scholarly journals A Review of Multidisciplinary Clinical Practice Guidelines in Suicide Prevention: Toward an Emerging Standard in Suicide Risk Assessment and Management, Training and Practice

2014 ◽  
Vol 38 (5) ◽  
pp. 585-592 ◽  
Author(s):  
Rebecca A. Bernert ◽  
Melanie A. Hom ◽  
Laura Weiss Roberts
Keyword(s):  
Risk Assessment ◽  
Clinical Practice ◽  
Suicide Prevention ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Suicide Risk ◽  
Management Training ◽  
Suicide Risk Assessment ◽  
Risk Assessment And Management

Online Suicide Risk Assessment and Management Training

Crisis ◽  
2017 ◽  
Vol 38 (3) ◽  
pp. 186-194 ◽  
Author(s):  
Kim Gryglewicz ◽  
Jason I. Chen ◽  
Gabriela D. Romero ◽  
Marc S. Karver ◽  
Melissa Witmeier
Keyword(s):  
Risk Assessment ◽  
Social Norms ◽  
Suicide Prevention ◽  
Suicide Risk ◽  
Behavioral Control ◽  
Online Training ◽  
Management Training ◽  
Perceived Behavioral Control ◽  
Suicide Risk Assessment ◽  
Risk Assessment And Management

Abstract. Background: Many mental health professionals (MHPs) encounter youth at risk for suicide but lack knowledge and confidence to assist these individuals. Unfortunately, training for MHPs on suicide risk assessment and management is often not adequately accessible. Aims: The aim of this study was to evaluate whether MHPs' knowledge, attitudes, perceived social norms, and perceived behavioral control in working with at-risk suicidal youth improve following an online training (QPRT: Question, Persuade, Refer, Treat). Method: QPRT was provided to 225 MHPs from three large urban areas in the United States. Suicide prevention literacy, attitudes, perceived social norms, and perceived behavioral control in assessing and managing suicide risk were assessed before and after training. Data were also collected on training engagement and completion. Results: Suicide prevention literacy in most competency domains and perceived behavioral control increased significantly after participation in QPRT. Suicide prevention attitudes and some knowledge domains did not significantly improve. MHPs reported high satisfaction with the training. Conclusion: The current study provides initial support for offering MHPs online suicide risk assessment and management training. Online training programs may be an engaging and feasible means for providing advanced suicide prevention skills to MHPs who may have numerous barriers to accessing face-to-face training.

Ethical Issues and Practical Challenges in Suicide Research

Crisis ◽  
2017 ◽  
Vol 38 (2) ◽  
pp. 107-114 ◽  
Author(s):  
Melanie A. Hom ◽  
Matthew C. Podlogar ◽  
Ian H. Stanley ◽  
Thomas E. Joiner
Keyword(s):  
Risk Assessment ◽  
Suicide Prevention ◽  
Suicide Risk ◽  
Ethical Issues ◽  
Panel Member ◽  
Prevention Research ◽  
Approval Process ◽  
Suicide Risk Assessment ◽  
Study Protocols ◽  
Risk Assessment And Management

Abstract. Background: Research with human subjects represents a critical avenue for suicide prevention efforts; however, such research is not without its ethical and practical challenges. Specifically, given the nature of research with individuals at elevated risk for suicide (e.g., increased concerns regarding participant safety, adverse events, liability, difficulties often arise during the institutional review board (IRB) evaluation and approval process. Aims: This paper aims to discuss IRB-related issues associated with suicide prevention research, including researcher and IRB panel member responsibilities, suicide risk assessment and management ethics and procedures, informed consent considerations, preparation of study protocols, and education and training. Points to consider and components to potentially include in an IRB application for suicide-related research are additionally provided. Method: Literature relevant to ethics in suicide research and suicide risk assessment and management was reviewed and synthesized. Results: Suicide research can be conducted in accordance with ethical principles while also furthering the science of suicide prevention. Conclusion: Despite the challenging nature of suicide prevention research, empirically informed solutions exist to address difficulties that may emerge in interfacing with IRBs. There remain areas for improvement in the IRB approval process that warrant further investigation and work.

Suicide Risk Assessment and Management in the Psychiatry Emergency Service: Psychiatric Provider Experience and Perceptions

2018 ◽  
Vol 23 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Sruthi Chunduri ◽  
Sabrina Browne ◽  
David E. Pollio ◽  
Barry A. Hong ◽  
Writtika Roy ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Suicide Risk ◽  
Emergency Service ◽  
Suicide Risk Assessment ◽  
Provider Experience ◽  
Risk Assessment And Management

Conceptualization and Pilot Testing of a Core Competency-Based Training Workshop in Suicide Risk Assessment and Management: Notes From the Field

2016 ◽  
Vol 73 (3) ◽  
pp. 233-238 ◽  
Author(s):  
Robert J. Cramer ◽  
Claire N. Bryson ◽  
Morgam K. Eichorst ◽  
Lee N. Keyes ◽  
Brittany E. Ridge
Keyword(s):  
Risk Assessment ◽  
Suicide Risk ◽  
Core Competency ◽  
Suicide Risk Assessment ◽  
Training Workshop ◽  
Pilot Testing ◽  
Competency Based ◽  
Risk Assessment And Management

Implementing machine learning algorithms for suicide risk prediction in clinical practice: A focus group study

2021 ◽  
Author(s):  
Kate Bentley ◽  
Kelly Zuromski ◽  
Rebecca Fortgang ◽  
Emily Madsen ◽  
Daniel Kessler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Clinical Practice ◽  
Risk Prediction ◽  
Suicide Risk ◽  
Prediction Models ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Suicide Risk Assessment ◽  
Focus Group Study

Background: Interest in developing machine learning algorithms that use electronic health record data to predict patients’ risk of suicidal behavior has recently proliferated. Whether and how such models might be implemented and useful in clinical practice, however, remains unknown. In order to ultimately make automated suicide risk prediction algorithms useful in practice, and thus better prevent patient suicides, it is critical to partner with key stakeholders (including the frontline providers who will be using such tools) at each stage of the implementation process.Objective: The aim of this focus group study was to inform ongoing and future efforts to deploy suicide risk prediction models in clinical practice. The specific goals were to better understand hospital providers’ current practices for assessing and managing suicide risk; determine providers’ perspectives on using automated suicide risk prediction algorithms; and identify barriers, facilitators, recommendations, and factors to consider for initiatives in this area. Methods: We conducted 10 two-hour focus groups with a total of 40 providers from psychiatry, internal medicine and primary care, emergency medicine, and obstetrics and gynecology departments within an urban academic medical center. Audio recordings of open-ended group discussions were transcribed and coded for relevant and recurrent themes by two independent study staff members. All coded text was reviewed and discrepancies resolved in consensus meetings with doctoral-level staff. Results: Though most providers reported using standardized suicide risk assessment tools in their clinical practices, existing tools were commonly described as unhelpful and providers indicated dissatisfaction with current suicide risk assessment methods. Overall, providers’ general attitudes toward the practical use of automated suicide risk prediction models and corresponding clinical decision support tools were positive. Providers were especially interested in the potential to identify high-risk patients who might be missed by traditional screening methods. Some expressed skepticism about the potential usefulness of these models in routine care; specific barriers included concerns about liability, alert fatigue, and increased demand on the healthcare system. Key facilitators included presenting specific patient-level features contributing to risk scores, emphasizing changes in risk over time, and developing systematic clinical workflows and provider trainings. Participants also recommended considering risk-prediction windows, timing of alerts, who will have access to model predictions, and variability across treatment settings.Conclusions: Providers were dissatisfied with current suicide risk assessment methods and open to the use of a machine learning-based risk prediction system to inform clinical decision-making. They also raised multiple concerns about potential barriers to the usefulness of this approach and suggested several possible facilitators. Future efforts in this area will benefit from incorporating systematic qualitative feedback from providers, patients, administrators, and payers on the use of new methods in routine care, especially given the complex, sensitive, and unfortunately still stigmatized nature of suicide risk.

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