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Identification of key potential infection processes and risk factors in the computed tomography examination process by FMEA method under COVID-19

BMC Infectious Diseases volume 24, Article number: 257 (2024) Cite this article

Abstract

Purpose

To identify the key infection processes and risk factors in Computed Tomography (CT) examination process within the standard prevention and control measures for the COVID-19 epidemic, aiming to mitigate cross-infection occurrences in the hospital.

Method

The case hospital has assembled a team of 30 experts specialized in CT examination. Based on the CT examination process, the potential failure modes were assessed from the perspective of severity (S), occurrence probability (O), and detectability (D); they were then combined with corresponding risk prevention measures. Finally, key infection processes and risk factors were identified according to the risk priority number (RPN) and expert analysis.

Results

Through the application of RPN and further analysis, four key potential infection processes were identified, including “CT request form (A1),” “during the scan of CT patient (B2),” “CT room and objects disposal (C2),” and “medical waste (garbage) disposal (C3)”. In addition, eight key risk factors were also identified, including “cleaning personnel does not wear masks normatively (C32),” “nurse does not select the vein well, resulting in extravasation of the peripheral vein for enhanced CT (B25),” “patient cannot find the CT room (A13),” “patient has obtained a CT request form but does not know the procedure (A12),” “patient is too unwell to continue with the CT scan (B24),” “auxiliary staff (or technician) does not have a good grasp of the sterilization and disinfection standards (C21),” “auxiliary staff (or technician) does not sterilize the CT machine thoroughly (C22),” and “cleaning personnel lacks of knowledge of COVID-19 prevention and control (C33)”.

Conclusion

Hospitals can publicize the precautions regarding CT examination through various channels, reducing the incidence of CT examination failure. Hospitals’ cleaning services are usually outsourced, and the educational background of the staff employed in these services is generally not high. Therefore, during training and communication, it is more necessary to provide a series of scope and training programs that are aligned with their understanding level. The model developed in this study effectively identifies the key infection prevention process and critical risk factors, enhancing the safety of medical staff and patients. This has significant research implications for the potential epidemic of major infectious diseases.

Peer Review reports

Introduction

Hospitals were challenged related to the transmission risk of SARS-CoV-2 between healthcare workers and patients during the COVID-19 outbreak [1]. For example, as of February 11, 2022, a statistical study indicated that 3,019 medical personnel from 422 Chinese hospitals providing diagnosis and treatment for patients with novel coronavirus pneumonia had contracted the virus [2]. A study conducted in a Dutch hospital revealed that healthcare workers played a predominant role in transmitting nosocomial infection, both in single and multi-person wards [1]. Furthermore, research demonstrates that healthcare workers constitute the most frequently infected group with covid-19. In addition, another study highlighted that the nosocomial infection of SARS-CoV-2 mainly resulted from close contact between medical staff and patients [3].

Since the outbreak of novel coronavirus pneumonia, diagnostic imaging has provided valuable radiological support for disease diagnosis and prognosis prediction, such as X-ray, and computed tomography (CT) examinations [2, 4]. Radiology departments are at high risk of nosocomial COVID-19 infection [5]. Radiology personnel engage in direct contact with patients and bear the dual pressures of infection prevention and control and radiation protection measures [2]. Radiology staff must work in fever clinics, infection and respiratory clinics, isolation wards, and other workplaces where they have close contact with patients with fever, suspected or confirmed cases, thereby exposing themselves to a high risk of infection [2]. Several studies have indicated that risk assessment and associated preventive measures, such as risk assessment, testing, symptom monitoring, and prevention guidelines, are essential for preventing COVID-19 nosocomial infection transmission [6, 7]. Therefore, establishing a radiology risk assessment model with covid-19 characteristics and identifying key potential risk areas and factors will help mitigate infection risk during the diagnostic radiation process, ensuring the safety of both radiology staff and patients.

Limited studies have combined quantitative risk methods with the analysis of nosocomial infections during COVID-19. For example, Hsiung, Tung [8] combine Multi-Criteria Decision-Making (MCDM) and Failure Mode and Effects Analysis (FMEA) to propose a comprehensive risk assessment model. This model facilitates a thorough risk analysis and explores the ranking associated with COVID-19 in hospital screening procedures. Thomas and Suresh [9] combined a multi-grade fuzzy approach with importance-performance analysis (IPA) to develop an assessment framework for Covid-19 prevention and protection measures in hospitals. This framework serves as a continuous assessment tool to enhance improve Covid-19 prevention operations. Kannangara, Seetulsingh [10] used root cause analyses to analyze COVID-19 cases to identify potential causes, routes of transmission, and areas for improvement in managing the COVID-19 outbreak within acute admission units. Meziane, Taous [11] used healthcare failure mode and effect analysis to identify potential failure modes, determine key risk factors, and define the mitigation measures to mitigate the risk of COVID-19 infection in the operating room. Few hospitals have used failure mode and effect analysis to study nosocomial infections during COVID-19, and no study has examined COVID-19 infection prevention and control within the context of the CT examination process using failure mode and effect analysis.

This study uses FMEA to establish a complete risk analysis model of the CT examination process to make up for the gap in this study. Further, standardize the operation process of radiological medical staff to improve the risk prevention and control of radiological infection in hospitals in the face of major infectious epidemics in the future and ensure the safety of medical staff and patients.

Methods and materials

Research design and analysis process

The Radiology Department was established as the FMEA team within the case hospital. Their primary objective was to confirm the different stages and potential failure within the hospital’s CT examination process. They identified the stages of the CT process and the steps involved in each stage, as shown in Fig. 1, and summarized the potential failure modes during each stage according to relevant elements. Subsequently, they evaluated the potential failure patterns regarding severity (S), occurrence probability (O), and detectability (D), resulting in the determination of the risk priority number (RPN) of each stage. Finally, based on the RPN results and corresponding guidelines, the team determined key potential infection processes and risk factors in the CT examination process of the case hospital. The specific implementation process is shown in Fig. 2.

Fig. 1
figure 1

CT examination processes

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Fig. 2
figure 2

The research design and implementation process

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FMEA

As a proactive risk management tool, FMEA is an appropriate and effective technique for risk management within the healthcare domain [12, 13]. It is a management tool for identifying a system or process’ possible failure modes and analyzing failure’s causes and effects [14]. It could be used to analyze complex healthcare processes prospectively.

The most important feature of FMEA is its capacity for proactive system failure prevention. It involves identifying, prioritizing, and addressing known or potential system failure modes before they occur [15, 16]. Presently, owing to its efficacy, it can reduce the occurrence of adverse events and reduce unnecessary costs. Nowadays, owing to its effectiveness, FMEA has been widely used in various fields, including medical [17, 18], manufacturing [19], aviation [20], electronics [21], the chemical industry [22], and other fields to evaluate system safety. In addition, FMEA has a wide range of applications for healthcare risk analysis, such as healthcare process, hospital management, hospital informatization, and medical equipment/production [14]. FMEA has mainly been applied to the healthcare processes, such as blood transfusion [23], medication use [24], radiation therapy [25], and other treatment processes, for quality improvement. Therefore, FMEA has high practicality for healthcare quality improvement and error reduction and has been employed to improve healthcare processes in hospitals.

The calculation formula is as follows: \(RPN=S \times O \times D\) (1)

where S is the severity of failure mode failure, O is the probability of failure mode failure, and D is the degree of failure mode failure detected. RPN is the total score after multiplying these three indexes (i.e., S, O, and D), and the higher the value, the better the improvement.

Ethical approval

The study’s design and procedure were conducted under the guidance of the Institute’s Ethics Review Committee and according to the principles of the Helsinki Declaration. This project has received ethical approval with the number 2023 L-01-03. All participant information remained anonymous.

Data collection and participants

The case hospital assembled a multidisciplinary FMEA team comprising 30 experts from fields including Radiology, Emergency, Nursing, Medical, Hospitalization service center, Infectious disease, and Public health. The team was focused on the CT examination process and was through purposive sampling. Most of these team members are men (63%), aged 31 and 50 (70%). Most people have undergraduate education (77%) and over 6 years of work experience (94%). During the COVID-19 epidemic, all team members actively engaged in the clinical work within the hospital. Among them, 87% have worked in hospitals for over 31 months since the outbreak began and have had direct exposure to suspected or confirmed COVID-19 patients. The demographic information of domain experts is detailed in Table 1.

Table 1 Demographic information of the FMEA team for in-hospital CT examination
Full size table

Results

All FMEA team members initially identified different stages, processes, and corresponding potential failure modes in the CT examination process. This encompassed three stages, 10 processes, and 39 potential failure modes designed from the perspectives of Humans, Machine, Regulations, Environment, and objects.

Subsequently, all FMEA team members individually scored all potential failure modes from S, O, and D based on the CT examination process. The scoring scale ranged from 1 (S-insignificant/O-extremely unlikelihood/D-Absolutely sure) to 10 (S-catastrophic/O-inevitable/D-Absolutely abnormal). The individual scores assigned by the 30 team members in each index (S, O, and D) were integrated into a value. The median represented the score of this expert group for that particular index. The values of each index were then multiplied to derive the final integrated total score, which is RPN.

Finally, based on the RPN results, FMEA team members identified eight key risk factors, including “cleaning personnel does not wear masks normatively (C32) (RPN = 144),” “nurse does not select the vein well, resulting in extravasation of the peripheral vein for enhanced CT (B25) (RPN = 120),” “patient cannot find the CT room (A13) (RPN = 112.5),” “patient has obtained a CT request form but does not know the procedure (A12) (RPN = 110),” “patient is too unwell to continue with the CT scan (B24) (RPN = 97.5),” “auxiliary staff (or technician) does not have a good grasp of the sterilization and disinfection standards (C21) (RPN = 90),” “auxiliary staff (or technician) does not sterilize the CT machine thoroughly (C22) (RPN = 90),” and “cleaning personnel lacks of knowledge of COVID-19 prevention and control (C33) (RPN = 90)”. Furthermore, four key potential infection processes are summarized, namely “CT request form (A1),” “during the scan of CT patient (B2),” “CT room and objects disposal (C2),” and “medical waste (garbage) disposal (C3). Detailed FMEA analysis results of three stages, 10 processes and 39 potential failure modes in CT detection process are shown in Table 2.

Table 2 Risk analysis and RPN of the hospital radiology department
Full size table

Discussion

Clinical significance

The FMEA results of the case hospital identified eight key risk factors and four key potential corresponding essential prevention processes. The corresponding key risk factors are discussed in the following sequence:

For the “CT request form (A1)” process, “patient has obtained a CT request form but does not know the procedure (A12) (RPN = 110)” and “patient cannot find the CT room (A13) (RPN = 112.5)” are key risk factors. These issues are part of the patient access process within the case hospital. Under normal circumstances (in the absence of infectious disease), the patient could seek guidance from nurses, technicians, or others to complete the CT. However, the risk of nosocomial infections will increase owing to unnecessary contact during COVID-19 [26]. In the process of outpatient service delivery, formulating a service flowchart can streamline patient inquiries, avoid repetition in interpretation by hospital staff, improve the efficiency of hospital service delivery and patient service utilization, and ultimately improve the patient’s medical experience. The flowchart is often used as a simple and effective tool in formulating hospital service processes [27]. Therefore, the establishment of patient-centered CT flowcharts is recommended to enhance CT resource utilization and contribute to an improved patient experience.

For the “during the scan of CT patient (B2)” process, “nurse does not select the vein well, resulting in extravasation of the peripheral vein for enhanced CT (B25) (RPN = 120)” and “patient is too unwell to continue with the CT scan (B24) (RPN = 97.5)” are key risk factors. The selection of the injection vein is crucial in mitigating extravasation risk; an inappropriate choice of vein can lead to extravasation [28]. Concurrently, patients may experience sudden discomfort during the CT, such as local/systemic allergic reactions to the contrast agent [29]. In both cases, the patient is unable to complete the CT successfully, necessitating a repeat scan or rescheduling, which increases the patient’s usage frequency of the CT room and the frequency of doctor-patient contact, thereby increasing the risk of nosocomial COVID-19 infection [26]. Therefore, nurses should refine their proficiency in handling the indwelling needle and venous access. Simultaneously, physicians should improve the assessment of patients’ physical conditions before CT examination to reduce the possibility of patient discomfort during CT.

Concerning the process designated as “CT room and objects disposal (C2)” the key risk factors encompass “auxiliary staff (or technician) does not have a good grasp of the sterilization and disinfection standards (C21) (RPN = 90)” and “auxiliary staff (or technician) does not sterilize the CT machine thoroughly (C22) (RPN = 90)”. These factors may be because of inadequate training, insufficient knowledge of disinfection standards and specifications, and lack of effective supervision. The disinfection of equipment rooms and objects is a step in preventing and controlling nosocomial COVID-19 infection [2, 8] and requires appropriate attention. In COVID-19 prevention and control, it becomes imperative to elucidate and standardize the infection prevention and control knowledge and site disinfection. This entails strengthening the training and supervision of relevant personnel to ensure better implementation of infection control measures during the diagnostic radiological examination of COVID-19 cases [2].

Regarding the “medical waste (garbage) disposal (C3)” process, “cleaning personnel does not wear masks normatively (C32) (RPN = 144)” and “cleaning personnel lacks of knowledge of COVID-19 prevention and control (C33) (RPN = 90)” are key risk factors. The failure of cleaning staff to wear masks according to the norm scores the highest among all risk factors. Proper wearing of masks and other protective equipment constitutes the paramount measure in controlling the spread of COVID-19 [30]. A significant portion of COVID-19 transmissions can be attributed to improper mask-wearing and other protective equipment. Notably, cleaning staff are non-medical professionals, and their awareness of infectious disease prevention and control is much lower than that of healthcare workers. Their limited understanding and compliance with protective equipment usage and a potential lack of vigilance for disease would put non-frontline healthcare workers at higher risk of infection [30, 31]. Cleaning staff are responsible for the daily cleaning and disinfection of healthcare facilities and play an important role in preventing and controlling nosocomial COVID-19 infections [32]. Therefore, it is imperative to prioritize the training and supervision of cleaning staff in managing nosocomial COVID-19 infection.

Practical applications

Based on the aforementioned FMEA results, the hospital management team can provide the following recommendations to control and improve the management of key risk factors during CT examinations.

For “cleaning personnel does not wear masks normatively (C32) (RPN = 144)”, the following improvement measures are suggested. First, address the issue of radiology cleaning staff. Subsequently, implement homogenization training and assessment [33]. Finally, the assessment results are linked with individual salary performance.

Regarding “nurse does not select the vein well, resulting in extravasation of the peripheral vein for enhanced CT (B25) (RPN = 120)”, the following improvement measures are proposed. First, the cephalic vein is the first choice for enhanced CT. For general enhancement or arterial series, appropriate indwelling needles should be used respectively. Then, for patients with poor venous condition, please consult the intravenous treatment team in the hospital under ultrasound guidance and perform difficult punctures. Finally, during CT, the patient should hold a venous alarm to call the nurse when the pain is uncomfortable, and the nurse can intervene early or in time to prevent venous extravasation [34].

Regarding “patient cannot find the CT room (A13) (RPN = 112.5)”, the following improvement measures are suggested. First, if it is an emergency patient, the hospital can increase the guiding staff in the emergency department area to assist the patient in navigating to the CT room. Subsequently, within the radiology department’s CT area, the hospital can deploy volunteers to enhance the accessibility of patients’ inquiries. Finally, add appropriate guidance signs from the patient’s point of view to increase patient autonomy, such as ground and wall.

For “patient has obtained a CT request form but does not know the procedure (A12) (RPN = 110)”, the following improvement measures are suggested. First, consider and modify the display mode of the original guide sheet from the patient’s point of view, and add the notification action of the CT examination process. Subsequently, the CT examination process video is added to the electronic education video of the radiology department. Finally, the video of the CT examination process is played in a continuous rolling way.

Addressing “patient is too unwell to continue with the CT scan (B24) (RPN = 97.5)”, the following improvement measures are recommended. Before the examination, CT technicians should initially inquire about patients’ medical history and allergies they might have. For unconscious patients, family members or medical personnel must be required to accompany them throughout the examination. In addition, communicate with patients at any time. If patients feel unwell, wave and call CT technicians and nursing staff in time.

Concerning “auxiliary staff (or technician) does not have a good grasp of the sterilization and disinfection standards (C21) (RPN = 90)”, the following improvement measures are proposed. Radiology assistants and technicians need a fixed time for training and assessment after the end to validate the training effectiveness [35]. During the epidemic period, daily inspections pertaining to COVID-19 are conducted in the department, ensuring stringent compliance with all disinfection standards.

For “auxiliary staff (or technician) does not sterilize the CT machine thoroughly (C22) (RPN = 90)”, the following improvement measures are suggested. First, the CT indoor air disinfection machine is opened regularly, four times a day, with an interval of 6 h each time [36]. Furthermore, use disposable sheets to ensure every patient uses the cleanest sheets. Subsequently, after daily inspections, disinfect the computer room for 1 h with an ultraviolet lamp [37]. Finally, the CT machine tool and air disinfection machine should be maintained regularly to ensure that the environment disinfection of the CT room is qualified.

Addressing “cleaning personnel lacks of knowledge of COVID-19 prevention and control (C33) (RPN = 90)”, the following improvement measures are recommended. First, establish fixed positions for the cleaning staff within the CT room. Subsequently, from the point of view of cleaning staff, provide training programs and implement assessment in a fixed and continuous manner. Finally, implement effective supervision and link it with performance. Crucially, ensure that the cleaning staff understands the principles and norms of COVID-19 infection prevention and control, emphasizing that implementation is the key to prevention.

Limitations

First, it is worth acknowledging that the CT examination process and corresponding potential failure modes may differ slightly from hospital to hospital. Secondly, the participants in this study adopt purpose sampling, which may lead to sampling deviation. On the contrary, the lack of comparative analyses of FMEA with other risk assessment methods is one of the limitations of this study. Finally, FMEA results only show the current investigation status of the case hospital at that time and should not infer the subsequent time point. Simultaneously, the results should not be inferred from other hospitals.

Conclusion

The findings of this study indicate the potential presence of key risk factors in the following procedures: (a) Patients might encounter difficulties locating the CT room before examination or do not understand the standard procedures of CT examination; (b) During the examination, nurses could select inappropriate veins, resulting in peripheral vein extravasation, and patients may struggle to adhere with the standard scanning procedures; (c) After examination, technicians (or auxiliary personnel) fail to carry out terminal disinfection treatment according to COVDID-19 standard prevention and control requirements. These problems could be preemptively addressed through good training and periodic retraining of the involved personnel. In addition, hospitals can publicize the precautions in the examination through various channels, which can reduce the incidence of failure in CT examination and make patients, medical staff, technicians, and auxiliary personnel actively participate and cooperate well. Finally, cleaning services in hospitals are usually outsourced, and their educational background is usually low. Therefore, in training and communication, it is crucial to provide a series of targeted training programs from a perspective that they can understand.

Radiology is crucial in preventing and controlling COVID-19, and CT examination is one of the keys to preventing and controlling infection. For suspected or confirmed COVID-19 patients, early examination, diagnosis, and isolation are imperative to mitigate hospital-acquired infections and potential systemic disruptions (such as the breakdown of the hospital medical system). Simultaneously, the CT examination need of other patients should also be prioritized.

FMEA risk assessment can identify potential risks during CT examination during COVID-19. Hospital decision-makers and managers can improve the occupational protection awareness of medical and cleaning staff in the radiology department through corresponding prevention planning and measures. In addition, this kind of research with practical application will help medical staff establish a risk management and control model of the CT examination process for major infectious epidemics in hospitals from the practical experience during the COVID-19 epidemic. The model can effectively identify the key infection prevention processes and key risk factors and better protect the safety of medical staff and patients. This is of great research significance in the face of potential major infectious epidemics in the future.

Data availability

The original contributions presented in the study are included in the article materials, further inquiries can be directed to the corresponding authors.

References

  1. Haanappel CP, et al. Combining epidemiological data and whole genome sequencing to understand SARS-CoV-2 transmission dynamics in a large tertiary care hospital during the first COVID-19 wave in the Netherlands focusing on healthcare workers. Antimicrob Resist Infect Control. 2023;12(1):46.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Niu Y, et al. Management of infection control and radiological protection in diagnostic radiology examination of COVID-19 cases. Radiat Med Prot. 2020;1(2):75–80.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Borges V et al. Nosocomial outbreak of SARS-CoV-2 in a Non-COVID-19 Hospital Ward: Virus Genome sequencing as a Key Tool to understand cryptic transmission. Viruses, 2021. 13(4).

  4. Ding J, et al. Prevention and control measures in radiology department for COVID-19. Eur Radiol. 2020;30(7):3603–8.

    Article  MathSciNet  CAS  PubMed  PubMed Central  Google Scholar 

  5. Chan WP, et al. Management and infection control practices in a Taiwanese radiology department during the COVID-19 outbreak. J Microbiol Immunol Infect. 2021;54(3):349–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Chong DW, et al. Propagation of a hospital-associated cluster of COVID-19 in Malaysia. BMC Infect Dis. 2021;21(1):1238.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Chang MC, Hur J, Park D. Strategies for the Prevention of the Intra-hospital transmission of COVID-19: a retrospective cohort study. Volume 8. Healthcare (Basel); 2020. 3.

  8. Hsiung M-C, et al. A hybrid model to assess the risk of COVID-19 in hospital screening procedures under uncertain information. Int J Disaster Risk Reduct. 2023;96:103911.

    Article  Google Scholar 

  9. Thomas A, Suresh M. Assessment of COVID-19 prevention and protection measures in hospitals. Clean Eng Technol. 2022;7:100440.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kannangara CI, et al. Investigation and management of an outbreak of COVID-19 infection in an acute admission unit in a District General Hospital: lessons learnt. Infect Prev Pract. 2021;3(3):100156.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Meziane S, Taous M, Bourkhiss B. Mitigating the risk of COVID-19 infection in the operating room: using healthcare failure mode and effect analysis. Perioper Care Oper Room Manag. 2023;31:100319.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Claxton K, Campbell-Allen NM. Failure modes effects analysis (FMEA) for review of a diagnostic genetic laboratory process. Int J Qual Reliab Manage. 2017;34(2):265–77.

    Article  Google Scholar 

  13. Wang L-E, Liu H-C, Quan M-Y. Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments. Comput Ind Eng. 2016;102:175–85.

    Article  Google Scholar 

  14. Liu HC, et al. Failure mode and effects analysis for proactive healthcare risk evaluation: a systematic literature review. J Eval Clin Pract. 2020;26(4):1320–37.

    Article  CAS  PubMed  Google Scholar 

  15. Huang J, et al. Failure mode and effect analysis improvement: a systematic literature review and future research agenda. Reliab Eng Syst Saf. 2020;199:106885.

    Article  Google Scholar 

  16. Faiella G, et al. Expanding healthcare failure mode and effect analysis: a composite proactive risk analysis approach. Reliab Eng Syst Saf. 2018;169:117–26.

    Article  Google Scholar 

  17. Rusu I, et al. Failure mode and effects analysis of linac-based liver stereotactic body radiotherapy. Med Phys. 2020;47(3):937–47.

    Article  PubMed  Google Scholar 

  18. Thornton E, et al. Application of failure mode and effect analysis in a radiology department. Radiographics. 2011;31(1):281–93.

    Article  PubMed  Google Scholar 

  19. Wu Z, Liu W, Nie W. Literature review and prospect of the development and application of FMEA in manufacturing industry. Int J Adv Manuf Technol. 2021;112(5–6):1409–36.

    Article  Google Scholar 

  20. Su X, et al. An improved method for risk evaluation in failure modes and effects analysis of aircraft engine rotor blades. Eng Fail Anal. 2012;26:164–74.

    Article  Google Scholar 

  21. Wang Z, et al. Failure mode and effects analysis using Dempster-Shafer theory and TOPSIS method: application to the gas insulated metal enclosed transmission line (GIL). Appl Soft Comput. 2018;70:633–47.

    Article  Google Scholar 

  22. Ahn J, et al. Fuzzy-based failure mode and effect analysis (FMEA) of a hybrid molten carbonate fuel cell (MCFC) and gas turbine system for marine propulsion. J Power Sources. 2017;364:226–33.

    Article  CAS  Google Scholar 

  23. Najafpour Z, et al. Preventing blood transfusion failures: FMEA, an effective assessment method. BMC Health Serv Res. 2017;17(1):453.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Aranaz-Andres JM, et al. Failure mode and effects analysis applied to the administration of liquid medication by oral syringes. Farm Hosp. 2017;41(6):674–7.

    PubMed  Google Scholar 

  25. Jones RT, et al. Safety and feasibility of STAT RAD: improvement of a novel rapid tomotherapy-based radiation therapy workflow by failure mode and effects analysis. Pract Radiat Oncol. 2015;5(2):106–12.

    Article  PubMed  Google Scholar 

  26. Girum T, et al. Optimal strategies for COVID-19 prevention from global evidence achieved through social distancing, stay at home, travel restriction and lockdown: a systematic review. Arch Public Health. 2021;79(1):150.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Su H, Wan G, Wang S. Online scheduling for outpatient services with heterogeneous patients and physicians. J Comb Optim. 2017;37(1):123–49.

    Article  MathSciNet  Google Scholar 

  28. Pacheco Compana FJ, Gago Vidal B, Mendez Diaz C. [Extravasation of contrast media at the puncture site: strategies for managment]. Radiologia. 2014;56(4):295–302.

    CAS  PubMed  Google Scholar 

  29. Nicola R, et al. Contrast media extravasation of computed tomography and magnetic resonance imaging: Management guidelines for the Radiologist. Curr Probl Diagn Radiol. 2016;45(3):161–4.

    Article  PubMed  Google Scholar 

  30. Cheng CW, et al. Infection control operations of a large hospital in Taiwan to prevent nosocomial outbreaks during COVID-19 pandemic. Biomed J. 2021;44(6 Suppl 1):S8–S14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ng QX, et al. Impact of COVID-19 on environmental services workers in healthcare settings: a scoping review. J Hosp Infect. 2022;130:95–103.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Singh V, et al. Impact of Video modules-based training on knowledge, attitude, and practices of Cleaning and Disinfection among Housekeeping Staff at a Tertiary Care Center during the COVID-19 pandemic. Cureus. 2021;13(10):e19125.

    PubMed  PubMed Central  Google Scholar 

  33. Rainford LA, et al. The impact of COVID-19 upon student radiographers and clinical training. Radiography (Lond). 2021;27(2):464–74.

    Article  CAS  PubMed  Google Scholar 

  34. Mandlik V, Prantl L, Schreyer AG. Contrast Media Extravasation in CT and MRI - A Literature Review and Strategies for Therapy. Rofo. 2019;191(1):25–32.

    Article  PubMed  Google Scholar 

  35. Akyurt N. Knowledge, practice and emotional status related to COVID-19 pandemic among radiology technicians working at pandemic hospitals. Eur J Radiol. 2021;134:109431.

    Article  PubMed  Google Scholar 

  36. Beggs CB, Avital EJ. Upper-room ultraviolet air disinfection might help to reduce COVID-19 transmission in buildings: a feasibility study. PeerJ. 2020;8:e10196.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Bono N et al. Effect of UV irradiation and TiO(2)-Photocatalysis on Airborne Bacteria and viruses: an overview. Mater (Basel), 2021. 14(5).

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Acknowledgements

Not applicable.

Funding

This work was supported by the National Natural Science Foundation of China (No. 72204183).

Author information

Author notes

  1. Lingzhi Jin and Meiting Ye contributed equally to this study.

Authors and Affiliations

  1. Radiology department, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China

    Lingzhi Jin, Meiting Ye & Wenhua Lin

  2. Institute of Public Health and Emergency Management, Taizhou University, Taizhou, Zhejiang, China

    Yong Ye & Yen-Ching Chuang

  3. Business College, Taizhou University, Taizhou, Zhejiang, China

    Yong Ye & Yen-Ching Chuang

  4. Key Laboratory of evidence-based Radiology of Taizhou, Linhai, Zhejiang, China

    Yen-Ching Chuang

  5. Institute for Hospital Management, Tsing Hua University, Shenzhen, Guangdong, China

    Jin-Yan Luo

  6. Nursing Department, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China

    Fuqin Tang

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Contributions

Lingzhi Jin, Yen-Ching Chuang, and Jin-Yan Luo conducted the study and drafted the manuscript. Meiting Ye and Wenhua Lin collected the FMEA data. Yen-Ching Chuang analyzed the FMEA data by Excel software. Yen-Ching Chuang, Yong Ye, and Fuqin Tang conceived the study, and participated in its design and coordination. All of the authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yen-Ching Chuang, Jin-Yan Luo or Fuqin Tang.

Ethics declarations

Ethical approval and consent to participate

All procedures were performed under the guidelines of our institution. The study was approved by the ethical review Board of Taizhou Central Hospital (Taizhou University Hospital) (approval number:2023 L-01-03) and comply with the principles of Helsinki Declaration. All respondents gave informed consent to participate in the survey, and personal information was treated anonymously.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Jin, L., Ye, M., Lin, W. et al. Identification of key potential infection processes and risk factors in the computed tomography examination process by FMEA method under COVID-19. BMC Infect Dis 24, 257 (2024). https://doi.org/10.1186/s12879-024-09136-z

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BMC Infectious Diseases

ISSN: 1471-2334

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