Depression is a common, debilitating, and costly chronic mental illness affecting 17% of Americans.^1-4 Depression burdens patients, their families, and the health care system.^1-4 Fortunately, treatment can effectively mitigate this burden.^5,6 Of the available treatments, pharmacotherapy has become the main modality, particularly for patients with moderate to severe depression.^7,8 Unfortunately, the efficacy of antidepressants is reduced by low patient adherence rates (13%-60%) and premature discontinuation (33%-42%) that contributes to avoidable disability, increased risk of relapse, and higher health care use.^9-11 Patients stop taking antidepressants because of unrealistic expectations, lack of treatment efficacy, or unacceptable adverse effects.^12,13 It would seem critical, therefore, to improve the process by which patients and clinicians select and implement antidepressants.

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

While an extensive array of existing evidence-based practices (EBPs) have the potential to improve patient outcomes, little is known about how to implement EBPs on a larger scale. Therefore, we sought to identify effective strategies for scaling up EBPs in primary care. We conducted a systematic review with the following inclusion criteria: (i) study design: randomized and non-randomized controlled trials, before-and-after (with/without control), and interrupted time series; (ii) participants: primary care-related units (e.g., clinical sites, patients); (iii) intervention: any strategy used to scale up an EBP; (iv) comparator: no restrictions; and (v) outcomes: no restrictions. We searched MEDLINE, Embase, PsycINFO, Web of Science, CINAHL, and the Cochrane Library from database inception to August 2016 and consulted clinical trial registries and gray literature. Two reviewers independently selected eligible studies, then extracted and analyzed data following the Cochrane methodology. We extracted components of scaling-up strategies and classified them into five categories: infrastructure, policy/regulation, financial, human resources-related, and patient involvement. We extracted scaling-up process outcomes, such as coverage, and provider/patient outcomes. We validated data extraction with study authors. We included 14 studies. They were published since 2003 and primarily conducted in low-/middle-income countries (n = 11). Most were funded by governmental organizations (n = 8). The clinical area most represented was infectious diseases (HIV, tuberculosis, and malaria, n = 8), followed by newborn/child care (n = 4), depression (n = 1), and preventing seniors’ falls (n = 1). Study designs were mostly before-and-after (without control, n = 8). The most frequently targeted unit of scaling up was the clinical site (n = 11). The component of a scaling-up strategy most frequently mentioned was human resource-related (n = 12). All studies reported patient/provider outcomes. Three studies reported scaling-up coverage, but no study quantitatively reported achieving a coverage of 80% in combination with a favorable impact. We found few studies assessing strategies for scaling up EBPs in primary care settings. It is uncertain whether any strategies were effective as most studies focused more on patient/provider outcomes and less on scaling-up process outcomes. Minimal consensus on the metrics of scaling up are needed for assessing the scaling up of EBPs in primary care. This review is registered as PROSPERO CRD42016041461 .

Background: Burnout among clinicians, including physicians, is a growing concern in healthcare. An overwhelming burden of clinical documentation is a significant contributor. While medical scribes have been employed to mitigate this burden, they have limitations such as cost, training needs, and high turnover rates. Artificial intelligence (AI) scribe systems can transcribe, summarize, and even interpret clinical conversations, offering a potential solution for improving clinician well-being. We aimed to evaluate the effectiveness of AI scribes in streamlining clinical documentation, with a focus on clinician experience, healthcare system efficiency, and patient engagement. Methods: We conducted a systematic review following Cochrane methods and PRISMA guidelines. Two reviewers conducted the selection process independently. Eligible intervention studies included quantitative and mixed-methods studies evaluating AI scribe systems. We summarized the data narratively. Results: Eight studies were included. AI scribes demonstrated positive effects on healthcare provider engagement, with users reporting increased involvement in their workflows. The documentation burden showed signs of improvement, as AI scribes helped alleviate the workload for some participants. Many clinicians have found AI systems to be user-friendly and intuitive, although some have expressed concerns about scribe training and documentation quality. A limited impact on reducing burnout was found, although documentation time improved in some studies. Conclusions: Most of the studies reported in this review involved small sample sizes and specific healthcare settings, limiting the generalizability of the findings to other contexts. Accuracy and consistency can vary significantly depending on the specific technology, model training data, and implementation approach. AI scribes show promise in improving documentation efficiency and clinician workflow, although the evidence remains limited and heterogeneous. Broader and real-world evaluations are needed to confirm their effectiveness and inform responsible implementations.

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.