This paper presents a comparison of four common Human Reliability Assessment (HRA) models through a scoping literature review and sensitivity analysis. The scoping literature review identified 72 relevant studies which formed the basis of the comparison. Studies reported the four selected models have similarities in terms of the sector of origin, applied sectors, output calculation, and a lack of clear guidelines on Performance Influencing Factors (PIFs) selection and risk level allocation. The studied models have differences in the number and type of PIF inputs and Human Error Probability (HEP) calculation procedures. The One Factor At a Time (OFAT) and "combined" sensitivity analysis were conducted to examine the HRA models' responses to systematic risk level changes when each of 8 matching PIFs were systematically set to "high" and then "low" levels individually and simultaneously. The OFAT analysis showed coefficients of variation (CV) in HEP varying from 9% for skills/training up to 94% for work procedure when the PIFs are assigned to a "low" risk level individually. The combined analysis showed the median HEP value close to 97% and 1% when PIFs are assigned to" high" and "low" risk levels respectively. Although the selected HRA models were reported to be validated in high-risk domains there was no study found that validated these models in low-risk domains such as manual order picking, or manual assembly lines. The HRA models examined here are disconnected from specific system design elements which can inhibit design improvement efforts. The study outcome suggests the need for clear guidelines for PIFs selection and risk level allocation. Future research should address both the connection of error assessment to the design of the system and the features of new HRA models that affect its reliability and validity in a variety of industrial contexts.
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http://dx.doi.org/10.1016/j.apergo.2022.103750 | DOI Listing |
Eye (Lond)
January 2025
Department of Surgical Sciences, University of Turin, Turin, Italy.
Purpose: This study aims to develop a deep-learning-based software capable of detecting and differentiating microaneurysms (MAs) as hyporeflective or hyperreflective on structural optical coherence tomography (OCT) images in patients with non-proliferative diabetic retinopathy (NPDR).
Methods: A retrospective cohort of 249 patients (498 eyes) diagnosed with NPDR was analysed. Structural OCT scans were obtained using the Heidelberg Spectralis HRA + OCT device.
BMC Health Serv Res
January 2025
ORCHID Centre for Outcomes and Experience Research in Child Health, Illness and Disability Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
Background: During COVID-19 pandemic, a rapid readjustment to continued delivery of healthcare was required. Redeployment is an intentional process to mobilise human resources by reassigning a healthcare worker to a new role or new work location, to achieve sustainable delivery of patient care. We report redeployment experiences of staff from a specialist children's hospital during first and second waves of the United Kingdom COVID-19 pandemic.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Institute of Physics, University of Zielona Góra, 65-069 Zielona Góra, Poland.
This study investigates whether heart rate asymmetry (HRA) parameters offer insights into sleep stages beyond those provided by conventional heart rate variability (HRV) and complexity measures. Utilizing 31 polysomnographic recordings, we focused exclusively on electrocardiogram (ECG) data, specifically the RR interval time series, to explore heart rate dynamics associated with different sleep stages. Employing both statistical techniques and machine learning models, with the Generalized Estimating Equation model as the foundational approach, we assessed the effectiveness of HRA in identifying and differentiating sleep stages and transitions.
View Article and Find Full Text PDFHuan Jing Ke Xue
January 2025
School of Water Resources & Environmental Engineering, East China University of Technology, Nanchang 330013, China.
The identification of distribution characteristics, pollution sources, and potential human health risks of heavy metals in groundwater is crucial for the scientific planning and rational development of groundwater resources in arid-semiarid regions. In this study, 46 groundwater samples were collected and analyzed using hydrogeochemical modeling and multivariate statistical analysis methods to reveal the pollution characteristics and speciation distribution of 11 heavy metals (As, B, Pb, Sb, Tl, Mn, Ba, Cd, Co, Cr, and Al) in the Datong Basin. The absolute principal component-linear regression (APCS-MLR) model and health risk assessment model (HRA) were employed to determine the sources and health risk levels of heavy metals in groundwater.
View Article and Find Full Text PDFBMC Health Serv Res
December 2024
Open Medical Ltd, London, UK.
Background: The UK's National Health Service (NHS) is grappling with rising demand and limited dermatologists, leading to longer waiting times. This is particularly concerning for conditions like malignant melanoma, where early diagnosis is crucial. Teledermatology is being introduced to address these issues, but its impact on patients' monetary and time costs, especially in deprived areas, is under-researched.
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