scholarly journals Probability and loss: two sides of the risk assessment coin

2011 ◽  
Vol 35 (11) ◽  
pp. 413-418 ◽  
Author(s):  
Matthew M. Large ◽  
Olav B. Nielssen
Keyword(s):  
Mental Health ◽  
Risk Assessment ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
The Other ◽  
Current Form ◽  
Mental Health Settings ◽  
Future Harm ◽  
Two Sides

SummaryRisk assessment has been widely adopted in mental health settings in the hope of preventing harms such as violence to others and suicide. However, risk assessment in its current form is mainly concerned with the probability of adverse events, and does not address the other component of risk – the extent of the resulting loss. Although assessments of the probability of future harm based on actuarial instruments are generally more accurate than the categorisations made by clinicians, actuarial instruments are of little assistance in clinical decision-making because there is no instrument that can estimate the probability of all the harms associated with mental illness, or estimate the extent of the resulting losses. The inability of instruments to distinguish between the risk of common but less serious harms and comparatively rare catastrophic events is a particular limitation of the value of risk categorisations. We should admit that our ability to assess risk is severely limited, and make clinical decisions in a similar way to those in other areas of medicine – by informed consideration of the potential consequences of treatment and non-treatment.

scholarly journals Correction to: Using behavioral insights to design implementation strategies in public mental health settings: a qualitative study of clinical decision-making

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Briana S. Last ◽  
Simone H. Schriger ◽  
Carter E. Timon ◽  
Hannah E. Frank ◽  
Alison M. Buttenheim ◽  
...  
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Qualitative Study ◽  
Clinical Decision Making ◽  
Public Mental Health ◽  
Implementation Strategies ◽  
Clinical Decision ◽  
Mental Health Settings

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals Assessment of violence risk in 440 psychiatric patients in China: examining the feasibility and acceptability of a novel and scalable approach (FoVOx)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shaoling Zhong ◽  
◽  
Rongqin Yu ◽  
Robert Cornish ◽  
Xiaoping Wang ◽  
...  
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Risk Assessment ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Psychiatric Patients ◽  
Clinical Decision ◽  
Violence Risk ◽  
Violence Risk Assessment ◽  
Risk Categories

Abstract Background Violence risk assessment is a routine part of clinical services in mental health, and in particular secure psychiatric hospitals. The use of prediction models and risk tools can assist clinical decision-making on risk management, including decisions about further assessments, referral, hospitalization and treatment. In recent years, scalable evidence-based tools, such as Forensic Psychiatry and Violent Oxford (FoVOx), have been developed and validated for patients with mental illness. However, their acceptability and utility in clinical settings is not known. Therefore, we conducted a clinical impact study in multiple institutions that provided specialist mental health service. Methods We followed a two-step mixed-methods design. In phase one, we examined baseline risk factors on 330 psychiatric patients from seven forensic psychiatric institutes in China. In phase two, we conducted semi-structured interviews with 11 clinicians regarding violence risk assessment from ten mental health centres. We compared the FoVOx score on each admission (n = 110) to unstructured clinical risk assessment and used a thematic analysis to assess clinician views on the accuracy and utility of this tool. Results The median estimated probability of violent reoffending (FoVOx score) within 1 year was 7% (range 1–40%). There was fair agreement (72/99, 73% agreement) on the risk categories between FoVOx and clinicians’ assessment on risk categories, and moderate agreement (10/12, 83% agreement) when examining low and high risk categories. In a majority of cases (56/101, 55%), clinicians thought the FoVOx score was an accurate representation of the violent risk of an individual patient. Clinicians suggested some additional clinical, social and criminal risk factors should be considered during any comprehensive assessment. In addition, FoVOx was considered to be helpful in assisting clinical decision-making and individual risk assessment. Ten out of 11 clinicians reported that FoVOx was easy to use, eight out of 11 was practical, and all clinicians would consider using it in the future. Conclusions Clinicians found that violence risk assessment could be improved by using a simple, scalable tool, and that FoVOx was feasible and practical to use.

scholarly journals Using behavioral insights to design implementation strategies in public mental health settings: a qualitative study of clinical decision-making

2020 ◽  
Author(s):  
Briana Shiri Last ◽  
Simone H Schriger ◽  
Carter E. Timon ◽  
Hannah E Frank ◽  
Alison M. Buttenheim ◽  
...  
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Implementation Strategies ◽  
Clinical Decision ◽  
Barriers And Facilitators ◽  
Trauma Narrative ◽  
Stakeholder Perspectives ◽  
Essential Components ◽  
Mental Health Settings

Abstract Background : Most studies evaluating the barriers and facilitators to evidence-based practice (EBP) implementation tend to overrely on stakeholder perspectives; underuse theory; and evaluate implementation of entire packages or protocols rather than specific, essential components of EBPs. These approaches make it challenging to generate implementation strategies that mechanistically target the core causes of behavior change. The present study seeks to leverage behavioral insights to identify factors that affect clinical decision-making surrounding the use of the trauma narrative (TN), the most active component of trauma focused-cognitive behavioral therapy (TF-CBT)—the gold standard EBP for youth with posttraumatic stress disorder—and to generate implementation strategies informed by these insights. Methods : Through semi-structured qualitative interviews, we sought the perspectives of trained TF-CBT therapists working in community mental health settings across the city of Philadelphia ( n =17) surrounding their clinical decision-making around when and why they use TNs. We used an iterative process of structured brainstorming and rapid validation informed by behavioral insights to uncover the barriers and facilitators to TN use and to generate implementation strategies using the “Easy Attractive Social Timely” (EAST) framework to increase TN implementation. Results : We generated and validated 9 hypothesized barriers to and facilitators of implementation of the trauma narrative that mapped onto 18 behavioral insights. Hypothesized barriers linked therapist perspectives (i.e., the barriers and facilitators they described) and behavioral insights to design 9 implementation strategies. Conclusions : The study responds to the growing need to identify implementation barriers and facilitators and to design implementation strategies that are informed by stakeholder perspectives, causal theories and that target specific components of EBPs. Our results reveal that behavioral insights of TN implementation can generate strategy designs. In the future, we will test the implementation strategies we designed to evaluate whether this process of developing implementation strategies is effective.

scholarly journals Biomechanical Rupture Risk Assessment

Aorta ◽  
2016 ◽  
Vol 04 (02) ◽  
pp. 42-60 ◽  
Author(s):  
T. Christian Gasser
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Validation Studies ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Patient Treatment ◽  
Maximum Diameter ◽  
Individual Risk ◽  
Rupture Risk ◽  
Aneurysm Wall

AbstractAbdominal aortic aneurysm (AAA) rupture is a local event in the aneurysm wall that naturally demands tools to assess the risk for local wall rupture. Consequently, global parameters like the maximum diameter and its expansion over time can only give very rough risk indications; therefore, they frequently fail to predict individual risk for AAA rupture. In contrast, the Biomechanical Rupture Risk Assessment (BRRA) method investigates the wall’s risk for local rupture by quantitatively integrating many known AAA rupture risk factors like female sex, large relative expansion, intraluminal thrombus-related wall weakening, and high blood pressure. The BRRA method is almost 20 years old and has progressed considerably in recent years, it can now potentially enrich the diameter indication for AAA repair. The present paper reviews the current state of the BRRA method by summarizing its key underlying concepts (i.e., geometry modeling, biomechanical simulation, and result interpretation). Specifically, the validity of the underlying model assumptions is critically disused in relation to the intended simulation objective (i.e., a clinical AAA rupture risk assessment). Next, reported clinical BRRA validation studies are summarized, and their clinical relevance is reviewed. The BRRA method is a generic, biomechanics-based approach that provides several interfaces to incorporate information from different research disciplines. As an example, the final section of this review suggests integrating growth aspects to (potentially) further improve BRRA sensitivity and specificity. Despite the fact that no prospective validation studies are reported, a significant and still growing body of validation evidence suggests integrating the BRRA method into the clinical decision-making process (i.e., enriching diameter-based decision-making in AAA patient treatment).

scholarly journals Storytelling, Safeguarding, Treatment, and Responsibility: attributes of recovery in psychiatric intensive care units

2014 ◽  
Vol 11 (02) ◽  
pp. 105-118 ◽  
Author(s):  
Karleen Gwinner ◽  
Louise Ward
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Mental Illness ◽  
Intensive Care ◽  
Lived Experience ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Review Of Literature ◽  
Psychiatric Distress ◽  
Nurse Knowledge

AbstractBackground and aimIn recent years, policy in Australia has endorsed recovery-oriented mental health services underpinned by the needs, rights and values of people with lived experience of mental illness. This paper critically reviews the idea of recovery as understood by nurses at the frontline of services for people experiencing acute psychiatric distress.MethodData gathered from focus groups held with nurses from two hospitals were used to ascertain their use of terminology, understanding of attributes and current practices that support recovery for people experiencing acute psychiatric distress. A review of literature further examined current nurse-based evidence and nurse knowledge of recovery approaches specific to psychiatric intensive care settings.ResultsFour defining attributes of recovery based on nurses’ perspectives are shared to identify and describe strategies that may help underpin recovery specific to psychiatric intensive care settings.ConclusionThe four attributes described in this paper provide a pragmatic framework with which nurses can reinforce their clinical decision-making and negotiate the dynamic and often incongruous challenges they experience to embed recovery-oriented culture in acute psychiatric settings.

scholarly journals Data linkage and statistical modelling to provide stratified risk assessment for HAI

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Kim Kavanagh ◽  
Jiafeng Pan ◽  
Chris Robertson ◽  
Marion Bennie ◽  
Charis Marwick ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Gastric Acid ◽  
Clinical Decision Making ◽  
Care Home ◽  
Clinical Decision ◽  
Elevated Risk ◽  
Defined Daily Dose ◽  
Healthcare Associated Infection ◽  
Healthcare Associated

ABSTRACT ObjectivesThe use of “real-time” data to support individual patient management and outcome assessment requires the development of risk assessment models. This could be delivered through a learning health system by the building robust statistical analysis tools onto the existing linked data held by NHS Scotland’s Infection Intelligence Platform (IIP) and developed within the Scottish Healthcare Associated Infection Prevention Institute (SHAIPI). This project will create prediction models for the risk of acquiring a healthcare associated infection (HAI), and particular outcomes, at the point of GP consultation/ hospital admission which could aid clinical decision making. ApproachWe demonstrate the capability using the HAI Clostridium difficile (CDI) from 2010-2013. Using linked national individual level data on community prescribing, hospitalisations, infections and death records we extracted all cases of CDI and by comparing to matched population-based controls, examined the impact of prior hospital admissions, care home residence, comorbidities, exposure to gastric acid suppressive drugs and antibiotic exposure, defined as both cumulative (total defined daily dose (DDD)) and temporal antimicrobial exposure in the previous 6 months, to the risk of CDI acquisition. Antimicrobial exposure was considered for all drugs and the higher risk broad spectrum antibiotics (4Cs). Associations are assessed using conditional logistic regression. Using cross-validation we assess the ability of the model to accurately predict CDI infection. Risk scores for acquisition of CDI are estimated by combining these predictions with age and gender population incidence. ResultsIn the period 2010-2013 there were 1446 cases of CDI with matched 7964 controls. A significant dose-response relationship for exposure to any antimicrobial (1-7 DDDs OR=2.3 rising to OR=4.4 for 29+ DDDs) and, with elevated risk, to the 4C group (1-7 DDDs OR=3.8 rising to OR=17.9 for 29+ DDDs). Exposure elevates CDI risk most in the month after prescription but for 4C antimicrobials the elevated risk remains 6 months later (4C OR=12.4 within 1 month, OR=2.6 4-6 months later). The risk of CDI was also increased with more co-morbidities, previous hospitalisations, care home residency, increased number of prescriptions, and gastric acid suppression. ConclusionDespite limitations to current application in practice,(paucity of patient level in-hospital prescribing data and constraints of the timeliness of the data), when fully developed this system will enable risk classification to identify patients most at risk of HAI and adverse outcomes to aid clinical decision making.

Mental Health Clinical Decision Support Exploiting Big Data

2019 ◽  
pp. 160-184 ◽  
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

Clinical outcome assessment in mental health

2018 ◽  
pp. 191-200
Author(s):  
Skye P. Barbic ◽  
Stefan J. Cano
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Clinical Outcome ◽  
Outcome Assessment ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Evidence Based ◽  
Measurement Issues ◽  
Health Disorders ◽  
Based Medicine

Clinical outcome assessment (COA) in mental health is essential to inform patient-centred care and clinical decision-making. In this chapter, the reader is introduced to COA as it is evolving in the field of mental health. Multiple approaches to COA are presented, but emphasis is placed on approaches that generate clinically meaningful data. Understanding COA can position clinicians and stakeholders to better evaluate their own practice and to contribute to the ongoing evolution of COA research and evidence-based medicine. This chapter begins with the definitions of assessment and measurement. Conceptual frameworks and models of COA development and testing are then presented. These are followed by a discussion of measurement in practice that reviews measurement issues related to clinical decision-making, programme evaluation, and clinical trials. Finally, this chapter highlights the contribution of metrology to improving health outcomes of individuals who experience mental health disorders.

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