Characteristics of the study population.

<p>EDs were grouped based on their mean occupancy rate. <i>*</i></p><p><i>* ED</i>: <i>emergency department</i></p><p><i>**SD</i>: <i>standard deviation</i></p><p>Characteristics of the study population.</p

Median occupancy rate of emergency departments in Seoul.

<p>Median occupancy rate of emergency departments in Seoul.</p

Linear regression analysis with a multi-level regression model.

<p>Covariates were distance between ED and ambulance base, age, gender, mentality, trauma, and overcrowding group.</p><p><i>*95% CI</i>: <i>95% confidence interval</i></p><p>Linear regression analysis with a multi-level regression model.</p

Subgroup analysis of EDs with occupancy rate > 1.0.

<p>Linear regression analysis with a multi-level regression model. Covariates were distance between ED and ambulance base, age, gender, mentality, trauma, and overcrowding group.</p><p><i>*95% CI</i>: <i>95% confidence interval</i></p><p>Subgroup analysis of EDs with occupancy rate > 1.0.</p

Machine Learning Analysis to Identify Data Entry Errors in Prehospital Patient Care Reports: A Case Study of a National Out-of-Hospital Cardiac Arrest Registry

Background: The objective of this study was to develop and validate machine learning models for data entry error detection in a national out-of-hospital cardiac arrest (OHCA) prehospital patient care report database. Methods: Adult OHCAs of presumed cardiac etiology were included. Data entry errors were defined as discrepancies between the coded data and the free-text note documenting the intervention or event; for example, information that was recorded as “absent” in the coded data but “present” in the free-text note. Machine learning models using the extreme gradient boosting, logistic regression, extreme gradient boosting outlier detection, and K-nearest neighbor outlier detection algorithms for error detection within nine core variables were developed and then validated for each variable. Results: Among 12,100 OHCAs, the proportion of cases with at least one error type was 16.2%. The area under the receiver operating characteristic curve (AUC) of the best-performing model (model with the highest AUC for each outcome variable) was 0.71–0.95. Machine learning models detected errors most efficiently for outcome place and initial rhythm errors; 82.6% of place errors and 93.8% of initial rhythm errors could be detected while checking 11 and 35% of data, respectively, compared to the strategy of checking all data. Conclusion: Machine learning models can detect data entry errors in care reports of emergency medical services (EMS) clinicians with acceptable performance and likely can improve the efficiency of the process of data quality control. EMS organizations that provide more prehospital interventions for OHCA patients could have higher error rates and may benefit from the adoption of error-detection models.</p

Effect of a first responder on survival outcomes after out-of-hospital cardiac arrest occurs during a period of exercise in a public place

<div><p>Introduction</p><p>The deployment of first responders in a public place is one of the interventions that is used for increasing bystander cardiopulmonary resuscitation (CPR) of out-of-hospital cardiac arrests (OHCA). We studied the association between the presence of a first responder and the survival of OHCA that occurred during a period of exercise in a public place.</p><p>Methods</p><p>All of the adult OHCAs of a presumed cardiac etiology that occurred during a period of exercise in a public place and that were witnessed by a bystander between 2013 and 2015 were analyzed. The main exposure of interest was the characteristics of the bystander (first responder vs. layperson). The endpoints were the provision of bystander CPR and good neurological recovery. Multivariable logistic regression analysis, adjusting for patient-environment and prehospital factors, was performed.</p><p>Results</p><p>A total of 870 patients had a cardiac arrest during a period of exercise in a public place, and 58 (6.7%) patients were witnessed by the first responder. The OHCAs witnessed by first responders were more likely to result in bystander CPR than those witnessed by laypersons (89.7% vs. 75.4%, <i>p</i> = 0.01, adjusted OR (95% CI): 3.51 (1.44–8.55)). In terms of good neurological recovery, the OHCAs witnessed by first responders had a higher likelihood than the patients witnessed by laypersons (37.9% vs, 24.0%, <i>p</i> = 0.02, adjusted OR (95% CI): 2.92 (1.33–6.40)).</p><p>Conclusion</p><p>The OHCAs occurred during a period of exercise in a public place and whom first responders witnessed were more likely to receive bystander CPR and to have a neurologically intact survival.</p></div

Study population.

<p>EMS: emergency medical service; OHCA: out-of-hospital cardiac arrest; CPR: cardiopulmonary resuscitation.</p

Associations between bystanders and study outcomes for study population.

<p>Associations between bystanders and study outcomes for study population.</p

Patient enrollment flowchart.

<p>Patient enrollment flowchart.</p

Prior knowledge and attitude toward speech and video recognition technology.

Prior knowledge and attitude toward speech and video recognition technology.</p