scholarly journals Emotionally evocative patients in the emergency department: a mixed methods investigation of providers’ reported emotions and implications for patient safety

2020 ◽  
Vol 29 (10) ◽  
pp. 1.3-2 ◽  
Author(s):  
Linda M Isbell ◽  
Julia Tager ◽  
Kendall Beals ◽  
Guanyu Liu
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Emergency Department ◽  
Patient Safety ◽  
Qualitative Data ◽  
Clinical Decision Making ◽  
Quality Care ◽  
Clinical Decision ◽  
Safety Risks ◽  
Recent Patient

BackgroundEmergency department (ED) physicians and nurses frequently interact with emotionally evocative patients, which can impact clinical decision-making and behaviour. This study introduces well-established methods from social psychology to investigate ED providers’ reported emotional experiences and engagement in their own recent patient encounters, as well as perceived effects of emotion on patient care.MethodsNinety-four experienced ED providers (50 physicians and 44 nurses) vividly recalled and wrote about three recent patient encounters (qualitative data): one that elicited anger/frustration/irritation (angry encounter), one that elicited happiness/satisfaction/appreciation (positive encounter), and one with a patient with a mental health condition (mental health encounter). Providers rated their emotions and engagement in each encounter (quantitative data), and reported their perception of whether and how their emotions impacted their clinical decision-making and behaviour (qualitative data).ResultsProviders generated 282 encounter descriptions. Emotions reported in angry and mental health encounters were remarkably similar, highly negative, and associated with reports of low provider engagement compared with positive encounters. Providers reported their emotions influenced their clinical decision-making and behaviour most frequently in angry encounters, followed by mental health and then positive encounters. Emotions in angry and mental health encounters were associated with increased perceptions of patient safety risks; emotions in positive encounters were associated with perceptions of higher quality care.ConclusionsPositive and negative emotions can influence clinical decision-making and impact patient safety. Findings underscore the need for (1) education and training initiatives to promote awareness of emotional influences and to consider strategies for managing these influences, and (2) a comprehensive research agenda to facilitate discovery of evidence-based interventions to mitigate emotion-induced patient safety risks. The current work lays the foundation for testing novel interventions.

scholarly journals What do emergency department physicians and nurses feel? A qualitative study of emotions, triggers, regulation strategies, and effects on patient care

2020 ◽  
Vol 29 (10) ◽  
pp. 1.5-2 ◽  
Author(s):  
Linda M Isbell ◽  
Edwin D Boudreaux ◽  
Hannah Chimowitz ◽  
Guanyu Liu ◽  
Emma Cyr ◽  
...  
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Patient Safety ◽  
Patient Care ◽  
Clinical Decision Making ◽  
Negative Emotions ◽  
Clinical Decision ◽  
System Level ◽  
Emotional Suppression ◽  
Regulation Strategies

BackgroundDespite calls to study how healthcare providers’ emotions may impact patient safety, little research has addressed this topic. The current study aimed to develop a comprehensive understanding of emergency department (ED) providers’ emotional experiences, including what triggers their emotions, the perceived effects of emotions on clinical decision making and patient care, and strategies providers use to manage their emotions to reduce patient safety risks.MethodsEmploying grounded theory, we conducted 86 semi-structured qualitative interviews with experienced ED providers (45 physicians and 41 nurses) from four academic medical centres and four community hospitals in the Northeastern USA. Constant comparative analysis was used to develop a grounded model of provider emotions and patient safety in the ED.ResultsED providers reported experiencing a wide range of emotions in response to patient, hospital, and system-level factors. Patients triggered both positive and negative emotions; hospital and system-level factors largely triggered negative emotions. Providers expressed awareness of possible adverse effects of negative emotions on clinical decision making, highlighting concerns about patient safety. Providers described strategies they employ to regulate their emotions, including emotional suppression, distraction, and cognitive reappraisal. Many providers believed that these strategies effectively guarded against the risk of emotions negatively influencing their clinical decision making.ConclusionThe role of emotions in patient safety is in its early stages and many opportunities exist for researchers, educators, and clinicians to further address this important issue. Our findings highlight the need for future work to (1) determine whether providers’ emotion regulation strategies are effective at mitigating patient safety risk, (2) incorporate emotional intelligence training into healthcare education, and (3) shift the cultural norms in medicine to support meaningful discourse around emotions.

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 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 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.

Combined data mining techniques based patient data outlier detection for healthcare safety

2016 ◽  
Vol 9 (1) ◽  
pp. 42-68 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He ◽  
Dawit Haile
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Patient Safety ◽  
Outlier Detection ◽  
Clinical Data ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Healthcare Services ◽  
Content Type ◽  
Outliers Detection

Purpose – Among the growing number of data mining (DM) techniques, outlier detection has gained importance in many applications and also attracted much attention in recent times. In the past, outlier detection researched papers appeared in a safety care that can view as searching for the needles in the haystack. However, outliers are not always erroneous. Therefore, the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care, in particular. Design/methodology/approach – It is a combined DM (clustering and the nearest neighbor) technique for outliers’ detection, which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare safety. The outcomes or the knowledge implicit is vitally essential to a proper clinical decision-making process. The method is important to the semantic, and the novel tactic of patients’ events and situations prove that play a significant role in the process of patient care safety and medications. Findings – The outcomes of the paper is discussing a novel and integrated methodology, which can be inferring for different biological data analysis. It is discussed as integrated DM techniques to optimize its performance in the field of health and medical science. It is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient factors. Based on these facts, outliers are detected as clusters and point events, and novel ideas proposed to empower clinical services in consideration of customers’ satisfactions. It is also essential to be a baseline for further healthcare strategic development and research works. Research limitations/implications – This paper mainly focussed on outliers detections. Outlier isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not touch. Therefore, the research can be extended more about the hierarchy of patient problems. Originality/value – DM is a dynamic and successful gateway for discovering useful knowledge for enhancing healthcare performances and patient safety. Clinical data based outlier detection is a basic task to achieve healthcare strategy. Therefore, in this paper, the authors focussed on combined DM techniques for a deep analysis of clinical data, which provide an optimal level of clinical decision-making processes. Proper clinical decisions can obtain in terms of attributes selections that important to know the influential factors or parameters of healthcare services. Therefore, using integrated clustering and nearest neighbors techniques give more acceptable searched such complex data outliers, which could be fundamental to further analysis of healthcare and patient safety situational analysis.

scholarly journals Clinical decision making in the emergency department setting using rapid PCR: Results of the CLADE study group

2018 ◽  
Vol 102 ◽  
pp. 42-49 ◽  
Author(s):  
Glen T. Hansen ◽  
Johanna Moore ◽  
Emily Herding ◽  
Tami Gooch ◽  
Diane Hirigoyen ◽  
...  
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Study Group ◽  
Rapid Pcr ◽  
Emergency Department Setting

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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