using a conversation aid intervention.

PURPOSE: To determine the effectiveness of a shared decision-making (SDM) tool versus guideline-informed usual care in translating evidence into primary care, and to explore how use of the tool changed patient perspectives about diabetes medication decision making. METHODS: In this mixed methods multicenter cluster randomized trial, we included patients with type 2 diabetes mellitus and their primary care clinicians. We compared usual care with or without a within-encounter SDM conversation aid. We assessed participant-reported decisions made and quality of SDM (knowledge, satisfaction, and decisional conflict), clinical outcomes, adherence, and observer-based patient involvement in decision-making (OPTION12-scale). We used semi-structured interviews with patients to understand their perspectives. RESULTS: We enrolled 350 patients and 99 clinicians from 20 practices and interviewed 26 patients. Use of the conversation aid increased post-encounter patient knowledge (correct answers, 52% vs. 45%, p = 0.02) and clinician involvement of patients (Mean between-arm difference in OPTION12, 7.3 (95% CI 3, 12); p = 0.003). There were no between-arm differences in treatment choice, patient or clinician satisfaction, encounter length, medication adherence, or glycemic control. Qualitative analyses highlighted differences in how clinicians involved patients in decision making, with intervention patients noting how clinicians guided them through conversations using factors important to them. CONCLUSIONS: Using an SDM conversation aid improved patient knowledge and involvement in SDM without impacting treatment choice, encounter length, medication adherence or improved diabetes control in patients with type 2 diabetes. Future interventions may need to focus specifically on patients with signs of poor treatment fit. CLINICAL TRIAL REGISTRATION: ClinicalTrial.gov: NCT01502891.

Deploying Clinical Decision Support for Familial Hypercholesterolemia.

Objective: Familial hypercholesterolemia (FH), a prevalent genomic disorder that increases risk of coronary heart disease, remains significantly underdiagnosed. Clinical decision support (CDS) tools have the potential to increase FH detection. We describe our experience in the development and implementation of a genomic CDS for FH at a large academic medical center. Methods: CDS development and implementation were conducted in four phases: (1) development and validation of an algorithm to identify "possible FH"; (2) obtaining approvals from institutional committees to develop the CDS; (3) development of the initial prototype; and (4) use of an implementation science framework to evaluate the CDS. Results: The timeline for this work was approximately 4 years; algorithm development and validation occurred from August 2018 to February 2020. During this 4-year period, we engaged with 15 stakeholder groups to build and integrate the CDS, including health care providers who gave feedback at each stage of development. During CDS implementation six main challenges were identified: (1) need for multiple institutional committee approvals; (2) need to align the CDS with institutional knowledge resources; (3) need to adapt the CDS to differing workflows; (4) lack of institutional guidelines for CDS implementation; (5) transition to a new institutional electronic health record (EHR) system; and (6) limitations of the EHR related to genomic medicine. Conclusion: We identified multiple challenges in different domains while developing CDS for FH and integrating it with the EHR. The lessons learned herein may be helpful in streamlining the development and deployment of CDS to facilitate genomic medicine implementation.

An Implementation Science Framework to Develop a Clinical Decision Support Tool for Familial Hypercholesterolemia.

Electronic health record (EHR)-based clinical decision support (CDS) can address the low awareness and undertreatment of familial hypercholesterolemia (FH), a disorder associated with a markedly increased risk of coronary heart disease. We aimed to incorporate provider perspectives into the development and implementation of a CDS tool for FH. An implementation science framework and a user-centered design process were used to create a CDS tool for FH. Primary care physicians and specialist physicians participated in qualitative interviews, usability testing and an implementation survey. The CDS was configured in two formats-a best practice alert (BPA) and an in-basket message and subsequently deployed in the EHR in silent mode. The key themes that emerged from the analysis of interview transcripts included understanding and awareness of FH, clinical workflow, physician preferences and value of CDS tools, perspectives on patient needs and values and dissemination and implementation. Recommendations related to usability included preferred CDS format and placement, content, timing and frequency, and level of alert urgency/prioritization. In response to the survey, 84.6% of physicians agreed that the CDS would improve early FH diagnosis and 92.3% agreed that it would help them identify and manage FH patients. Physician feedback led to iterative CDS refinement. In summary, we developed a CDS tool for FH using an implementation science framework and physician feedback. Initial deployment revealed a significant burden of FH and the potential for the CDS tool to have a large impact.

Computerized Advisory Decision Support for Cardiovascular Diseases in Primary Care: A Cluster Randomized Trial.

PURPOSE: The purpose of this research was to evaluate the impact of an outpatient computerized advisory clinical decision support system (CDSS) on adherence to guideline-recommended treatment for heart failure, atrial fibrillation, and hyperlipidemia. METHODS: Twenty care teams (109 clinicians) in a primary care practice were cluster-randomized to either access or no access to an advisory CDSS integrated into the electronic medical record. For patients with an outpatient visit, the CDSS determined if they had heart failure with reduced ejection fraction, hyperlipidemia, or atrial fibrillation; and if so, was the patient receiving guideline-recommended treatment. In the intervention group, an alert was visible in the medical record if there was a discrepancy between current and guideline-recommended treatment. Clicking the alert displayed the treatment discrepancy and recommended treatment. Outcomes included prescribing patterns, self-reported use of decision aids, and self-reported efficiency. The trial was conducted between May 1 and November 15, 2016, and incorporated 16,310 patient visits. RESULTS: The advisory CDSS increased adherence to guideline-recommended treatment for heart failure (odds ratio [OR] 7.6, 95% confidence interval [CI], 1.2, 47.5) but had no impact in atrial fibrillation (OR 0.94, 95% CI 0.15, 5.94) or hyperlipidemia (OR 1.1, 95% CI 0.6, 1.8). Clinicians with access to the CDSS self-reported greater use of risk assessment tools for heart failure (3.6 [1.1] vs 2.7 [1.0], mean [standard deviation] on a 5-point scale) but not for atrial fibrillation or hyperlipidemia. The CDSS did not impact self-assessed efficiency. The overall usage of the CDSS was low (19%). CONCLUSIONS: A computerized advisory CDSS improved adherence to guideline-recommended treatment for heart failure but not for atrial fibrillation or hyperlipidemia.

Associations Among Practice Variation, Clinician Characteristics, and Care Algorithm Usage: A Multispecialty Vignette Study.

The objective was to quantitatively evaluate clinician characteristics associated with unwarranted practice variation, and how clinical care algorithms influence this variation. Participants (142 physicians, 53 nurse practitioners, and 9 physician assistants in family medicine, internal medicine, and cardiology) described their management of 4 clinical vignettes, first based on their own practice (unguided), then using care algorithms (guided). The authors quantitatively estimated variation in management. Cardiologists demonstrated 17% lower variation in unguided responses than generalists (fold-change 0.83 [95% confidence interval (CI) 0.68, 0.97]), and those who agreed that practice variation can realistically be reduced had 16% lower variation than those who did not (fold-change 0.84 [CI, 0.71, 0.99]). A 17% reduction in variation was observed for guided responses compared with baseline (unguided) responses (fold-change 0.83 [CI, 0.76, 0.90]). Differences were otherwise similar across clinician subgroups and attitudes. Unwarranted practice variation was similar across most clinician subgroups. The authors conclude that care algorithms can reduce variation in management.

Practice variation and practice guidelines: Attitudes of generalist and specialist physicians, nurse practitioners, and physician assistants.

OBJECTIVE: To understand clinicians' beliefs about practice variation and how variation might be reduced. METHODS: We surveyed board-certified physicians (N = 178), nurse practitioners (N = 60), and physician assistants (N = 12) at an academic medical center and two community clinics, representing family medicine, general internal medicine, and cardiology, from February-April 2016. The Internet-based questionnaire ascertained clinicians' beliefs regarding practice variation, clinical practice guidelines, and costs. RESULTS: Respondents agreed that practice variation should be reduced (mean [SD] 4.5 [1.1]; 1 = strongly disagree, 6 = strongly agree), but agreed less strongly (4.1 [1.0]) that it can realistically be reduced. They moderately agreed that variation is justified by situational differences (3.9 [1.2]). They strongly agreed (5.2 [0.8]) that clinicians should help reduce healthcare costs, but agreed less strongly (4.4 [1.1]) that reducing practice variation would reduce costs. Nearly all respondents (234/249 [94%]) currently depend on practice guidelines. Clinicians rated differences in clinician style and experience as most influencing practice variation, and inaccessibility of guidelines as least influential. Time to apply standards, and patient decision aids, were rated most likely to help standardize practice. Nurse practitioners and physicians assistants (vs physicians) and less experienced (vs senior) clinicians rated more favorably several factors that might help to standardize practice. Differences by specialty and academic vs community practice were small. CONCLUSIONS: Clinicians believe that practice variation should be reduced, but are less certain that this can be achieved. Accessibility of guidelines is not a significant barrier to practice standardization, whereas more time to apply standards is viewed as potentially helpful.

Electronic Knowledge Resources and Point-of-Care Learning: A Scoping Review.

PURPOSE: The authors sought to summarize quantitative and qualitative research addressing electronic knowledge resources and point-of-care learning in a scoping review. METHOD: The authors searched MEDLINE, Embase, PsycINFO, and the Cochrane Database for studies addressing electronic knowledge resources and point-of-care learning. They iteratively revised inclusion criteria and operational definitions of study features and research themes of interest. Two reviewers independently performed each phase of study selection and data extraction. RESULTS: Of 10,811 studies identified, 305 were included and reviewed. Most studies (225; 74%) included physicians or medical students. The most frequently mentioned electronic resources were UpToDate (88; 29%), Micromedex (59; 19%), Epocrates (50; 16%), WebMD (46; 15%), MD Consult (32; 10%), and LexiComp (31; 10%). Eight studies (3%) evaluated electronic resources or point-of-care learning using outcomes of patient effects, and 36 studies (12%) reported objectively measured clinician behaviors. Twenty-five studies (8%) examined the clinical or educational impact of electronic knowledge resource use on patient care or clinician knowledge, 124 (41%) compared use rates of various knowledge resources, 69 (23%) examined the quality of knowledge resource content, and 115 (38%) explored the process of point-of-care learning. Two conceptual clarifications were identified, distinguishing the impact on clinical or educational outcomes versus the impact on test setting decision support, and the quality of information content versus the correctness of information obtained by a clinician-user. CONCLUSIONS: Research on electronic knowledge resources is dominated by studies involving physicians and evaluating use rates. Studies involving nonphysician users, and evaluating resource impact and implementation, are needed.

Information needs of generalists and specialists using online best-practice algorithms to answer clinical questions.

OBJECTIVE: To better understand clinician information needs and learning opportunities by exploring the use of best-practice algorithms across different training levels and specialties. METHODS: We developed interactive online algorithms (care process models [CPMs]) that integrate current guidelines, recent evidence, and local expertise to represent cross-disciplinary best practices for managing clinical problems. We reviewed CPM usage logs from January 2014 to June 2015 and compared usage across specialty and provider type. RESULTS: During the study period, 4009 clinicians (2014 physicians in practice, 1117 resident physicians, and 878 nurse practitioners/physician assistants [NP/PAs]) viewed 140 CPMs a total of 81 764 times. Usage varied from 1 to 809 views per person, and from 9 to 4615 views per CPM. Residents and NP/PAs viewed CPMs more often than practicing physicians. Among 2742 users with known specialties, generalists ( N = 1397) used CPMs more often (mean 31.8, median 7 views) than specialists ( N = 1345; mean 6.8, median 2; P < .0001). The topics used by specialists largely aligned with topics within their specialties. The top 20% of available CPMs (28/140) collectively accounted for 61% of uses. In all, 2106 clinicians (52%) returned to the same CPM more than once (average 7.8 views per topic; median 4, maximum 195). Generalists revisited topics more often than specialists (mean 8.8 vs 5.1 views per topic; P < .0001). CONCLUSIONS: CPM usage varied widely across topics, specialties, and individual clinicians. Frequently viewed and recurrently viewed topics might warrant special attention. Specialists usually view topics within their specialty and may have unique information needs.

Impact of electronic clinical decision support on adherence to guideline-recommended treatment for hyperlipidaemia, atrial fibrillation and heart failure: protocol for a cluster randomised trial.

INTRODUCTION: Clinical practice guidelines facilitate optimal clinical practice. Point of care access, interpretation and application of such guidelines, however, is inconsistent. Informatics-based tools may help clinicians apply guidelines more consistently. We have developed a novel clinical decision support tool that presents guideline-relevant information and actionable items to clinicians at the point of care. We aim to test whether this tool improves the management of hyperlipidaemia, atrial fibrillation and heart failure by primary care clinicians. METHODS/ANALYSIS: Clinician care teams were cluster randomised to receive access to the clinical decision support tool or passive access to institutional guidelines on 16 May 2016. The trial began on 1 June 2016 when access to the tool was granted to the intervention clinicians. The trial will be run for 6 months to ensure a sufficient number of patient encounters to achieve 80% power to detect a twofold increase in the primary outcome at the 0.05 level of significance. The primary outcome measure will be the percentage of guideline-based recommendations acted on by clinicians for hyperlipidaemia, atrial fibrillation and heart failure. We hypothesise care teams with access to the clinical decision support tool will act on recommendations at a higher rate than care teams in the standard of care arm. ETHICS AND DISSEMINATION: The Mayo Clinic Institutional Review Board approved all study procedures. Informed consent was obtained from clinicians. A waiver of informed consent and of Health Insurance Portability and Accountability Act (HIPAA) authorisation for patients managed by clinicians in the study was granted. In addition to publication, results will be disseminated via meetings and newsletters. TRIAL REGISTRATION NUMBER: NCT02742545.

Encounter Decision Aid vs. Clinical Decision Support or Usual Care to Support Patient-Centered Treatment Decisions in Osteoporosis: The Osteoporosis Choice Randomized Trial II.

PURPOSE: Osteoporosis Choice, an encounter decision aid, can engage patients and clinicians in shared decision making about osteoporosis treatment. Its effectiveness compared to the routine provision to clinicians of the patient's estimated risk of fracture using the FRAX calculator is unknown. METHODS: Patient-level, randomized, three-arm trial enrolling women over 50 with osteopenia or osteoporosis eligible for treatment with bisphosphonates, where the use of Osteoporosis Choice was compared to FRAX only and to usual care to determine impact on patient knowledge, decisional conflict, involvement in the decision-making process, decision to start and adherence to bisphosphonates. RESULTS: We enrolled 79 women in the three arms. Because FRAX estimation alone and usual care produced similar results, we grouped them for analysis. Compared to these, use of Osteoporosis Choice increased patient knowledge (median score 6 vs. 4, p = .01), improved understanding of fracture risk and risk reduction with bisphosphonates (p = .01 and p<.0001, respectively), had no effect on decision conflict, and increased patient engagement in the decision making process (OPTION scores 57% vs. 43%, p = .001). Encounters with the decision aid were 0.8 minutes longer (range: 33 minutes shorter to 3.0 minutes longer). There were twice as many patients receiving and filling prescriptions in the decision aid arm (83% vs. 40%, p = .07); medication adherence at 6 months was no different across arms. CONCLUSION: Supporting both patients and clinicians during the clinical encounter with the Osteoporosis Choice decision aid efficiently improves treatment decision making when compared to usual care with or without clinical decision support with FRAX results. TRIAL REGISTRATION: clinical trials.gov NCT00949611.

Peering into the black box: a meta-analysis of how clinicians use decision aids during clinical encounters.

OBJECTIVE: To quantify the extent to which clinicians use clinically-efficacious decision aids as intended during implementation in practice and how fidelity to usage instructions correlates with shared decision making (SDM) outcomes. METHODS: Participant-level meta-analysis including six practice-based randomized controlled trials of SDM in various clinical settings encompassing a range of decisions. RESULTS: Of 339 encounters in the SDM intervention arm of the trials, 229 were video recorded and available for analysis. The mean proportion of fidelity items observed in each encounter was 58.4% (SD = 23.2). The proportion of fidelity items observed was significantly associated with patient knowledge (p = 0.01) and clinician involvement of the patient in decision making (p <0.0001), while no association was found with patient decisional conflict or satisfaction with the encounter. CONCLUSION: Clinicians' fidelity to usage instructions of point-of-care decision aids in randomized trials was suboptimal during their initial implementation in practice, which may have underestimated the potential efficacy of decision aids when used as intended.

Effectiveness of the Chest Pain Choice decision aid in emergency department patients with low-risk chest pain: study protocol for a multicenter randomized trial.

BACKGROUND: Chest pain is the second most common reason patients visit emergency departments (EDs) and often results in very low-risk patients being admitted for prolonged observation and advanced cardiac testing. Shared decision-making, including educating patients regarding their 45-day risk for acute coronary syndrome (ACS) and management options, might safely decrease healthcare utilization. METHODS/DESIGN: This is a protocol for a multicenter practical patient-level randomized trial to compare an intervention group receiving a decision aid, Chest Pain Choice (CPC), to a control group receiving usual care. Adults presenting to five geographically and ethnically diverse EDs who are being considered for admission for observation and advanced cardiac testing will be eligible for enrollment. We will measure the effect of CPC on (1) patient knowledge regarding their 45-day risk for ACS and the available management options (primary outcome); (2) patient engagement in the decision-making process; (3) the degree of conflict patients experience related to feeling uninformed (decisional conflict); (4) patient and clinician satisfaction with the decision made; (5) the rate of major adverse cardiac events at 30 days; (6) the proportion of patients admitted for advanced cardiac testing; and (7) healthcare utilization. To assess these outcomes, we will administer patient and clinician surveys immediately after each clinical encounter, obtain video recordings of the patient-clinician discussion, administer a patient healthcare utilization diary, analyze hospital billing records, review the electronic medical record, and conduct telephone follow-up. DISCUSSION: This multicenter trial will robustly assess the effectiveness of a decision aid on patient-centered outcomes, safety, and healthcare utilization in low-risk chest pain patients from a variety of geographically and ethnically diverse EDs. TRIAL REGISTRATION: NCT01969240.

Translating comparative effectiveness of depression medications into practice by comparing the depression medication choice decision aid to usual care: study protocol for a randomized controlled trial.

BACKGROUND: Comparative effectiveness research (CER) documents important differences in antidepressants in terms of efficacy, safety, cost, and burden to the patient. Decision aids can adapt this evidence to help patients participate in making informed choices. In turn, antidepressant therapy will more likely reflect patients' values and context, leading to improved adherence and mood outcomes. METHODS/DESIGN: The objective of this study is to develop the Depression Medication Choice decision aid for use during primary care encounters, and to test its efficacy by conducting a clustered practical randomized trial comparing the decision aid to usual depression care in primary care practices.We will use a novel practice-based, patient-centered approach based on participatory action research that involves a multidisciplinary team of designers, investigators, clinicians, patient representatives, and other stakeholders for the development of the decision aid. We will then conduct a clustered practical randomized trial enrolling clinicians and their patients (n = 300) with moderate to severe depression from rural, suburban and inner city primary care practices (n = 10). The intervention will consist of the use of the depression medication choice decision aid during the clinical encounter. This trial will generate preliminary evidence of the relative impact of the decision aid on patient involvement in decision making, decision making quality, patient knowledge, and 6-month measures of medication adherence and mental health compared to usual depression care. DISCUSSION: Upon completion of the proposed research, we will have developed and evaluated the efficacy of the decision aid depression medication choice as a novel translational tool for CER in depression treatment, engaged patients with depression in their care, and refined the process by which we conduct practice-based trials with limited research footprint. TRIAL REGISTRATION: Clinical Trials.gov: NCT01502891.

Lessons learned from the conduct of a multisite cluster randomized practical trial of decision aids in rural and suburban primary care practices.

BACKGROUND: The decision aids for diabetes (DAD) trial explored the feasibility of testing the effectiveness of decision aids (DAs) about coronary prevention and diabetes medications in community-based primary care practices, including rural clinics that care for patients with type 2 diabetes. METHODS: As originally designed, we invited clinicians in eight practices to participate in the trial, reviewed the patient panel of clinicians who accepted our invitation for potentially eligible patients, and contacted these patients by phone, enrolling those who accepted our invitation. As enrollment failed to meet targets, we recruited four new practices. After discussing the study with the clinicians and receiving their support, we reviewed all clinic panels for potentially eligible patients. Clinicians were approached to confirm participation and patient eligibility, and patients were approached before their visit to provide written informed consent. This in-clinic approach required study coordinators to travel and stay longer at the clinics as well as to screen more patient records for eligibility. The in-clinic approach was associated with better recruitment rates, lower patient retention and outcome completion rates, and a better intervention effect. RESULTS: We drew four lessons: 1) difficulties identifying potentially eligible patients threaten the viability of practical trials of DAs; 2) to improve the recruitment yield, recruit clinicians and patients for the study at the clinic, just before their visit; 3) approaches that improve recruitment may be associated with reduced retention and survey response; and 4) procedures that involve working closely with the practice may improve recruitment and may also affect the quality of the implementation of the interventions. CONCLUSION: Success in practice-based trials in usual primary care including rural clinics may require the smallest possible research footprint on the practice while implementing a streamlined protocol favoring in-clinic, in-person interactions with clinicians and patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT01029288.

Shared decision making in patients with stable coronary artery disease: PCI choice.

BACKGROUND: Percutaneous coronary intervention (PCI) and optimal medical therapy (OMT) are comparable, alternative therapies for many patients with stable angina; however, patients may have misconceptions regarding the impact of PCI on risk of death and myocardial infarction (MI) in stable coronary artery disease (CAD). METHODS AND RESULTS: We designed and developed a patient-centered decision aid (PCI Choice) to promote shared decision making for patients with stable CAD. The estimated benefits and risks of PCI+OMT as compared to OMT were displayed in a decision aid using pictographs with natural frequencies and text. We engaged patients, clinicians, health service researchers, and designers with over 20 successive iterations of the decision aid, which were field tested during real-world clinical encounters involving clinicians and patients. The decision aid is intended to facilitate knowledge transfer, deliberation based on patient values and preferences, and shared decision making. CONCLUSIONS: We describe the methods and outcomes of the design and development of a decision aid (PCI Choice) to promote shared decision making between clinicians and patients regarding the choice of PCI+OMT vs. OMT for treatment of stable CAD. We will evaluate the impact of PCI Choice on patient knowledge, decisional conflict, participation in decision-making, and treatment choice in an upcoming randomized trial.

The chest pain choice decision aid: a randomized trial.

BACKGROUND: Cardiac stress testing in patients at low risk for acute coronary syndrome is associated with increased false-positive test results, unnecessary downstream procedures, and increased cost. We judged it unlikely that patient preferences were driving the decision to obtain stress testing. METHODS AND RESULTS: The Chest Pain Choice trial was a prospective randomized evaluation involving 204 patients who were randomized to a decision aid or usual care and were followed for 30 days. The decision aid included a 100-person pictograph depicting the pretest probability of acute coronary syndrome and available management options (observation unit admission and stress testing or 24-72 hours outpatient follow-up). The primary outcome was patient knowledge measured by an immediate postvisit survey. Additional outcomes included patient engagement in decision making and the proportion of patients who decided to undergo observation unit admission and cardiac stress testing. Compared with usual care patients (n=103), decision aid patients (n=101) had significantly greater knowledge (3.6 versus 3.0 questions correct; mean difference, 0.67; 95% CI, 0.34-1.0), were more engaged in decision making as indicated by higher OPTION (observing patient involvement) scores (26.6 versus 7.0; mean difference, 19.6; 95% CI, 1.6-21.6), and decided less frequently to be admitted to the observation unit for stress testing (58% versus 77%; absolute difference, 19%; 95% CI, 6%-31%). There were no major adverse cardiac events after discharge in either group. CONCLUSIONS: Use of a decision aid in patients with chest pain increased knowledge and engagement in decision making and decreased the rate of observation unit admission for stress testing.