Shuk Yin Joey Ho
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Shuk Yin Joey Ho has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Alice Yip
S.K. Yee School of Health Sciences, Saint Francis University, 2 Chui Ling Lane, Tseung Kwan O, New Territories, Hong Kong, China
1 shared publication - Chun Kit Jacky Chan
School of Nursing and Health Sciences, Hong Kong Metropolitan University, Hong Kong, China
1 shared publication - Fabian Ling Ngai Tung
School of Nursing and Health Sciences, Hong Kong Metropolitan University, 1 Sheung Shing Street, Ho Man Tin, Hong Kong, China
1 shared publication - Jeff Yip
Hong Kong Institute of Paramedicine, Hong Kong, China
1 shared publication - Zoe Tsui
S.K. Yee School of Health Sciences, Saint Francis University, 2 Chui Ling Lane, Tseung Kwan O, New Territories, Hong Kong, China
1 shared publication
Latest Publications
- Open Access
A Randomized Controlled Trial of ABCD-IN-BARS Drone-Assisted Emergency Assessments
Emergency medical services confront significant challenges in delivering timely patient assessments within geographically isolated or disaster-impacted regions. While drones (unmanned aircraft systems, UAS) show transformative potential in healthcare, standardized protocols for drone-assisted patient evaluations remain underdeveloped. This study introduces the ABCD-IN-BARS protocol, a 9-step telemedicine checklist integrating patient-assisted maneuvers and drone technology to systematize remote emergency assessments. A wait-list randomized controlled trial with 68 first-aid-trained volunteers evaluated the protocol’s feasibility. Participants underwent web-based modules and in-person simulations and were randomized into immediate training or waitlist control groups. The ABCD-IN-BARS protocol was developed via a content validity approach, incorporating expert-rated items from the telemedicine literature. Outcomes included time-to-assessment, provider confidence (Modified Cooper–Harper Scale), measured at baseline, post-training, and 3-month follow-up. Ethical approval and informed consent were obtained. Most of the participants can complete the assessment with a cue card within 4 min. A mixed-design repeated measures ANOVA assessed the effects of Time (baseline, post-test, 3-month follow-up within subject) on assessment durations. Assessment times improved significantly over three time points (p = 0.008), improving with standardized protocols, while patterns were similar across groups (p = 0.101), reflecting skill retention at 3 months and not affected by injury or not. Protocol adherence in simulated injury identification increased from 63.3% pre-training to 100% post-training. Provider confidence remained high (MCH scores: 2.4–2.7/10), and Technology Acceptance Model (TAM) ratings emphasized strong Perceived Usefulness (PU2: M = 4.48) despite moderate ease-of-use challenges (EU2: M = 4.03). Qualitative feedback highlighted workflow benefits but noted challenges in drone maneuvering. The ABCD-IN-BARS protocol effectively standardizes drone-assisted emergency assessments, demonstrating retained proficiency and high usability. While sensory limitations persist, its modular design and alignment with ABCDE principles offer a scalable solution for prehospital care in underserved regions. Further multicenter validation is needed to generalize findings.
Open AccessA Randomized Controlled Trial of ABCD-IN-BARS Drone-Assisted Emergency Assessments
Emergency medical services confront significant challenges in delivering timely patient assessments within geographically isolated or disaster-impacted regions. While drones (unmanned aircraft systems, UAS) show transformative potential in healthcare, standardized protocols for drone-assisted patient evaluations remain underdeveloped. This study introduces the ABCD-IN-BARS protocol, a 9-step telemedicine checklist integrating patient-assisted maneuvers and drone technology to systematize remote emergency assessments. A wait-list randomized controlled trial with 68 first-aid-trained volunteers evaluated the protocol’s feasibility. Participants underwent web-based modules and in-person simulations and were randomized into immediate training or waitlist control groups. The ABCD-IN-BARS protocol was developed via a content validity approach, incorporating expert-rated items from the telemedicine literature. Outcomes included time-to-assessment, provider confidence (Modified Cooper–Harper Scale), measured at baseline, post-training, and 3-month follow-up. Ethical approval and informed consent were obtained. Most of the participants can complete the assessment with a cue card within 4 min. A mixed-design repeated measures ANOVA assessed the effects of Time (baseline, post-test, 3-month follow-up within subject) on assessment durations. Assessment times improved significantly over three time points (p = 0.008), improving with standardized protocols, while patterns were similar across groups (p = 0.101), reflecting skill retention at 3 months and not affected by injury or not. Protocol adherence in simulated injury identification increased from 63.3% pre-training to 100% post-training. Provider confidence remained high (MCH scores: 2.4–2.7/10), and Technology Acceptance Model (TAM) ratings emphasized strong Perceived Usefulness (PU2: M = 4.48) despite moderate ease-of-use challenges (EU2: M = 4.03). Qualitative feedback highlighted workflow benefits but noted challenges in drone maneuvering. The ABCD-IN-BARS protocol effectively standardizes drone-assisted emergency assessments, demonstrating retained proficiency and high usability. While sensory limitations persist, its modular design and alignment with ABCDE principles offer a scalable solution for prehospital care in underserved regions. Further multicenter validation is needed to generalize findings.