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A pilot of a veteran suicide prevention learning collaborative among community organizations: Initial results and outcomes.

Veteran suicide remains an ongoing public health concern in need of fresh, community-based initiatives. The Department of Veterans Affairs (VA) has built an enterprise-wide integrated behavioral health system that has pioneered numerous suicide prevention methods. However, most Veterans receive healthcare outside the VA, from organizations that may not be equipped to address Veteran suicide risk. One solution is implementing a VA/community suicide prevention learning collaborative to support organizations in implementing suicide prevention best practices for Veterans. Although learning collaboratives have a history of supporting improved patient safety in healthcare systems, to our knowledge, none have focused on Veteran suicide prevention. The current quality improvement project sought to pilot a VA/community suicide prevention learning collaborative in the broader Denver and Colorado Springs areas with 13 organizations that served, interacted with, or employed Veterans. The collaborative had a large footprint in the region, with organizations interacting with over 24,000 community members and over 5000 Veterans. Organizations implemented 92 Veteran suicide prevention program components within a 16-month period. Overall, the learning collaborative made significant strides in Veteran suicide prevention. Findings suggest that this method facilitates rapid implementation of Veteran suicide prevention practices and may be promising for accelerating uptake within communities.

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Open Access
Leveraging the utility of pharmacogenomics in psychiatry through clinical decision support: a focus group study

BackgroundPharmacogenomics is starting to build momentum in clinical utility, perhaps the most in mental and behavioral healthcare. However, efficient delivery of this information to the point of prescribing remains a significant challenge. Clinical decision support has an opportunity to address this void by integrating pharmacogenomics into the clinician workflow.MethodsTo address the specific needs of mental health clinicians at the point of care, we conducted 3 focus groups with a total of 16 mental health clinicians. Each 1-h focus group was designed to identify the desired clinical decision support features, with a particular interest in pharmacogenomics, and potential negative or unintended consequences of clinical decision support integration at the point of care in a mental healthcare setting. We implemented an iterative design to expand upon knowledge generated in prior focus groups. The results from the guided discussion in the first focus group were used to develop a mental health clinical decision support prototype. This prototype was then presented during the next two focus groups to drive the discussion.ResultsThis study has identified main themes related to the desired clinical decision support features of mental health clinicians, the use of pharmacogenomics in practice, and unintended and negative consequences of clinical decision support integration at the point of care. Clinicians desire a more complete picture of the medication history of patients and guidance to choose medications in relation to cost, insurance coverage, and pharmacogenetics interactions. Mental health clinicians agreed that pharmacogenetics is useful and impacts their prescribing decisions when the data are available. Several negative consequences of clinical decision support integration were identified including alert fatigue and frustration using the tool. Several points of contention were related to the integration of the clinical decision support with the electronic health record, including bidirectional flow of information, speed, location within workflow, and potential incompleteness of information.ConclusionsWe have identified general and unique considerations of mental health clinicians with regard to clinical decision support. Clinical decision support that incorporates desired features while avoiding negative and unintended consequences will increase clinician usage and will have the potential to improve the care of patients.

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Open Access
Autistic Symptoms in Schizophrenia Spectrum Disorders: A Systematic Review and Meta-Analysis.

Background: Recent studies have examined the association between autism spectrum disorder and schizophrenia spectrum disorders, describing a number of cognitive features common to both conditions (e.g., weak central coherence, difficulties in set-shifting, impairment in theory of mind). Several studies have reported high levels of autistic symptoms in population with schizophrenia spectrum disorders. Our study systematically reviews and quantitatively synthetizes the current evidence on the presence of autistic symptoms in individuals with schizophrenia spectrum disorders.Methods: A comprehensive literature search of the PubMed/MEDLINE, Cochrane Library, CINHAL, and Embase databases was performed from the date of their inceptions until March 2018. The primary outcome measure was the Autism Spectrum Quotient (AQ). As secondary outcome measures, we analyzed the AQ subscales. Data were extracted and analyzed by using a conservative model and expressed by standardized mean difference (SMD).Results: Thirteen studies comprising a total of 1,958 individuals were included in the analysis. Results showed that individuals with schizophrenia spectrum disorders have higher levels of autistic symptoms compared to healthy controls [SMD: 1.39, 95% confidence interval (CI): 1.11 to 1.68] and lower levels of autistic symptoms compared to individuals with autism (SMD: −1.27, 95% CI: −1.77 to −0.76).Conclusions: Current findings support that individuals with schizophrenia spectrum disorders have higher autistic symptoms than healthy controls. Therefore, further studies are needed in order to shed light on the association between these two conditions.

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Open Access
The Relationship between Sleep Problems, Neurobiological Alterations, Core Symptoms of Autism Spectrum Disorder, and Psychiatric Comorbidities.

Children with Autism Spectrum Disorder (ASD) are at an increased risk for sleep disturbances, and studies indicate that between 50 and 80% of children with ASD experience sleep problems. These problems increase parental stress and adversely affect family quality of life. Studies have also suggested that sleep disturbances may increase behavioral problems in this clinical population. Although understanding the causes of sleep disorders in ASD is a clinical priority, the causal relationship between these two conditions remains unclear. Given the complex nature of ASD, the etiology of sleep problems in this clinical population is probably multi-factorial. In this overview, we discuss in detail three possible etiological explanations of sleep problems in ASD that can all contribute to the high rate of these symptoms in ASD. Specifically, we examine how neurobiological alterations, genetic mutations, and disrupted sleep architecture can cause sleep problems in individuals with ASD. We also discuss how sleep problems may be a direct result of core symptoms of ASD. Finally, a detailed examination of the relationship between sleep problems and associated clinical features and psychiatric comorbidities in individuals with ASD is described.

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Chronic Pain in Children: A Look at the Referral Process to a Pediatric Pain Clinic

We reviewed the referral pattern of children with chronic pain to a specialized pediatric pain clinic. Data were obtained from referring physicians and medical records and during an interview with patients and their parents by physicians and a psychologist. We analyzed the following: referral diagnosis, demographics, duration of symptoms, number of physicians previously consulted, school attendance, sports activities, presence of psychological disorders, final team diagnosis, and outcomes. Children had been experiencing pain for 34 ± 55 months. Patients had consulted on average 3 physicians in addition to their pediatrician. 32% of the patients had missed at least 10 days of school in a calendar year, and 47% had stopped playing sports. 15% had an operation because of pain that had been unsuccessful. The most common missed diagnosis was anxiety (25%) and depression (13%). 69% of the patients were back to school and/or playing sports within 4 months from our initial consultation. 32% of the patients did not make any progress during the follow-up period. The most common reasons for failure to improve were no compliance with the recommended treatments and poorly controlled major mood disorder. The time to refer children with chronic pain for specialized care could be extremely long causing significant social and psychological consequence.

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Open Access