Sajjad Mozaffari, Kamal Ad-Din Abedi, Amir Abbasi Garmaroudi,
Volume 18, Issue 1 (3-2021)
Abstract
Introduction: This study aimed to assess the preparedness of educational hospitals of Kurdistan University of Medical Sciences using a self-designed questionnaire that measures the six factors of resiliency.
Methods and Materials: In this descriptive-analytical study, data collection was performed through a self-designed questionnaire covering six factors of resilience engineering, the validity and reliability of that were examined using CVR, CVI and alpha Cronbach. Data collection using a questionnaire by stratified sampling from four hospitals and data analysis was performed with SPSS.24.
Results: Reliability analysis shows that the alpha coefficient (α = 0.98) has excellent internal consistency, and CVI and CVR were estimated at 0.78 and 0.97, respectively. From 1211 people of survey population by considering inclusion study criteria, 118 persons were randomly selected through stratified sampling. The results of the questionnaire analysis revealed that the average factor score (SD) as follows: Correct culture 42.54 (2.98), learning culture 72.69 (3.45), awareness and opacity 34.61 (3.5) were assessed with the highest score. The management commitment 25.66 (2.85) and preparedness factor 119.93 (5.8) were in the range of moderate, and the flexibility factor was 14.64 (2.12). The results of correlation show that there is a positive and significant strong relationship between hospital resiliency and demographic characteristics (age: r = 0.65, years of experience: r = 0.69, level of education; r = 0.53, P-value < 0.001.
Conclusion: Years of work experience and level of education play an essential role in increasing the resiliency of hospitals. Should give training and maneuvering in triage, treatment, accident command, and decontamination priority.
Amir Abbasi Garmaroudi, Sajjad Mozaffari, Mahshid Bahrami, Hadi Alimoradi,
Volume 18, Issue 1 (3-2021)
Abstract
Introduction: Every year, many patients in different wards of the hospital die due to human error. One of the important aims in the field of human errors related to clinical care is to identify and prevent the occurrence of harmful effects caused by such errors. One of the preventive approaches in order to prevent human errors is to recognize and analyze them. The purpose of this study is to investigate all clinical care processes in the coronary care unit, identify human errors and prioritize the tasks and errors in this section.
Methods and Materials: This descriptive-analytical study was performed cross-sectional in three stages. In the first stage, human errors were identified and evaluated using the SHERPA approach and the frequency of each human error in the CCU was determined. In the second stage, the criteria and factors affecting the occurrence of human error in the CCU were determined by Delphi method. Finally, in the third stage, with the Analytic hierarchy process, the criteria and tasks that had the highest frequency of errors were prioritized. SPSS.24 and Expert choice.11 are two software that used to analyze data in this study.
Results: Among the 116 identified errors with SHERPA, the highest percentages of errors identified from the total errors were related to the functional error (68.1%) and lowest percentages related to selective errors (2.58%) respectively. The results of different Delphi rounds showed that the factor of skill, experience, equipment, work time and workload are the most important factors which affecting the occurrence of human errors in the CCU. By pair comparison of this factors, we founded that experience and skill of nurses, with a weight of 0.278 and 0.272, have the most important and factor of equipment with a weight of 0.087, had the lowest important among the five effective factors for incidence of human error in the CCU. The task of extracting airway discharge with a weight of 0.125 was the most preferred among 16 tasks, in other words task of extracting airway discharge was the most risk of error in functional error. Factor of experience and skill as the most important factor in the occurrence of functional error. In the error type of recovery, the task of identifying drug information (time, date, dose and dosage form, etc.) with a weight of 0.054 was the highest rank in this type of error. The factor of experience and loading with weights of 0.018 and 0.01 were the most important, respectively in this type.
Conclusion: In the coronary care unit, functional and revisions errors in the vital tasks such as cardiopulmonary resuscitation, dose adjustment, and proper drug injection were the most common. Therefore, design and implementation of control measures such as periodic training on how to do the job properly, preparation of checklists with a focus on human behavior in different job processes recommended to eliminate or reduce the amount of identified errors in the hospital's CCU.
Sajjad Mozaffari, Majid Bayatian, Nan-Hung Hsieh, Monireh Khadem, Amir Abbasi Garmaroudi, Khosro Ashrafi, Seyed Jamaleddin Shahtaheri,
Volume 20, Issue 1 (3-2023)
Abstract
Abstract
Background and aims: Given the lack of a developed physiologically based toxicokinetic (PBTK) model for human systemic exposure assessment of methylene diisocyanate (MDI) and prediction of its urinary metabolites, this study aims to develop a PBTK model for exposure risk assessment of MDI.
Methods : In this study, to assess the potential exposure to the MDI, a PBTK model was constructed with parameter uncertainty and variability and calibrated using Bayesian analysis via Markov chain Monte Carlo approach. Exposure reconstruction or reverse dosimetry was performed as an occupational exposure risk assessment through time-kinetic urinary elimination of methylenedianiline (MDA), as the biomarker of MDI, in those exposed to unknown exposure scenarios.
Results: Approximately 15 hours after the start of exposure, the amount of MDA excretion peaked. Understanding simulation results of reverse dosimetry for both exposed persons to the unknown concentration of MDI revealed experienced more systemic exposure than NOAEL (NOAEL = 0.2 ug / l), the exposure concentration (±SD) was 1.58 (±0.856) and 1.005 (±0.705) ug/l for person A and B, respectively. Comparison of predicted results with experimental data shows the model can estimate the kinetic elimination closely to experimental data (R2 = 0.9).
Conclusion: Developed model can be performed to estimate the internal dose of body tissues and understand the risk of occupational exposures by comparing the simulation of biological monitoring with acceptable limit values and determining the potential of external exposure.
