The Centers for Medicare and Medicaid Services (CMS) has mandated shared decision making (SDM) using patient decision aids for three conditions (lung cancer screening, atrial fibrillation, and implantable defibrillators). These forward-thinking approaches are in response to a wealth of efficacy data demonstrating that decision aids can improve patient decision making. However, there has been little focus on how to implement these approaches in real-world practice. This article demonstrates how using an implementation science framework may help programs understand multilevel challenges and opportunities to improve adherence to the CMS mandates. Using the PRISM (Pragmatic Robust Implementation and Sustainability Model) framework, we discuss general challenges to implementation of SDM, issues specific to each mandate, and how to plan for, enhance, and assess SDM implementation outcomes. Notably, a theme of this discussion is that successful implementation is context-specific and to truly have successful and sustainable changes in practice, context variability, and adaptation to context must be considered and addressed.

BACKGROUND: Counseling to reduce access to lethal means such as firearms and medications is recommended for suicidal adults but does not routinely occur. We developed the Web-based Lock to Live (L2L) decision aid to help suicidal adults and their families choose options for safer home storage. OBJECTIVE: This study aimed to test the feasibility and acceptability of L2L among suicidal adults in emergency departments (EDs). METHODS: At 4 EDs, we enrolled participants (English-speaking, community-dwelling, suicidal adults) in a pilot randomized controlled trial. Participants were randomized in a 13:7 ratio to L2L or control (website with general suicide prevention information) groups and received a 1-week follow-up telephone call. RESULTS: Baseline characteristics were similar between the intervention (n=33) and control (n=16) groups. At baseline, many participants reported having access to firearms (33/49, 67%), medications (46/49, 94%), or both (29/49, 59%). Participants viewed L2L for a median of 6 min (IQR 4-10 min). L2L also had very high acceptability; almost all participants reported that they would recommend it to someone in the same situation, that the options felt realistic, and that L2L was respectful of values about firearms. In an exploratory analysis of this pilot trial, more participants in the L2L group reported reduced firearm access at follow-up, although the differences were not statistically significant. CONCLUSIONS: The L2L decision aid appears feasible and acceptable for use among adults with suicide risk and may be a useful adjunct to lethal means counseling and other suicide prevention interventions. Future large-scale studies are needed to determine the effect on home access to lethal means. TRIAL REGISTRATION: ClinicalTrials.gov NCT03478501; https://clinicaltrials.gov/ct2/show/NCT03478501.

Shared decision making (SDM) is not widely practiced in routine care due to a variety of organizational, provider, patient, and contextual factors. This article explores how implementation science-which encourages attention to the multilevel contextual factors that influence the adoption, implementation, and sustainment of health care practices-can provide useful insights for increasing SDM use in routine practice. We engaged with stakeholders representing different organizations and geographic locations over three phases: 1) multidisciplinary workgroup meeting comprising researchers and clinicians (n = 11); 2) survey among a purposive sample of 47 patient advocates, clinicians, health care system leaders, funders, policymakers, and researchers; and 3) working session among diverse stakeholders (n = 30). The workgroup meeting identified priorities for action and research, which included targeting multiple audiences and levels, shifting culture toward valuing and supporting SDM, and considering contextual factors influencing SDM implementation. Survey respondents provided recommendations for increasing adoption, implementation, and maintenance of SDM in practice including providing tools to support SDM, obtaining stakeholders' involvement, and raising awareness of the importance of SDM. Stakeholders in the working session provided recommendations on the design of a guide for implementation of SDM in clinical settings, strategies to disseminate educational curricula on SDM, and strategies to influence policies to increase SDM use. These specific recommendations serve as a call to action to pursuing specific promising strategies aimed at increasing SDM use in practice and enhance understanding of the perspectives of diverse stakeholders at multiple levels from an implementation science perspective that appear fruitful for further study and application.

Importance: The clinical practice guidelines for heart failure recommend the use of validated risk models to estimate prognosis. Understanding how well models identify individuals who will die in the next year informs decision making for advanced treatments and hospice. Objective: To quantify how risk models calculated in routine practice estimate more than 50% 1-year mortality among ambulatory patients with heart failure who die in the subsequent year. Design, Setting, and Participants: Ambulatory adults with heart failure from 3 integrated health systems were enrolled between 2005 and 2008. The probability of death was estimated using the Seattle Heart Failure Model (SHFM) and the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk calculator. Baseline covariates were collected from electronic health records. Missing covariates were imputed. Estimated mortality was compared with actual mortality at both population and individual levels. Main Outcomes and Measures: One-year mortality. Results: Among 10930 patients with heart failure, the median age was 77 years, and 48.0% of these patients were female. In the year after study enrollment, 1661 patients died (15.9% by life-table analysis). At the population level, 1-year predicted mortality among the cohort was 9.7% for the SHFM (C statistic of 0.66) and 17.5% for the MAGGIC risk calculator (C statistic of 0.69). At the individual level, the SHFM predicted a more than 50% probability of dying in the next year for 8 of the 1661 patients who died (sensitivity for 1-year death was 0.5%) and for 5 patients who lived at least a year (positive predictive value, 61.5%). The MAGGIC risk calculator predicted a more than 50% probability of dying in the next year for 52 of the 1661 patients who died (sensitivity, 3.1%) and for 63 patients who lived at least a year (positive predictive value, 45.2%). Conversely, the SHFM estimated that 8496 patients (77.8%) had a less than 15% probability of dying at 1 year, yet this lower-risk end of the score range captured nearly two-thirds of deaths (n = 997); similarly, the MAGGIC risk calculator estimated a probability of dying of less than 25% for the majority of patients who died at 1 year (n = 914). Conclusions and Relevance: Although heart failure risk models perform reasonably well at the population level, they do not reliably predict which individual patients will die in the next year.