Project description:BackgroundPeer support provides the opportunity for peers with experiential knowledge of a mental illness to give emotional, appraisal and informational assistance to current service users, and is becoming an important recovery-oriented approach in healthcare for people with mental illness.ObjectivesTo assess the effects of peer-support interventions for people with schizophrenia or other serious mental disorders, compared to standard care or other supportive or psychosocial interventions not from peers.Search methodsWe searched the Cochrane Schizophrenia Group's Study-Based Register of Trials on 27 July 2016 and 4 July 2017. There were no limitations regarding language, date, document type or publication status.Selection criteriaWe selected all randomised controlled clinical studies involving people diagnosed with schizophrenia or other related serious mental illness that compared peer support to standard care or other psychosocial interventions and that did not involve 'peer' individual/group(s). We included studies that met our inclusion criteria and reported useable data. Our primary outcomes were service use and global state (relapse).Data collection and analysisThe authors of this review complied with the Cochrane recommended standard of conduct for data screening and collection. Two review authors independently screened the studies, extracted data and assessed the risk of bias of the included studies. Any disagreement was resolved by discussion until the authors reached a consensus. We calculated the risk ratio (RR) and 95% confidence interval (CI) for binary data, and the mean difference and its 95% CI for continuous data. We used a random-effects model for analyses. We assessed the quality of evidence and created a 'Summary of findings' table using the GRADE approach.Main resultsThis review included 13 studies with 2479 participants. All included studies compared peer support in addition to standard care with standard care alone. We had significant concern regarding risk of bias of included studies as over half had an unclear risk of bias for the majority of the risk domains (i.e. random sequence generation, allocation concealment, blinding, attrition and selective reporting). Additional concerns regarding blinding of participants and outcome assessment, attrition and selective reporting were especially serious, as about a quarter of the included studies were at high risk of bias for these domains.All included studies provided useable data for analyses but only two trials provided useable data for two of our main outcomes of interest, and there were no data for one of our primary outcomes, relapse. Peer support appeared to have little or no effect on hospital admission at medium term (RR 0.44, 95% CI 0.11 to 1.75; participants = 19; studies = 1, very low-quality evidence) or all-cause death in the long term (RR 1.52, 95% CI 0.43 to 5.31; participants = 555; studies = 1, very low-quality evidence). There were no useable data for our other prespecified important outcomes: days in hospital, clinically important change in global state (improvement), clinically important change in quality of life for peer supporter and service user, or increased cost to society.One trial compared peer support with clinician-led support but did not report any useable data for the above main outcomes.Authors' conclusionsCurrently, very limited data are available for the effects of peer support for people with schizophrenia. The risk of bias within trials is of concern and we were unable to use the majority of data reported in the included trials. In addition, the few that were available, were of very low quality. The current body of evidence is insufficient to either refute or support the use of peer-support interventions for people with schizophrenia and other mental illness.

Project description:ImportanceDiscussions about goals of care with patients who are seriously ill typically occur infrequently and late in the illness trajectory, are of low quality, and focus narrowly on the patient's resuscitation preferences (ie, code status), risking provision of care that is inconsistent with patients' values. The Serious Illness Care Program (SICP) is a multifaceted communication intervention that builds capacity for clinicians to have earlier, more frequent, and more person-centered conversations.ObjectiveTo explore clinicians' experiences with the SICP 1 year after implementation.Design, setting, and participantsThis qualitative study was conducted at 2 tertiary care hospitals in Canada. The SICP was implemented at Hamilton General Hospital (Hamilton, Ontario) from March 1, 2017, to January 19, 2018, and at Foothills Medical Centre (Calgary, Alberta) from March 1, 2018, to December 31, 2020. A total of 45 clinicians were invited to participate in the study, and 23 clinicians (51.1%) were enrolled and interviewed. Semistructured interviews of clinicians were conducted between August 2018 and May 2019. Content analysis was used to evaluate information obtained from these interviews between May 2019 and May 2020.ExposuresThe SICP includes clinician training, communication tools, and processes for system change.Main outcomes and measuresClinicians' experiences with and perceptions of the SICP.ResultsAmong 23 clinicians interviewed, 15 (65.2%) were women. The mean (SD) number of years in practice was 14.6 (9.1) at the Hamilton site and 12.0 (6.9) at the Calgary site. Participants included 19 general internists, 3 nurse practitioners, and 1 social worker. The 3 main themes were the ways in which the SICP (1) supported changes in clinician behavior, (2) shifted the focus of goals-of-care conversations beyond discussion of code status, and (3) influenced clinicians personally and professionally. Changes in clinician behavior were supported by having a unit champion, interprofessional engagement, access to copies of the Serious Illness Conversation Guide, and documentation in the electronic medical record. Elements of the program, especially the Serious Illness Conversation Guide, shifted the focus of goals-of-care conversations beyond discussion of code status and influenced clinicians on personal and professional levels. Concerns with the program included finding time to have conversations, building transient relationships, and limiting conversation fluidity.Conclusions and relevanceIn this qualitative study, hospital clinicians described components of the SICP as supporting changes in their behavior and facilitating meaningful patient interactions that shifted the focus of goals-of-care conversations beyond discussion of code status. The perceived benefits of SICP implementation stimulated uptake within the medical units. These findings suggest that the SICP may prompt hospital culture changes in goals-of-care dialogue with patients and the care of hospitalized patients with serious illness.

Project description:BackgroundAdvance care planning (ACP) conversations are associated with improved end-of-life healthcare outcomes and patients want to engage in ACP with their healthcare providers. Despite this, ACP conversations rarely occur in primary care settings. The objective of this study was to implement ACP through adapted Serious Illness Care Program (SICP) training sessions, and to understand primary care provider (PCP) perceptions of implementing ACP into practice.MethodsWe conducted a quality improvement project guided by the Normalization Process Theory (NPT), in an interprofessional academic family medicine group in Hamilton, Ontario, Canada. NPT is an explanatory model that delineates the processes by which organizations implement and integrate new work. PCPs (physicians, family medicine residents, and allied health care providers), completed pre- and post-SICP self-assessments evaluating training effectiveness, a survey evaluating program implementability and sustainability, and semi-structured qualitative interviews to elaborate on barriers, facilitators, and suggestions for successful implementation. Descriptive statistics and pre-post differences (Wilcoxon Sign-Rank test) were used to analyze surveys and thematic analysis was used to analyze qualitative interviews.Results30 PCPs participated in SICP training and completed self-assessments, 14 completed NoMAD surveys, and 7 were interviewed. There were reported improvements in ACP confidence and skills. NoMAD surveys reported mixed opinions towards ACP implementation, specifically concerning colleagues' abilities to conduct ACP and patients' abilities to participate in ACP. Physicians discussed busy clinical schedules, lack of patient preparedness, and continued discomfort or lack of confidence in having ACP conversations. Allied health professionals discussed difficulty sharing patient prognosis and identification of appropriate patients as barriers.ConclusionsTraining in ACP conversations improved PCPs' individual perceived abilities, but discomfort and other barriers were identified. Future iterations will require a more systematic process to support the implementation of ACP into regular practice, in addition to addressing knowledge and skill gaps.

Project description:BACKGROUND:Peer support is recognized globally as an essential recovery service for people with mental health conditions. With the influx of digital mental health services changing the way mental health care is delivered, peer supporters are increasingly using technology to deliver peer support. In light of these technological advances, there is a need to review and synthesize the emergent evidence for peer-supported digital health interventions for adults with mental health conditions. OBJECTIVE:The aim of this study was to identify and review the evidence of digital peer support interventions for people with a lived experience of a serious mental illness. METHODS:This systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedures. The PubMed, Embase, Web of Science, Cochrane Central, CINAHL, and PsycINFO databases were searched for peer-reviewed articles published between 1946 and December 2018 that examined digital peer support interventions for people with a lived experience of a serious mental illness. Additional articles were found by searching the reference lists from the 27 articles that met the inclusion criteria and a Google Scholar search in June 2019. Participants, interventions, comparisons, outcomes, and study design (PICOS) criteria were used to assess study eligibility. Two authors independently screened titles and abstracts, and reviewed all full-text articles meeting the inclusion criteria. Discrepancies were discussed and resolved. All included studies were assessed for methodological quality using the Methodological Quality Rating Scale. RESULTS:A total of 30 studies (11 randomized controlled trials, 2 quasiexperimental, 15 pre-post designs, and 2 qualitative studies) were included that reported on 24 interventions. Most of the studies demonstrated feasibility, acceptability, and preliminary effectiveness of peer-to-peer networks, peer-delivered interventions supported with technology, and use of asynchronous and synchronous technologies. CONCLUSIONS:Digital peer support interventions appear to be feasible and acceptable, with strong potential for clinical effectiveness. However, the field is in the early stages of development and requires well-powered efficacy and clinical effectiveness trials. TRIAL REGISTRATION:PROSPERO CRD42020139037; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID= 139037.

Project description:Importance:Serious illness conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Interventions that increase the rate of SICs between oncology clinicians and patients may improve goal-concordant care and patient outcomes. Objective:To determine the effect of a clinician-directed intervention integrating machine learning mortality predictions with behavioral nudges on motivating clinician-patient SICs. Design, Setting, and Participants:This stepped-wedge cluster randomized clinical trial was conducted across 20 weeks (from June 17 to November 1, 2019) at 9 medical oncology clinics (8 subspecialty oncology and 1 general oncology clinics) within a large academic health system in Pennsylvania. Clinicians at the 2 smallest subspecialty clinics were grouped together, resulting in 8 clinic groups randomly assigned to the 4 intervention wedge periods. Included participants in the intention-to-treat analyses were 78 oncology clinicians who received SIC training and their patients (N?=?14?607) who had an outpatient oncology encounter during the study period. Interventions:(1) Weekly emails to oncology clinicians with SIC performance feedback and peer comparisons; (2) a list of up to 6 high-risk patients (?10% predicted risk of 180-day mortality) scheduled for the next week, estimated using a validated machine learning algorithm; and (3) opt-out text message prompts to clinicians on the patient's appointment day to consider an SIC. Clinicians in the control group received usual care consisting of weekly emails with cumulative SIC performance. Main Outcomes and Measures:Percentage of patient encounters with an SIC in the intervention group vs the usual care (control) group. Results:The sample consisted of 78 clinicians and 14?607 patients. The mean (SD) age of patients was 61.9 (14.2) years, 53.7% were female, and 70.4% were White. For all encounters, SICs were conducted among 1.3% in the control group and 4.6% in the intervention group, a significant difference (adjusted difference in percentage points, 3.3; 95% CI, 2.3-4.5; P?<?.001). Among 4124 high-risk patient encounters, SICs were conducted among 3.6% in the control group and 15.2% in the intervention group, a significant difference (adjusted difference in percentage points, 11.6; 95% CI, 8.2-12.5; P?<?.001). Conclusions and Relevance:In this stepped-wedge cluster randomized clinical trial, an intervention that delivered machine learning mortality predictions with behavioral nudges to oncology clinicians significantly increased the rate of SICs among all patients and among patients with high mortality risk who were targeted by the intervention. Behavioral nudges combined with machine learning mortality predictions can positively influence clinician behavior and may be applied more broadly to improve care near the end of life. Trial Registration:ClinicalTrials.gov Identifier: NCT03984773.

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PSST! I Need Help! Development of a Peer Support Program for Clinicians Having Serious Illness Conversations During COVID-19.

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