E-Triage Systems for COVID-19 Outbreak: Review and Recommendations

AI triage or manual triage? Exploring medical staffs' preference for AI triage in China

OBJECTIVES

The introduction of AI technology in healthcare presents both opportunities and challenges. The aim of this study was to investigate medical staffs' preference for AI triage and the influencing factors.

METHODS

A survey was conducted online among medical staffs in China from March 4th to April 28th, 2021. Participants were recruited through multiple channels, including medical professional platforms and social media. A total of 677 valid responses were obtained from medical staff members located in 28 provinces across China.

RESULTS

The results showed that AI triage had an overall acceptance rate of 77.1%, and 45.2% of the medical staffs surveyed preferred "AI triage exclusively." Direct experience was positively associated with medical staffs' preference for AI triage (β = 0.223, p < .001). Additionally, greater exposure to a variety of media was positively associated with the perceived value of AI technology, which, in turn, increased preference for AI triage (β = 0.040, SE = 0.013, p < .001, 95% CI = [0.017, 0.067]).

CONCLUSION

Medical staffs generally hold a favorable attitude towards AI triage, particularly in areas with a high medical burden and during pandemics. In a multimedia environment, media exposure variety impacts medical staffs' preferences through their perceived value of AI technology. This study has implications for the implementation of AI triage on a larger scale.

Characteristics and Admission Preferences of Pediatric Emergency Patients and Their Waiting Time Prediction Using Electronic Medical Record Data: Retrospective Comparative Analysis

BACKGROUND

The growing number of patients visiting pediatric emergency departments could have a detrimental impact on the care provided to children who are triaged as needing urgent attention. Therefore, it has become essential to continuously monitor and analyze the admissions and waiting times of pediatric emergency patients. Despite the significant challenge posed by the shortage of pediatric medical resources in China's health care system, there have been few large-scale studies conducted to analyze visits to the pediatric emergency room.

OBJECTIVE

This study seeks to examine the characteristics and admission patterns of patients in the pediatric emergency department using electronic medical record (EMR) data. Additionally, it aims to develop and assess machine learning models for predicting waiting times for pediatric emergency department visits.

METHODS

This retrospective analysis involved patients who were admitted to the emergency department of Children's Hospital Capital Institute of Pediatrics from January 1, 2021, to December 31, 2021. Clinical data from these admissions were extracted from the electronic medical records, encompassing various variables of interest such as patient demographics, clinical diagnoses, and time stamps of clinical visits. These indicators were collected and compared. Furthermore, we developed and evaluated several computational models for predicting waiting times.

RESULTS

In total, 183,024 eligible admissions from 127,368 pediatric patients were included. During the 12-month study period, pediatric emergency department visits were most frequent among children aged less than 5 years, accounting for 71.26% (130,423/183,024) of the total visits. Additionally, there was a higher proportion of male patients (104,147/183,024, 56.90%) compared with female patients (78,877/183,024, 43.10%). Fever (50,715/183,024, 27.71%), respiratory infection (43,269/183,024, 23.64%), celialgia (9560/183,024, 5.22%), and emesis (6898/183,024, 3.77%) were the leading causes of pediatric emergency room visits. The average daily number of admissions was 501.44, and 18.76% (34,339/183,204) of pediatric emergency department visits resulted in discharge without a prescription or further tests. The median waiting time from registration to seeing a doctor was 27.53 minutes. Prolonged waiting times were observed from April to July, coinciding with an increased number of arrivals, primarily for respiratory diseases. In terms of waiting time prediction, machine learning models, specifically random forest, LightGBM, and XGBoost, outperformed regression methods. On average, these models reduced the root-mean-square error by approximately 17.73% (8.951/50.481) and increased the R2 by approximately 29.33% (0.154/0.525). The SHAP method analysis highlighted that the features "wait.green" and "department" had the most significant influence on waiting times.

CONCLUSIONS

This study offers a contemporary exploration of pediatric emergency room visits, revealing significant variations in admission rates across different periods and uncovering certain admission patterns. The machine learning models, particularly ensemble methods, delivered more dependable waiting time predictions. Patient volume awaiting consultation or treatment and the triage status emerged as crucial factors contributing to prolonged waiting times. Therefore, strategies such as patient diversion to alleviate congestion in emergency departments and optimizing triage systems to reduce average waiting times remain effective approaches to enhance the quality of pediatric health care services in China.

Craniofacial Pain Management in Severe COVID-19 Patients During the Pandemic Peak in Kosovo: A Comprehensive Approach

Background This cross-sectional study aims to evaluate pain management's success in limiting admissions and assesses remote care's effectiveness for optimal pain relief. By utilizing data from severe COVID-19 inpatients in Prishtina, Kosovo, this study offers insights into the challenges posed by the pandemic and innovative care approaches aimed at improving patient well-being. Methodology This cross-sectional study includes 55 patients with severe COVID-19 after hospital discharge. All study participants completed the questionnaire in the presence of a clinical pharmacologist. The questionnaire of this study consisted of three parts: sociodemographic data (first part), the Intensity and Characteristics of Toothache (IaCofT) and headache (second part), and pharmacological treatment of headache and dental pain (third part). The questionnaire on IaCofT and headache was created with some modifications of the Modified Dental Pain Screening Questionnaire (M-DePaQ). Descriptive statistics were conducted using Prism version 10.0.1 (Windows and Mac). Results According to the study data, 89.1% (n = 49) of the participants experienced pain during hospitalization with COVID-19, while 72.72% (n = 40) of them experienced pain after hospital discharge. Of the participants, 32.7% (n = 18) experienced dental pain, whereas 60% (n = 33) reported having headaches. Regarding the pain scale, more than two-thirds (n = 40, 72.72%) of the participants had moderate to moderately severe pain, and in 70.9% (n = 39) of the cases, the pain occurred episodically. The absolute majority (n = 53, 96.4%) of study participants reported the use of analgesics for pain management. Paracetamol (n = 46, 83.6%) and ibuprofen (n = 14, 25.5%) were the most commonly used analgesics for pain management. Conclusions This study highlighted the prevalence of headache and acute dental pain in these patients. The majority of the study participants were convinced by the healthcare system and were highly dependent on pharmacological treatment for headaches and acute toothache during the COVID-19 peak. The study results showed that the pain was proven to be successfully treated pharmacologically with analgesics such as paracetamol, ibuprofen, and diclofenac. Telemedicine is expected to become an important healthcare practice in the post-COVID-19 era. Therefore, the introduction of this service could be considered.

The effect of computer-based decision support system on emergency department triage: Non-randomised controlled trial

BACKGROUND

Deciding on triage in emergency departments is difficult and requires comprehensive knowledge and experience.

PURPOSE

This study was conducted to evaluate the effect of a "computer-based emergency department triage decision support system (DSS)," which was designed and integrated into the hospital information management system, on triage decision accuracy and triage duration by using real patient data.

METHODS

Single-group, pretest-posttest non-randomised clinical trial. The study was conducted with the real data of patients who had been triaged in the adult emergency department of a university hospital. The pretest was applied between July 16 and September 16, 2019, and the post-test on September 1 and October 31, 2020. In the pre-test and post-test phases of the study, triage decision accuracy rates, and triage duration were evaluated. In the post-test phase, Emergency Triage Decision Support System (ETDSS) was prepared with a rule-based decision trees method using the Emergency Severity Index Version 4 and The Australasian Triage Scale and was integrated into the hospital information management system. The effect of the developed ETDSS was evaluated. The mean, standard deviation, frequency and percentage values were calculated for the descriptive characteristics. Independent samples t-test, analysis of variance, Sidak paired comparison, and Bonferroni tests were applied.

RESULTS

The effect of the computer-based emergency triage DSS on triage management was tested based on the data of 16,409 patients in the pretest phase and 7,765 patients in the posttest phase. While the accuracy rate of nurses' triage decisions was 57.8% in the pretest, it was found to increase to 64.9% in the posttest. The mean duration of triage was 1.47 ± 0.72 in the pretest and 1.79 ± 0.85 min in the posttest.

CONCLUSIONS

The DSS increased triage decision accuracy independently of professional and triage experience and brought the triage duration closer to the time recommended in the literature. Clinically, this is associated with patient safety, quality improvement processes, and professional accountability.

Oral adverse events following COVID-19 and influenza vaccination in Australia

Vaccine hesitancy, spurred by misinterpretation of Adverse Events (AEs), threatens public health. Despite sporadic reports of oral AEs post-COVID-19 vaccination, systematic analysis is scarce. This study evaluates these AEs using the Australian Database of Adverse Event Notifications (DAEN). A secondary analysis of DAEN data was conducted, with the analysis period commencing from the start of the COVID-19 vaccination rollout in February 2021 and the inception of the influenza vaccine database in 1971, both through until December 2022. The focus of the analysis was on oral AEs related to COVID-19 and influenza vaccines. Reports were extracted according to a predefined schema and then stratified by vaccine type, sex, and age. Oral paresthesia was the most common oral AE after COVID-19 vaccination (75.28 per 10,000 reports), followed by dysgeusia (73.96), swollen tongue (51.55), lip swelling (49.43), taste disorder (27.32), ageusia (25.85), dry mouth (24.75), mouth ulceration (18.97), oral hypoaesthesia (15.60), and oral herpes (12.74). While COVID-19 and influenza vaccines shared most oral AEs, taste-related AEs, dry mouth, and oral herpes were significantly more common after COVID-19 vaccination. mRNA vaccines yielded more oral AEs than other types. Females had higher oral AE incidence. Most oral AEs did not differ significantly between COVID-19 and influenza vaccination. However, specific oral AEs, particularly taste-related, dry mouth, and oral herpes, were more prevalent after COVID-19 vaccination compared with seasonal influenza, especially in females and mRNA vaccine recipients.

Oral side effects of COVID-19 vaccines in 32 European countries: Analysis of EudraVigilance reports

The recent reports of oral side effects (SEs) following COVID-19 vaccination warrant further investigation into their prevalence, severity, and aetiology. This study was conducted to synthesize the first-ever population-level evidence about oral SEs of COVID-19 vaccines in Europe. The European Union Drug Regulating Authorities Pharmacovigilance (EudraVigilance) database was accessed in August 2022 to extract summary data of all potential oral SEs reported after COVID-19 vaccination. The data were reported descriptively and cross-tabulated to facilitate sub-group analysis per vaccine type, sex, and age group. Dysgeusia was the most commonly reported oral SE (0.381 case per each 100 received reports), followed by oral paraesthesia (0.315%), ageusia (0.296%), lip swelling (0.243%), dry mouth (0.215%), oral hypoaesthesia (0.210%), swollen tongue (0.207%), and taste disorder (0.173%). Females had significantly (Sig. < 0.001) a higher prevalence of all most common (top 20) oral SEs, except for salivary hypersecretion, which was equally prevalent among females and males. The present study revealed a low prevalence of oral SEs, with taste-related, other sensory and anaphylactic SEs being the most common SEs in Europe, similar to what was found earlier among the US population. Future studies should explore the potential risk factors of oral sensory and anaphylactic SEs to verify whether they are causally linked to COVID-19 vaccines.

A Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak

The use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was ER facilities (14.4%), while Procedures and protocols evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network.

Operational Status of Isolation Rooms in Emergency Departments and Patient Concentration in Higher-Level Emergency Departments in Daegu Metropolitan City and Neighboring Provinces, South Korea, during the COVID-19 Pandemic

BACKGROUND

In a pandemic situation such as the one of the COVID-19 pandemic, nosocomial transmissions attempted to be prevented by initially classifying them in triage. Therefore, emergency departments (EDs) installed isolation rooms at their entrance. Additionally, a system for pre-emptive quarantine at the triage stage was established nationwide for patients with COVID-19-related symptoms.

METHODS

Data were retrospectively collected from 28,609 patients who visited the regional emergency medical center of Yeungnam University Hospital in Daegu Metropolitan City in 2021. The study population was divided into experimental and control groups comprising patients with and without COVID-19-related symptoms, respectively. The difference in the percentage of patients visiting from outside the city was investigated between the two groups. The critically ill patient (CP) ratio was analyzed in the experimental group to verify the appropriateness of visiting a higher-level ED and was further divided into sub-regions to determine their reason for visiting an ED beyond their residential region.

RESULTS

Most lower-level EDs did not have isolation rooms. About 20.1% and 17.3% of patients in the experimental and control groups visited a higher-level ED with an isolation room beyond their residential region, respectively. The absence of an isolation room in the ED in their residential region was one reason for traveling beyond their residential region, with an odds ratio of 4.44 (95% confidence interval: 0.53-8.35).

CONCLUSION

In the process of implementing the "pre-emptive quarantine" system, it was revealed that the cooperation of the lower-level EDs was not effective during the implementation of the "pre-emptive quarantine" system. Consequently, a higher number of patients with COVID-19-related symptoms had to locate an ED with an isolation room and travel a longer distance than general patients. The participation of more EDs is required.

The Pragmatic Return to Effective Dental Infection Control Through Triage and Testing (PREDICT) Study: Protocol for a Prospective Clinical Study in the National Dental Practice-Based Research Network

BACKGROUND

Dental practice has been greatly affected by the COVID-19 pandemic. As SARS-CoV-2 infection is transmitted by respiratory fluids, dental practice techniques, which include aerosol-generating procedures, can increase the risk of transmission causing heightened safety concerns for both dental health care workers (DHCWs) and patients. These concerns have resulted in the reduction in patient volume and the available workforce within dental practices across the United States. Standardized methods for COVID-19 triage and testing may lead to increased safety and perceptions of safety for DHCWs and their patients and promote willingness to provide and access oral health care services.

OBJECTIVE

This study is designed to develop procedures that test the feasibility of enhanced COVID-19 triage and testing in dental offices. It will provide preliminary data to support a larger network-wide study grant application aimed at developing protocols to address safety concerns of patients and DHCWs in a peri-COVID-19 pandemic era.

METHODS

The feasibility study is being conducted in 4 private dental practices, each of which has a dentist member of the National Dental Practice-Based Research Network. Participants include the DHCWs and patients of the dental practice. Study procedures include completion of COVID-19 triage, completion of COVID-19 testing (point-of-care [POC] or laboratory-based [LAB] SARS-CoV-2 viral, antigen, and antibody tests based on office designation), and administration of perception and attitude surveys for participating DCHWs and patients of the dental practice over a defined study period. The office designation and the participant's role in the practice determines which testing protocol is executed within the office. There are 4 study groups following 4 distinct protocols: (1) POC DHCWs, (2) POC patients, (3) LAB DHCWs, and (4) LAB patients.

RESULTS

Data collection began in December of 2021 and concluded in March 2022. Study results are expected to be published in fall 2022.

CONCLUSIONS

The results of this feasibility study will help identify the viability and functionality of COVID-19 triage and testing in dental practices and inform a larger network-wide study grant application that develops protocols that address safety concerns of patients and DHCWs in a COVID-19 environment.

TRIAL REGISTRATION

ClinicalTrials.gov NTC05123742; https://clinicaltrials.gov/ct2/show/NCT05123742?term=NCT05123742.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/38386.

Oral Adverse Events Following COVID-19 Vaccination: Analysis of VAERS Reports

Background

Oral adverse events (AEs) following COVID-19 vaccination have been sporadically reported during the previous months, warranting further investigation for their prevalence and suspected relationship with vaccine-elicited immune response.

Methods

A retrospective analysis using the Vaccine Adverse Event Reporting System (VAERS) data was conducted to evaluate AEs within the oral cavity (mucosa, tongue, lips, palate, dentition, salivary glands) and AEs involving taste and other sensations. Oral AEs reported after receiving COVID-19 vaccination (test group) and seasonal influenza vaccination (control group) were extracted and cross-tabulated to assess their relative prevalence.

Results

Among the 128 solicited (suspected) oral AEs, oral paresthesia (0.872%) was most reported after receiving COVID-19 vaccines, followed by the swelling of lips (0.844%), ageusia (0.722%), oral hypoesthesia (0.648%), swollen tongue (0.628%), and dysgeusia (0.617%). The reported prevalence of oral AEs was higher in the COVID-19 vaccine group than in the seasonal influenza group. The distribution pattern of the most reported oral AEs was similar for both COVID-19 and seasonal influenza vaccines. Female sex, older age (>39 years old), primer doses, and mRNA-based COVID-19 vaccines exhibited a higher reported prevalence of oral AEs.

Conclusion

Within the limitations of this study, COVID-19 vaccines were found to be associated with rare oral AEs that are predominantly similar to those emerging following seasonal influenza vaccines. The most commonly reported oral AEs were oral paraesthesia (mouth-tingling), lip swelling, and ageusia, representing various pathophysiologic pathways that remain unclear. Taste-related AEs should be acknowledged in the context of the COVID-19 pandemic and the public should be adequately informed about a potential taste dysfunction after receiving the COVID-19 vaccination. Dentists and dental teams need to be aware of the prevalence, severity, and prognosis of oral AEs to inform their patients and increase public confidence in vaccines.

Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities

Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.