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- 저자 : ( Marcus Eng Hock Ong ) (검색결과 5 건)
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오영석,안기옥,신상도,켄타로 카지노,타츠야 니시우치,Matthew Ma6,,Patrick Ko,Marcus Eng Hock Ong, M.D.,Ng Yih Yng,Benjamin Leong 대한응급의학회 2020 Clinical and Experimental Emergency Medicine Vol.7 No.2
Objective To investigate variations in the effects of prehospital advanced airway management (AAM) on outcomes of out-of-hospital cardiac arrest (OHCA) patients according to regional emergency medical service (EMS) systems in four Asian cities. Methods We enrolled adult patients with EMS-treated OHCA of presumed cardiac origin between 2012 and 2014 from Osaka (Japan), Seoul (Republic of Korea), Singapore (Singapore), and Taipei (Taiwan). The main exposure variable was prehospital AAM. The primary endpoint was neurological recovery. We compared outcomes between the prehospital AAM and non-AAM groups using multivariable logistic regression with an interaction term between prehospital AAM and the four Asian cities. Results A total of 16,510 patients were included in the final analyses. The rates of prehospital AAM varied among Osaka, Seoul, Singapore, and Taipei (65.0%, 19.2%, 84.9%, and 34.1%, respectively). The non-AAM group showed better outcomes than the AAM group (adjusted odds ratio [aOR] for neurological recovery 0.30; 95% confidence interval [CI], 0.24–0.38]). In the interaction model for neurological recovery, the aORs for AAM in Osaka and Singapore were 0.12 (95% CI, 0.06–0.26) and 0.21 (95% CI, 0.16–0.28), respectively. In Seoul and Taipei, the association between prehospital AAM and neurological recovery was not significant (aOR 0.58 [95% CI, 0.31–1.10] and 0.79 [95% CI, 0.52–1.20], respectively). The interaction between prehospital AAM and region was significant (P=0.01). Conclusion The effects of prehospital AAM on outcomes of OHCA patients differed according to regional variability in the EMS systems.
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Effect of the Time-related Overcrowding Factors on the Ambulance Diversion
조진성,차원철,송경준,신상도,Marcus Eng Hock Ong 대한응급의학회 2010 大韓應急醫學會誌 Vol.21 No.1
Purpose: We evaluated the influence of time-related input,throughput, and output factors on ambulance diversions in an urban emergency department (ED). Methods: Data was prospectively collected in an urban ED for one year. We measured daily input factors (daily number of visit, etc), throughput factors (ED length of stay), and output factors (occupancy rate of adult ward, etc). The duty emergency physician had the authority to declare an ambulance diversion. There was no written protocol for ambulance diversion, and each diversion lasted 4 hours if not extended. We estimated the effect of the potential factors of the same day and the previous day on ambulance diversion with a multivariates logistic regression analysis excluding variables with collinearity Results: The total annual number of visits was 40,863. The number of patients delivered by ambulance was 4,059(9.9%). Ambulance diversion occurred 29 times during 365twenty-four hour observation intervals (7.9%). The multivariates logistic regression analyses revealed three significant independent factors of ambulance diversion: the ward occupancy rate of the previous day (odds ratio [OR], 1.278; 95%confidence interval [CI], 1.039-1.573), the elderly proportion for the day (OR, 1.106; 95% CI, 1.005-1.217), the total number of visits of the day (OR, 1.079; 95% CI, 1.039-1.120). Conclusion: Daily number of visits, proportion of elderly, and ward occupancy rate of the previous day were found to be factors related with ambulance diversion, with the hospital occupancy rate of the previous day showing the highest OR.
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Explainable artificial intelligence in emergency medicine: an overview
Okada Yohei,Ning Yilin,Ong Marcus Eng Hock 대한응급의학회 2023 Clinical and Experimental Emergency Medicine Vol.10 No.4
Artificial intelligence (AI) and machine learning (ML) have potential to revolutionize emergency medical care by enhancing triage systems, improving diagnostic accuracy, refining prognostication, and optimizing various aspects of clinical care. However, as clinicians often lack AI expertise, they might perceive AI as a “black box,” leading to trust issues. To address this, “explainable AI,” which teaches AI functionalities to end-users, is important. This review presents the definitions, importance, and role of explainable AI, as well as potential challenges in emergency medicine. First, we introduce the terms explainability, interpretability, and transparency of AI models. These terms sound similar but have different roles in discussion of AI. Second, we indicate that explainable AI is required in clinical settings for reasons of justification, control, improvement, and discovery and provide examples. Third, we describe three major categories of explainability: pre-modeling explainability, interpretable models, and post-modeling explainability and present examples (especially for post-modeling explainability), such as visualization, simplification, text justification, and feature relevance. Last, we show the challenges of implementing AI and ML models in clinical settings and highlight the importance of collaboration between clinicians, developers, and researchers. This paper summarizes the concept of “explainable AI” for emergency medicine clinicians. This review may help clinicians understand explainable AI in emergency contexts.
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Risk Stratification-based Surveillance of Bacterial Contamination in Metropolitan Ambulances
노현,신상도,김남중,노영선,오향순,주세익,김정인,Marcus Eng Hock Ong, M.D. 대한의학회 2011 Journal of Korean medical science Vol.26 No.1
We aimed to know the risk-stratification-based prevalence of bacterial contamination of ambulance vehicle surfaces, equipment, and materials. This study was performed in a metropolitan area with fire-based single-tiered Basic Life Support ambulances. Total 13 out of 117 ambulances (11.1%) were sampled and 33 sites per each ambulance were sampled using a soft rayon swab and aseptic containers. These samples were then plated onto a screening media of blood agar and MacConkey agar. Specific identification with antibiotic susceptibility was performed. We categorized sampling sites into risk stratification-based groups (Critical, Semi-critical, and Non-critical equipment) related to the likelihood of direct contact with patients’ mucosa. Total 214 of 429 samples showed positive results (49.9%) for any bacteria. Four of these were pathogenic (0.9%) (MRSA, MRCoNS, and K. pneumoniae), and 210 of these were environmental flora (49.0%). However, the prevalence (positive/number of sample) of bacterial contamination in critical, semi-critical airway, semi-critical breathing apparatus group was as high as 15.4% (4/26), 30.7%(16/52), and 46.2% (48/104), respectively. Despite current formal guidelines, critical and semi-critical equipments were contaminated with pathogens and normal flora. This study suggests the need for strict infection control and prevention for ambulance services.
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