Accuracy of the Traditional COVID-19 Phone Triaging System and Phone Triage-Driven Deep Learning Model.

OBJECTIVES: During the COVID-19 pandemic, a quick and reliable phone-triage system is critical for early care and efficient distribution of hospital resources. The study aimed to assess the accuracy of the traditional phone-triage system and phone triage-driven deep learning model in the prediction of positive COVID-19 patients. SETTING: This is a retrospective study conducted at the family medicine department, Cairo University. METHODS: The study included a dataset of 943 suspected COVID-19 patients from the phone triage during the first wave of the pandemic. The accuracy of the phone triaging system was assessed. PCR-dependent and phone triage-driven deep learning model for automated classifications of natural human responses was conducted. RESULTS: Based on the RT-PCR results, we found that myalgia, fever, and contact with a case with respiratory symptoms had the highest sensitivity among the symptoms/ risk factors that were asked during the phone calls (86.3%, 77.5%, and 75.1%, respectively). While immunodeficiency, smoking, and loss of smell or taste had the highest specificity (96.9%, 83.6%, and 74.0%, respectively). The positive predictive value (PPV) of phone triage was 48.4%. The classification accuracy achieved by the deep learning model was 66%, while the PPV was 70.5%. CONCLUSION: Phone triage and deep learning models are feasible and convenient tools for screening COVID-19 patients. Using the deep learning models for symptoms screening will help to provide the proper medical care as early as possible for those at a higher risk of developing severe illness paving the way for a more efficient allocation of the scanty health resources.

Diagnosis of coronavirus disease 2019 and the potential role of deep learning: insights from the experience of Cairo University Hospitals.

OBJECTIVES: Early detection of coronavirus disease 2019 (COVID-19) is crucial for patients and public health to ensure pandemic control. We aimed to correlate clinical and laboratory data of patients with COVID-19 and their polymerase chain reaction (PCR) results and to assess the accuracy of a deep learning model in diagnosing COVID-19. METHODS: This was a retrospective study using an anonymized dataset of patients with suspected COVID-19. Only patients with a complete dataset were included (n = 440). A deep analytics framework and dual-modal approach for PCR-based classification was used, integrating symptoms and laboratory-based modalities. RESULTS: Participants with loss of smell or taste were two times more likely to have positive PCR results (odds ratio [OR] 1.86). Participants with neutropenia, high serum ferritin, or monocytosis were three, four, and five times more likely to have positive PCR results (OR 2.69, 4.18, 5.42, respectively). The rate of accuracy achieved using the deep learning framework was 78%, with sensitivity of 83.9% and specificity of 71.4%. CONCLUSION: Loss of smell or taste, neutropenia, monocytosis, and high serum ferritin should be routinely assessed with suspected COVID-19 infection. The use of deep learning for diagnosis is a promising tool that can be implemented in the primary care setting.

Consensus Meeting Report "Technology Enhanced Assessment" in Covid-19 Time, MENA Regional Experiences and Reflections.

PURPOSE: Majority of the assessments, appraisals and placements have been disturbed, with some being cancelled, postponed, or modified in design. New approaches for assessment should be well-thought-out. This work attempts at capturing the collective wisdom of educators in the Middle East and North Africa region (MENA), providing an understanding of the online assessment conceptual framework in the era of COVID-19 that tells the story rather than determining cause and effect, and identifying the biggest gaps that derail the digital transformation. METHODS: A qualitative inductive study using the grounded theory approach was implemented following a synchronous virtual online meeting, a summary of the reflections as well as experiences of medical education experts was prepared. Data for this qualitative study were collected from the meeting. The meeting was video-recorded and transcribed by the researchers. Thematic analysis was performed by three separate researcher coders. The authors then discussed together until they reached a consensus. RESULTS: Three main thematic areas were identified: 1) feasibility, 2) exam fairness/equity and 3) acceptable graduate attributes, society/community acceptance. CONCLUSION: The COVID-19 era necessitated revisiting of our assessment strategies to cope with new changes within the available context. Rapid adaptation is required.

An Adapted Online Family Medicine Training for House Officers Amid Covid-19 Pandemic in Egypt.

INTRODUCTION: The COVID-19 pandemic is an unprecedented challenge to house officers training programs because of the safety measures. OBJECTIVE: This current study aimed to introduce the adaptation of family medicine training for house officers during COVID-19 pandemic and gauge their level of satisfaction with the training. METHODS: Unfortunately, more than one-fourth of the house officers attending the family medicine training turned out to be hospital-admitted or in obligatory home isolation. A time-sensitive plan was proposed to maintain a competent training guaranteeing safety and support of house officers and fulfilling the training objectives in a virtual setting. Three mentors were assigned to each 10 house officers to provide continuous support and monitoring. Tutor and house officer interaction and reflection were maintained through a virtual clinical training session via Zoom application and a daily online discussion of a clinical scenario. Peer interaction was provided through post-webinar and small-group online discussion sessions. RESULTS: The adapted training was applied on thirteen cohorts of house officers. The response rate was 70% (666 out of 950). Most of them were satisfied with the training (84.6%). Their satisfaction with each modality of the training was encouraging. CONCLUSIONS: During COVID-19 pandemic, successful adaptation of family medicine training has succeeded in fulfilling the training objectives and providing psychological support and engagement for house officers without burdening the hospital-admitted and home-isolated house officers.