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Fulltext Effectiveness of teaching automated external defibrillators use using a traditional classroom instruction versus self-instruction video in non-critical care nurses

Saiboon IM, Qamruddin RM, Jaafar JM, Bakar AA, Hamzah FA, Eng HS, et al.

Saudi Med J, 2016 Apr;37(4):429-35.
PMID: 27052286 DOI: 10.15537/smj.2016.4.14833

To evaluate the effectiveness and retention of learning automated external defibrillator (AED) usage taught through a traditional classroom instruction (TCI) method versus a novel self instructed video (SIV) technique in non-critical care nurses (NCCN).
Fulltext Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

Albadr MAA, Tiun S, Ayob M, Al-Dhief FT, Omar K, Hamzah FA

PLoS One, 2020;15(12):e0242899.
PMID: 33320858 DOI: 10.1371/journal.pone.0242899

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.
Improving door-to-doctor and door-to-analgesia time in a tertiary teaching hospital emergency department through a novel 'Red Box' system

Saiboon IM, Singmamae N, Jaafar MJ, Muniandy BK, Sengmamae K, Hamzah FA, et al.

Saudi Med J, 2014 Jul;35(7):718-23.
PMID: 25028229

To evaluate the effectiveness of a new patient flow system, `The Red Box` on the quality of patient care in respect of the time taken for the care to be delivered to the patient.
Knowledge and Confidence Level Among Emergency Healthcare Workers in Airway Management and Resuscitation of Suspected COVID-19 Patients: A Cross Sectional Study in Malaysia

Abd Samat AH, Isa MH, Sabardin DM, Jamal SM, Jaafar MJ, Hamzah FA, et al.

Ann Acad Med Singap, 2020 Sep;49(9):643-651.
PMID: 33241252

INTRODUCTION: This study aims to evaluate the knowledge and confidence of emergency healthcare workers (EHCW) in facing the COVID-19 pandemic.
MATERIALS AND METHODS: A cross-sectional online study using a validated questionnaire was distributed to doctors (MD), assistant medical officers (AMO), and staff nurses (SN) at an urban tertiary Emergency Department. It comprised of 40 knowledge and 10 confidence-level questions related to resuscitation and airway management steps.
RESULTS: A total of 135 from 167 eligible EHCW were enrolled. 68.9% (n = 93) had high knowledge while 53.3% (n = 72) possessed high confidence level. Overall knowledge mean score was 32.96/40 (SD = 3.63) between MD (33.88±3.09), AMO (32.28±4.03), and SN (32.00±3.60), P= 0.025. EHCWs with a length of service (LOS) between 4-10 years had the highest knowledge compared to those with LOS <4-year (33.71±3.39 versus 31.21±3.19 P = 0.002). Airway-related knowledge was significantly different between the designations and LOS (P = 0.002 and P = 0.003, respectively). Overall, EHCW confidence level against LOS showed significant difference [F (2, 132) = 5.46, P = 0.005] with longer LOS showing better confidence. MD showed the highest confidence compared to AMO and SN (3.67±0.69, 3.53±0.68, 3.26±0.64) P = 0.049. The majority EHCW were confident in performing high-quality chest-compression, and handling of Personal Protective Equipment but less than half were confident in resuscitating, leading the resuscitation, managing the airway or being successful in first intubation attempt.
CONCLUSIONS: EHCW possessed good knowledge in airway and resuscitation of COVID-19 patients, but differed between designations and LOS. A longer LOS was associated with better confidence, but there were some aspects in airway management and resuscitation that needed improvement.