scholarly journals Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ji Hoon Kim ◽  
Sang Gil Han ◽  
Ara Cho ◽  
Hye Jung Shin ◽  
Song-Ee Baek
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Deep Learning ◽  
Assistive Technology ◽  
Performance Assessment ◽  
Technology Use ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Assessment System ◽  
Kappa Value

Abstract Background Interpretation of chest radiographs (CRs) by emergency department (ED) physicians is inferior to that by radiologists. Recent studies have investigated the effect of deep learning-based assistive technology on CR interpretation (DLCR), although its relevance to ED physicians remains unclear. This study aimed to investigate whether DLCR supports CR interpretation and the clinical decision-making of ED physicians. Methods We conducted a prospective interventional study using a web-based performance assessment system. Study participants were recruited through the official notice targeting board for certified emergency physicians and residents working at the present ED. Of the eight ED physicians who volunteered to participate in the study, seven ED physicians were included, while one participant declared withdrawal during performance assessment. Seven physicians’ CR interpretations and clinical decision-making were assessed based on the clinical data from 388 patients, including detecting the target lesion with DLCR. Participant performance was evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy analyses; decision-making consistency was measured by kappa statistics. ED physicians with < 24 months of experience were defined as ‘inexperienced’. Results Among the 388 simulated cases, 259 (66.8%) had CR abnormality. Their median value of abnormality score measured by DLCR was 59.3 (31.77, 76.25) compared to a score of 3.35 (1.57, 8.89) for cases of normal CR. There was a difference in performance between ED physicians working with and without DLCR (AUROC: 0.801, P < 0.001). The diagnostic sensitivity and accuracy of CR were higher for all ED physicians working with DLCR than for those working without it. The overall kappa value for decision-making consistency was 0.902 (95% confidence interval [CI] 0.884–0.920); concurrently, the kappa value for the experienced group was 0.956 (95% CI 0.934–0.979), and that for the inexperienced group was 0.862 (95% CI 0.835–0.889). Conclusions This study presents preliminary evidence that ED physicians using DLCR in a clinical setting perform better at CR interpretation than their counterparts who do not use this technology. DLCR use influenced the clinical decision-making of inexperienced physicians more strongly than that of experienced physicians. These findings require prospective validation before DLCR can be recommended for use in routine clinical practice.

Deep Learning-Based Assistive Technology in Chest Radiograph Interpretation by Emergency Department Physicians: Prospective Study (Preprint)

2021 ◽  
Author(s):  
Ji Hoon Kim ◽  
Sang Gil Han ◽  
Ara Cho ◽  
Hye Jung Shin ◽  
Song-Ee Baek
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Deep Learning ◽  
Prospective Study ◽  
Assistive Technology ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
Receiver Operating Characteristics Curve

BACKGROUND Interpretation of chest radiographs (CRs) performed by emergency department (ED) physicians is inferior to that by radiologists. Recent studies have investigated the impact of deep learning-based assistive technology on CR interpretation (DLCR), but its relevance to ED physicians remains unclear. OBJECTIVE This study aimed to investigate whether DLCR supports CR interpretation and clinical decision-making of ED physicians METHODS Seven ED physicians were used in a prospective study. CR interpretation and clinical decision-making were assessed based on 388 clinical cases, including detecting the target lesion with DLCR. Participant performance was evaluated by area under the receiver operating characteristics curve, sensitivity, specificity, and accuracy analyses; decision-making consistency was measured by kappa statistics. RESULTS There was a difference in performance between ED physicians working with and without DLCR (area under the receiver operating characteristics curve: 0.801, P<.001). Diagnostic sensitivity and accuracy of CR were higher for all ED physicians working with DLCR than for those working without it. The overall kappa value for decision-making consistency was 0.902 (95% CI: 0.884–0.920); concurrently, that for the experienced group was 0.956 (95% CI: 0.934–0.979) and that for the inexperienced group was 0.862 (95% CI: 0.835–0.889). CONCLUSIONS This study presents preliminary evidence that ED physicians using DLCR in a clinical setting perform better at CR interpretation than their counterparts who do not use this technology. DLCR use influenced clinical decision-making of inexperienced physicians more strongly than it did that of experienced physicians. These findings require prospective validation before DLCR can be recommended for use in routine clinical practice. CLINICALTRIAL none declared

scholarly journals Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Steven A. Hicks ◽  
Jonas L. Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Data ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Model

AbstractDeep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.

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

scholarly journals Utility and Acceptability of Influenza Surveillance amongst Emergency Providers

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Andrea F. Dugas ◽  
Howard Burkom ◽  
Anna L. DuVal ◽  
Richard Rothman
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Real Time ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Surveillance Data ◽  
Influenza Surveillance ◽  
Additional Information ◽  
Provider Surveys

We provided emergency department providers with a real-time laboratory-based influenza surveillance tool, and evaluated the utility and acceptability of the surveillance information using provider surveys. The majority of emergency department providers found the surveillance data useful and indicated the additional information impacted their clinical decision making regarding influenza testing and treatment.

scholarly journals Recovery from profound acidosis (pH 6.685) in multi-organ dysfunction syndrome

2019 ◽  
pp. 175114371987010
Author(s):  
Eryl A Davies ◽  
Christopher Saleh ◽  
Jonathan Bannard-Smith
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Cardiac Arrest ◽  
Critical Care ◽  
Organ Dysfunction ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Circulatory Shock ◽  
Arterial Ph ◽  
Multi Organ Dysfunction Syndrome

Acidosis is a common feature of patients referred to critical care from the emergency department. We present the case of a 49-year-old female with multi-organ dysfunction syndrome (MODS) and an arterial pH of 6.685 on arrival to the emergency department. This case is unique as the patient was in circulatory shock with MODS from rhabdomyolysis on arrival and had not suffered a cardiac arrest. We believe this to be the first reported case of full recovery from such an extreme metabolic disturbance in this context, and discuss the relevance of profound acidosis to early clinical decision-making.

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 FUTURES: Forecasting the Unexpected Transfer to Upgraded Resources in Sepsis

2019 ◽  
Vol 8 (1) ◽  
pp. 187-191
Author(s):  
Alexa Profozich ◽  
Trevor Sytsma ◽  
Ryan Arnold ◽  
Kristen Miller ◽  
Muge Capan
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Clinical Decision Making ◽  
Point Of Care ◽  
Initial Point ◽  
Clinical Decision ◽  
Sepsis Management ◽  
Insight Into

Sepsis is one of the most deadly and costly diseases. The Emergency Department (ED) is the initial point of care for most patients who become hospitalized due to sepsis. Quantifying the accuracy of ED clinician forecasting regarding patients’ clinical trajectories and outcomes can provide insight into clinical decision making and inform sepsis management.

scholarly journals Perceptions of critical and emergency department nurses regarding changes in their professional role

2018 ◽  
Vol 51 (0) ◽  
Author(s):  
Maria NK Karanikola ◽  
Margarita Giannakopoulou ◽  
Meropi Mpouzika ◽  
Christiana Nicolaou ◽  
George Tsiaousis ◽  
...  
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Clinical Decision Making ◽  
Clinical Skills ◽  
Preliminary Investigation ◽  
Clinical Decision ◽  
Professional Role ◽  
Professional Autonomy ◽  
Emergency Department Nurses ◽  
Participation In Decision Making

ABSTRACT Objective Preliminary investigation of the way Greek critical and emergency department nurses conceptualize changes in their professional role. Method A qualitative focus-group methodology was applied. Following purposeful sampling and informed consent of participants. Results Participated eight individuals. The need for enhancement of nurses’ participation in decision-making in order for an actual change in their professional role to be achieved was the central theme of participants’ narratives. Perceived advancements in professional role performance regarded: evidence-based practice; technology; education, knowledge; clinical skills; research; heightened nurse-physician collaboration. Perceived reasons why these advancements failed to enhance nurses’ professional role were lack of meritocracy; competitive relationships; lack of support among nurses; insufficient managerial support; budget limitations. Conclusion Despite advancements in clinical practice, participants did not deem that their professional role was enhanced significantly, as participation in decision-making and control over practice remain limited. Interventions targeted to enhance nurses’ participation in clinical decision-making, and overall professional autonomy are recommended.

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