Human factors in anesthesia: Risk assessment and clinical decision-making

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).

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Decision-making under pressure: medical errors in uncertain and dynamic environments

Australian Health Review ◽

10.1071/ah16088 ◽

2018 ◽

Vol 42 (4) ◽

pp. 395 ◽

Cited By ~ 15

Author(s):

Alicia M. Zavala ◽

Gary E. Day ◽

David Plummer ◽

Anita Bamford-Wade

Keyword(s):

Decision Making ◽

Human Factors ◽

Clinical Outcomes ◽

Medical Errors ◽

Thematic Analysis ◽

Clinical Decision Making ◽

Clinical Decision ◽

Dynamic Environments ◽

Emergency Situations ◽

Case Complexity

Objective This paper provides a narrative overview of the literature concerning clinical decision-making processes when staff come under pressure, particularly in uncertain, dynamic and emergency situations. Methods Studies between 1980 and 2015 were analysed using a six-phase thematic analysis framework to achieve an in-depth understanding of the complex origins of medical errors that occur when people and systems are under pressure and how work pressure affects clinical performance and patient outcomes. Literature searches were conducted using a Summons Search Service platform; search criteria included a variety of methodologies, resulting in the identification of 95 papers relevant to the present review. Results Six themes emerged in the present narrative review using thematic analysis: organisational systems, workload, time pressure, teamwork, individual human factors and case complexity. This analysis highlights that clinical outcomes in emergency situations are the result of a variety of interconnecting factors. These factors may affect the ability of clinical staff in emergency situations to provide quality, safe care in a timely manner. Conclusions The challenge for researchers is to build the body of knowledge concerning the safe management of patients, particularly where clinicians are working under pressure. This understanding is important for developing pathways that optimise clinical decision making in uncertain and dynamic environments. What is known about the topic? Emergency departments (EDs) are characterised by high complexity, high throughput and greater uncertainty compared with routine hospital wards or out-patient situations, and the ED is therefore prone to unpredictable workflows and non-replicable conditions when presented with unique and complex cases. What does this paper add? Clinical decision making can be affected by pressures with complex origins, including organisational systems, workload, time constraints, teamwork, human factors and case complexity. Interactions between these factors at different levels of the decision-making process can increase the complexity of problems and the resulting decisions to be made. What are the implications for practitioners? The findings of the present study provide further evidence that consideration of medical errors should be seen primarily from a ‘whole-of-system’ perspective rather than as being primarily the responsibility of individuals. Although there are strategies in place in healthcare organisations to eliminate errors, they still occur. In order to achieve a better understanding of medical errors in clinical practice in times of uncertainty, it is necessary to identify how diverse pressures can affect clinical decisions, and how these interact to influence clinical outcomes.

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Probability and loss: two sides of the risk assessment coin

The Psychiatrist ◽

10.1192/pb.bp.110.033910 ◽

2011 ◽

Vol 35 (11) ◽

pp. 413-418 ◽

Cited By ~ 11

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.

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

International Journal for Population Data Science ◽

10.23889/ijpds.v1i1.297 ◽

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.

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Improving risk assessment of violence among military Veterans: An evidence-based approach for clinical decision-making

Clinical Psychology Review ◽

10.1016/j.cpr.2010.03.009 ◽

2010 ◽

Vol 30 (6) ◽

pp. 595-607 ◽

Cited By ~ 49

Author(s):

Eric B. Elbogen ◽

Sara Fuller ◽

Sally C. Johnson ◽

Stephanie Brooks ◽

Patricia Kinneer ◽

...

Keyword(s):

Risk Assessment ◽

Decision Making ◽

Clinical Decision Making ◽

Military Veterans ◽

Clinical Decision ◽

Evidence Based

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Clinical Decision Making Using Ovarian Cancer Risk Assessment

American Journal of Roentgenology ◽

10.2214/ajr.09.3669 ◽

2010 ◽

Vol 194 (2) ◽

pp. 337-342 ◽

Cited By ~ 9

Author(s):

Michael P. Stany ◽

G. Larry Maxwell ◽

G. Scott Rose

Keyword(s):

Ovarian Cancer ◽

Risk Assessment ◽

Decision Making ◽

Cancer Risk ◽

Clinical Decision Making ◽

Clinical Decision ◽

Cancer Risk Assessment ◽

Ovarian Cancer Risk

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Integrated Risk Assessment and Feedback Reporting for Clinical Decision Making in a Medicare Risk Plan

Journal of Ambulatory Care Management ◽

10.1097/00004479-200010000-00006 ◽

2000 ◽

Vol 23 (4) ◽

pp. 40-47 ◽

Cited By ~ 1

Author(s):

Cheryl Schraeder ◽

Teri Britt ◽

Paul Shelton

Keyword(s):

Risk Assessment ◽

Decision Making ◽

Clinical Decision Making ◽

Clinical Decision ◽

Integrated Risk Assessment ◽

Integrated Risk

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

BMC Psychiatry ◽

10.1186/s12888-021-03115-3 ◽

2021 ◽

Vol 21 (1) ◽

Cited By ~ 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.

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