Triage nurses' clinical decision making. An observational study of urgency assessment

2001 ◽  
Vol 35 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Marie F. Gerdtz ◽  
Tracey K. Bucknall
Keyword(s):  
Decision Making ◽  
Observational Study ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals Routine Follow-Up Radiographs for Ankle Fractures Seldom Add Value to Clinical Decision-Making: A Retrospective, Observational Study

2018 ◽  
Vol 57 (5) ◽  
pp. 957-960 ◽  
Author(s):  
Pieter van Gerven ◽  
Nikki L. Weil ◽  
Marco F. Termaat ◽  
Sidney M. Rubinstein ◽  
Mostafa El Moumni ◽  
...  
Keyword(s):  
Decision Making ◽  
Observational Study ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Ankle Fractures ◽  
Retrospective Observational Study

scholarly journals Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study

10.2196/19878 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e19878
Author(s):  
Ping-Yen Liu ◽  
Yi-Shan Tsai ◽  
Po-Lin Chen ◽  
Huey-Pin Tsai ◽  
Ling-Wei Hsu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Observational Study ◽  
Processing Time ◽  
Clinical Decision Making ◽  
Image Interpretation ◽  
Clinical Decision ◽  
Tablet Computer ◽  
X Rays ◽  
Quarantine Station

Background As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties. Objective The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times. Methods This observational study on the emerging COVID-19 pandemic included 643 patients. An “AI trilogy” of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shorten the quarantine survey process and reduce processing time during the COVID-19 pandemic. Results The use of the AI trilogy facilitated the processing of suspected cases of COVID-19 with or without symptoms; also, travel, occupation, contact, and clustering histories were obtained with the tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest x-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source data set. The detection rates for posteroanterior and anteroposterior chest x-rays were 55/59 (93%) and 5/11 (45%), respectively. The SCAS algorithm was continuously adjusted based on updates to the Taiwan Centers for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of disinfecting the tablet computer surface by wiping it twice with 75% alcohol sanitizer. To further analyze the impact of the AI application in the quarantine station, we subdivided the station group into groups with or without AI. Compared with the conventional ED (n=281), the survey time at the quarantine station (n=1520) was significantly shortened; the median survey time at the ED was 153 minutes (95% CI 108.5-205.0), vs 35 minutes at the quarantine station (95% CI 24-56; P<.001). Furthermore, the use of the AI application in the quarantine station reduced the survey time in the quarantine station; the median survey time without AI was 101 minutes (95% CI 40-153), vs 34 minutes (95% CI 24-53) with AI in the quarantine station (P<.001). Conclusions The AI trilogy improved our medical care workflow by shortening the quarantine survey process and reducing the processing time, which is especially important during an emerging infectious disease epidemic.

scholarly journals Ethnic bias and clinical decision-making among New Zealand medical students: an observational study

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Ricci Harris ◽  
Donna Cormack ◽  
James Stanley ◽  
Elana Curtis ◽  
Rhys Jones ◽  
...  
Keyword(s):  
Decision Making ◽  
New Zealand ◽  
Medical Students ◽  
Observational Study ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Ethnic Bias

scholarly journals Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study (Preprint)

2020 ◽  
Author(s):  
Ping-Yen Liu ◽  
Yi-Shan Tsai ◽  
Po-Lin Chen ◽  
Huey-Pin Tsai ◽  
Ling-Wei Hsu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Observational Study ◽  
Processing Time ◽  
Clinical Decision Making ◽  
Image Interpretation ◽  
Clinical Decision ◽  
Tablet Computer ◽  
X Rays ◽  
Quarantine Station

BACKGROUND As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties. OBJECTIVE The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times. METHODS This observational study on the emerging COVID-19 pandemic included 643 patients. An “AI trilogy” of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shorten the quarantine survey process and reduce processing time during the COVID-19 pandemic. RESULTS The use of the AI trilogy facilitated the processing of suspected cases of COVID-19 with or without symptoms; also, travel, occupation, contact, and clustering histories were obtained with the tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest x-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source data set. The detection rates for posteroanterior and anteroposterior chest x-rays were 55/59 (93%) and 5/11 (45%), respectively. The SCAS algorithm was continuously adjusted based on updates to the Taiwan Centers for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of disinfecting the tablet computer surface by wiping it twice with 75% alcohol sanitizer. To further analyze the impact of the AI application in the quarantine station, we subdivided the station group into groups with or without AI. Compared with the conventional ED (n=281), the survey time at the quarantine station (n=1520) was significantly shortened; the median survey time at the ED was 153 minutes (95% CI 108.5-205.0), vs 35 minutes at the quarantine station (95% CI 24-56; <i>P</i>&lt;.001). Furthermore, the use of the AI application in the quarantine station reduced the survey time in the quarantine station; the median survey time without AI was 101 minutes (95% CI 40-153), vs 34 minutes (95% CI 24-53) with AI in the quarantine station (<i>P</i>&lt;.001). CONCLUSIONS The AI trilogy improved our medical care workflow by shortening the quarantine survey process and reducing the processing time, which is especially important during an emerging infectious disease epidemic.

scholarly journals Multisite Exploration of Clinical Decision Making for Antibiotic Use by Emergency Medicine Providers Using Quantitative and Qualitative Methods

2014 ◽  
Vol 35 (9) ◽  
pp. 1114-1125 ◽  
Author(s):  
Larissa May ◽  
Glencora Gudger ◽  
Paige Armstrong ◽  
Gillian Brooks ◽  
Pamela Hinds ◽  
...  
Keyword(s):  
Decision Making ◽  
Observational Study ◽  
Clinical Decision Making ◽  
A Priori ◽  
Clinical Decision ◽  
Antibiotic Use ◽  
Knowledge Generation ◽  
Clinical Inertia ◽  
Antibiotic Prescribing ◽  
Individual Interviews

Objectives.To explore current practices and decision making regarding antimicrobial prescribing among emergency department (ED) clinical providers.MethodsWe conducted a survey of ED providers recruited from 8 sites in 3 cities. Using purposeful sampling, we then recruited 21 providers for in-depth interviews. Additionally, we observed 10 patient-provider interactions at one of the ED sites. SAS 9.3 was used for descriptive and predictive statistics. Interviews were audio recorded, transcribed, and analyzed using a thematic, constructivist approach with consensus coding using NVivo 10.0. Field and interview notes collected during the observational study were aligned with themes identified through individual interviews.ResultsOf 150 survey respondents, 76% agreed or strongly agreed that antibiotics are overused in the ED, while half believed they personally did not overprescribe. Eighty-nine percent used a smartphone or tablet in the ED for antibiotic prescribing decisions. Several significant differences were found between attending and resident physicians. Interview analysis identified 42 codes aggregated into the following themes: (1) resource and environmental factors that affect care; (2) access to and quality of care received outside of the ED consult; (3) patient-provider relationships; (4) clinical inertia; and (5) local knowledge generation. The observational study revealed limited patient understanding of antibiotic use. Providers relied heavily upon diagnostics and provided limited education to patients. Most patients denied a priori expectations of being prescribed antibiotics.ConclusionsPatient, provider, and healthcare system factors should be considered when designing interventions to improve antimicrobial stewardship in the ED setting.Infect Control Hosp Epidemiol 2014;35(9):1114-1125

Debates in Dysphagia Management: How Do You Use Evidence-Based Practice in Your Dysphagia Patient Care?

2011 ◽  
Vol 20 (4) ◽  
pp. 121-123
Author(s):  
Jeri A. Logemann
Keyword(s):  
Decision Making ◽  
Patient Care ◽  
Clinical Outcomes ◽  
Clinical Judgment ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Method ◽  
Do So

Evidence-based practice requires astute clinicians to blend our best clinical judgment with the best available external evidence and the patient's own values and expectations. Sometimes, we value one more than another during clinical decision-making, though it is never wise to do so, and sometimes other factors that we are unaware of produce unanticipated clinical outcomes. Sometimes, we feel very strongly about one clinical method or another, and hopefully that belief is founded in evidence. Some beliefs, however, are not founded in evidence. The sound use of evidence is the best way to navigate the debates within our field of practice.

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