Bottleneck factors impacting nurses’ workflow and the opportunity to prioritize improvement efforts: Factor analysis

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Certain bottleneck variables restrict the workflow of nurses, resulting in extended shift times. This study is designed to pinpoint and analyze the principal factors contributing to bottleneck issues in nursing workflow, to direct improvement endeavors. This study seeks to provide insights into the key variables contributing to nurses' extended shift times, with the ultimate goal of prioritizing efforts for improvement. Methods A descriptive multicenter cross-sectional study was conducted. A scale was developed for this study by the authors after conducting a literature review, subsequently validated, and its reliability was assessed. Results Among the 31 bottleneck variables, 29 were retained under three bottleneck factors: (1) Nurse staffing— This pertains to the availability of sufficient nursing staff at all times across the continuum of care; (2) Working environment and quality of care—This refers to the availability of necessary skills and resources for nurses to perform their duties effectively and; (3) Medical devices— This factor concerns the availability of fully functional medical devices required for providing care. Conclusion Efforts aimed at enhancing the overall healthcare system should concentrate on addressing bottleneck factors. This may involve the implementation of a healthcare workforce management system, the establishment of standards for a conducive and supportive working environment, and the utilization of a standardized system for the management of medical equipment. The outcomes of this study can be utilized by nurses and policymakers to devise comprehensive strategies for improvement. work-flow nursing productivity staffing equipment device medical Figures Figure 1 Introduction In the Kingdom of Saudi Arabia (KSA), the nursing workforce is the largest population group in the healthcare system, with a total of 184,565 registered nurses currently working in Ministry of Health (MOH) institutions [ 1 ]. The nursing workforce plays a crucial role in almost every aspect of health services and serves as a driver for care improvement [ 2 ]. Ensuring nursing workflows is essential to improve patient care outcomes, enabling healthcare institutions to operate effectively and support service improvement [ 3 ]. Workflow efficiency refers to the measurement of resources, especially time, spent by a nurse to complete a regularly recurring task during a shift [ 3 ]. However, the efficiency of the nursing workforce in healthcare settings is restricted by one or more variables, referred to as bottlenecks, indicating that despite the successful implementation of improvement efforts, challenges cannot be avoided [ 4 ]. Nurse workflow In the context of healthcare services, a bottleneck refers to a factor that restricts or slows down the workflow of nurses, leading to inefficiency [ 5 ]. Prioritizing improvement activities to address bottlenecks can result in significant improvements in the overall work productivity of the healthcare institution [ 6 ]. Therefore, healthcare stakeholders need to conduct a thorough investigation to identify bottleneck factors that may affect nurses' work efficiency, particularly if the healthcare system aims to provide efficient healthcare services. Various techniques have been used to analyze the gap between nurse workflow and the actual activities performed by them in a healthcare setting. This study is the second phase of a multi-phase, multi-center quality improvement project in the Taif region of Saudi Arabia. The ultimate goal of the project is to enhance patient care outcomes from the perspective of nursing productivity. Al-Moteri et al. [ 3 ] conducted a study to determine the time taken by nurses to complete various activities during their shifts. The research revealed that on average, nurses spent 408 minutes per shift on the following activities: 93 minutes on direct patient care, 58 minutes on indirect patient care, 68 minutes on documentation, 24 minutes on professional communication, 87 minutes on ward/room activities, 20 minutes on miscellaneous activities, and 58 minutes on personal needs. Additionally, the study found that nurses spent 69 minutes traveling between patient rooms, stations, and units. According to Al-Moteri and colleagues, it was found that there was an average delay of 73 minutes beyond the end of the shift to complete all necessary unaccomplished shift activities. Researchers concluded that the amount of work undertaken by nurses during a shift exceeds the capacity to process it within the shift duration (8-hour morning shift: 480 minutes), resulting in overextended resources and shift times (550 minutes) Fig. 1 . Extensive research has focused on identifying the factors influencing nurses' workflow and contributing to shift delays [ 7 – 10 ]. However, there is a notable dearth of comprehensive investigations aimed at analyzing the critical variables that give rise to inefficiencies in nursing workflow. This absence of inquiry fails to underscore the influential variables that precipitate inefficiency. The primary objective of the current study is to investigate the bottleneck variables influencing the nursing workflow. Given the complexity of identifying bottlenecks, this necessitates the utilization of the bottleneck detection analysis technique, as outlined by Bemthuis et al. [ 11 ]. This multi-center study aims to address the existing knowledge gap through the specific identification and analysis of bottlenecks within the nursing workflow. Through this approach, the study seeks to provide insights into the key variables contributing to nurses' extended shift times, with the ultimate goal of prioritizing efforts for improvement. Methods Design This descriptive multicenter cross-sectional study was conducted between October 2023 and November 2023 in the Taif region of Saudi Arabia. A bottleneck variable assessment method was used and included a combination of techniques to identify problems that occur and impact nursing workflow efficiency. The following includes a step-by-step procedure [ 6 ]: Step 1 Obtain variables from the nursing literature Step 2 Design a structured questionnaire using variables obtained from Step 1 Step 3 Determine sample size Step 4 Distribute the questionnaire online Step 5 Data analysis Step 6 Conduct PCA Step 7 Identify bottleneck variables to devise and prioritize improvement actions Setting and sampling Every government hospital in the Taif region was invited to participate in the current study. Four large government hospitals agreed to participate, collectively having a total of 1,903 beds and serving around 709,000 people. Once the ethical approval was obtained, permission from the participating hospitals was requested. In this study, the representative sample size of the nursing population was determined using Thompson's [ 12 ] formula. Out of the 2460 nurses working in the four governmental hospitals, 334 nurses were considered appropriate for the study when population distributions were normal. Drafting the questionnaire Step 1: An initial review of the literature was undertaken to identify bottleneck variables impacting nursing workflow. Forty bottleneck variables affecting nursing workflow were identified and statements of the data collection tool were constructed accordingly. Step 2: The content validity index (CVI) of the current study data collection tool was evaluated by five subject matter experts (SMEs) possessing a minimum of 10 years of work experience in nursing administration. According to Shrotryia and Dhanda [ 13 ] assessing CVI requires assessing the relevancy of each item (I-CVI) and the overall scale (S-CVI). In this regard, SMEs were instructed to independently rate their opinion regarding the relevancy of the items by answering the following question: to what extent is item (X) perceived as a challenge that may restrict the workflow of nurses, resulting in extended shift times? SMEs were instructed to rate the 40 initial items using a 4-point Likert scale in which: 1 = not at all, 2 = slightly, 3 = moderately, and 4 = highly. A four-point scale was selected to avoid a neutral opinion. The I-CVI was then assessed by calculating the number of SMEs who rated the item as moderate = 3 or high = 4 and divided by the total number of SMEs [ 14 ]. The I-CVI for all the items on the scale ranged from 0.72–1. According to Lynn [ 15 ], any item scored below 0.78 can be eliminated. Nine items scored below 0.78 and were omitted. Thirty-one items were then tested for the S-CVI and were 0.85 [ 14 ], indicating high content validity for the overall instrument. Thirty-one items were included in the semi-final version. Step 3: Before commencing the data collection process, a pilot study was carried out with 25 eligible participants to evaluate the clarity of the scale items and the reliability of the whole scale using a 5-point Likert scale, ranging from "always" to "never". These participants were informed to read the scale items loudly and briefly explain their understanding of each. Thus, the authors were able to determine if participants had difficulty understanding or if there were discrepancies in understanding and then modified the items accordingly. The pilot study was then conducted and the results showed a robust 𝛼 coefficient of 0.956 for the whole scale based on participants' responses, indicating that the items effectively measure the underlying construct of bottleneck factors among respondents. Step 4: The final questionnaire (31 items) was supplemented with additional items to assess the demographic data such as age, gender, qualifications, and work experience of the participants (Please see supplemented file) Data collection The final version of the scale was distributed to all nurses in the participating hospitals via an online Google Form, facilitated by their respective administration offices. Participants in the pilot study were excluded. Before accessing the Google Form, participants were required to review an explanatory statement and a consent statement. The online survey became accessible upon the participant's selection of "agree to participate". A total of 570 nurses engaged in and completed the survey for the present study. Data analysis Data were analyzed using the Statistical Package for the Social Sciences (IBM SPSS) version 26. A descriptive analysis was used for demographic data, and a Principal Component Analysis (PCA) was performed to identify how much each of the bottleneck variables impacts the nurses’ workflow. Result Participants' characteristics Table 1 shows that of the 570 participants in the survey, the age of 82% (n = 465) of the respondents ranged from 20 to 40 years and that 83% (n = 473) had 15 years or less of work experience. Such data on age and work experience indicate that the participants were young. Furthermore, the majority of the participants were women (n = 467) and 78% of the participants had a bachelor’s degree. Table 1 Participants' characteristics (N = 570). Demographic Category n (%) Age 20–30 years 199 (34.9) 31–40 years 266 (46.7) 41–50 years 86 (15.1) Over 50 years 19 (3.3) Gender Men 103(3.3) Women 467 (18.1) Professional qualification Diploma 89 (15.6) Bachelor 446 (78.2) Master 30 (5.3) PhD 5 (0.9) Years of work experience 0–5 years 193 (33.9) 6–10 years 161 (28.3) 11–15 years 119 (20.9) 16–20 years 66 (11.6) Over 21 years 31 (5.4) Factor analysis The adequacy of the sample size was checked using Kaiser-Meyer-Olkin (KMO). Table 2 shows that the KMO value is 0.962 which is larger than 0.5, indicating that the Bartlett test has a high sampling adequacy to perform a factor analysis. Therefore, each of the 31 variables can be loaded heavily on only one of the principal components, while the absolute value of the loadings exceeds 0.50. Principal component analysis (PCA) was used to identify patterns in the correlations between the 31 bottleneck variables. These patterns are used to determine the presence of underlying factors, called components. Table 2 Kaiser-Meyer-Olkin and Bartlett test. KMO measure of sampling adequacy Bartlett's Test of Sphericity .962 Approx. Chi-Square Df p 12228.574 465 < .001 Note. KMO = Kaiser-Meyer-Olkin In the current study, factor aggregation was based on varimax rotation and this is indicated in Table 3 . Using the varimax rotation has simplified the interpretation. Indeed, with varimax each variable is associated with one of the factors and each factor represents only a small number of variables. The three components are interpretable as follows: (1) Nurse staffing— This pertains to the availability of sufficient nursing staff at all times across the continuum of care (Aiken & Fagin, 2018); (2) Working environment and quality of care —This refers to the availability of necessary skills and resources for nurses to perform their duties effectively (Johansen et al.,2021) and; (3) Medical devices— This factor concerns the availability of fully functional medical devices required for providing care (Aronson et al., 2020). Table 3 Initial matrix and rotated matrix of nurses’ workflow bottleneck factors Components Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings n (%) n (%) 1. Nurse staffing 14.6 (47.0) 6.3 (20.5) 2. Working environment and quality of care 2.2(7.2) 6.2 (20.1) 3. Medical devices 1.5 (4.8) 5.7 (18.4) Cumulative % 58.9% 58.9% Among the 31 bottleneck variables, 29 were retained in three components (Table 4 ). For example, the variable that measures the frequency of encountering an unbalanced nurse-patient ratio is relatively strongly correlated with the first component (0.83). Similarly, the variable that measures the frequency of encountering a lack of the skills and knowledge to provide high-quality care and malfunctioning medical equipment are relatively strongly correlated with the second (0.70) and the third (0.80) component, respectively. Table 4 Bottleneck variables impacting nurses’ workflow results after rotated factor matrix (load). Bottleneck variables Component 1 2 3 Nurse staffing 1. Unplanned nurse-to-patient ratio .830 2. Nursing shortage .829 3. Professional burnout .731 4. Nurse absenteeism .702 5. Lack of actions to control nurses’ absenteeism .672 6. Insufficient rest breaks .653 7. Unbalanced nurses’ distribution across the hospital wards .644 8. Lack of workplace support .556 9. Inappropriate shift scheduling .504 Working environment & quality of care 10. Lack of clinical skills and knowledge to provide quality of care .708 11. Lack of research skills to apply evidence-based practice .676 12. High-acuity patients .654 13. Lack of training to update own skills and knowledge .636 14. Dealing with difficult family members .616 15. Lack of professional development activities .600 16. Lack of communication with leaders .590 17. Lack of recognition/appreciation .576 18. Lack of teamwork .558 19. Lack of transparency in leadership .530 20. Lack of out-hospital educational activities .514 21. Conflicts and disputes with healthcare team members .513 22. Schedule inflexibility .503 Medical devices 23. Malfunctioning .809 24. Lack of maintenance .772 25. Low quality .762 26. Shortages of supplies .735 27. Incompetency .732 28. Unavailability .666 29. Loss .609 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser normalization. The rotation converged in 6 iterations. Discussion In this research, a bottleneck variable analysis using the PCA tool [ 6 ] was used to identify key variables that affect nurses' workflow to prioritize improvement efforts. The analysis identified 29 bottleneck variables, each contributing to different levels of variance in nursing workflow. Understanding these key bottleneck variables and their corresponding factors can help in developing and prioritizing improvement initiatives. Bottleneck factor 1 (nursing staffing) is a principal factor that loaded 21% of the variables studied and contains nine variables. Of the nine variables, the 'unbalanced nurse-patient ratio', the 'nursing shortage', the 'professional burnout', and 'nurse absenteeism' received high loadings of 0.830, 0.829, 0.731, and 0.702, respectively. Meanwhile, ‘lack of actions to control absenteeism of nurses', 'insufficient rest breaks’, and ‘unbalanced nurses’ distribution across hospital wards’ received moderate loads of 0.672, 0.653, and 0.644. ‘Lack of support from your workplace colleagues and leaders’ received 0.556 and ‘inappropriate shift scheduling’ received 0.504. Many studies link nursing productivity to nurse staffing level [ 16 – 18 ]. Nurse staff levels are a determinant of care outcomes [ 19 ]. Just recently, nurse staffing level has become the focus of attention of healthcare setting managers and it is increasingly important that healthcare setting managers control costs without compromising the quality of the services [ 20 ]. Many techniques are available in the literature for planning nurse staffing levels; however, there is a lack of agreement on “what staffing levels are acceptable” and “how staffing levels should be planned” in different situations [ 20 ]. Achieving the goal of controlling costs without compromising quality outcomes requires replacing traditional techniques with reliable and integrated database-based staffing systems, systems that allow nursing managers to control who is doing what, where, and when. Studies that examine workforce management systems in healthcare settings remain limited [ 21 , 22 ]. Bottleneck factor 2 (Working environment and quality of care) is a major factor that loaded 20% of the variables studied and contains 13 variables. Of the 13 variables, ‘lack of skills and knowledge needed to provide high-quality care', 'lack of research skills to apply evidence-based practice’, ‘high-acuity patients’, ‘lack of training to update their skills and knowledge’, ‘dealing with difficult patients/family members', and ‘lack of professional development activities’ received high loadings of 0.708, 0.676, 0.654, 0.636, 0.616 and 0.600, respectively. Meanwhile, the loads received for 'lack of communication with leaders’, ‘lack of recognition/ appreciation’, ‘lack of teamwork’, ‘lack of transparency in leadership’, ‘lack of educational activities outside the hospital', 'conflicts and disputes with healthcare team', and 'schedule inflexibility' ranged from 0.590 to 0.503. It is well-acknowledged that equipping nurses with the required skills and having good work environments are key factors in improving nurses’ work outcomes [ 23 ]. Creating a work environment that acts as a foundation for quality of care requires hospital managers’ support [ 24 ]. Some studies raise the potential importance of implementing the six essential standards of the American Association of Critical Care Nurses (AACN) of healthy work environment standards [ 25 ]. The six essential standards of the AACN are skilled communication, true collaboration, effective decision-making, appropriate staffing, meaningful recognition, and authentic leadership. Integrating the six essential standards of the AACN was found to help produce effective and sustainable outcomes for nurse work [ 26 ]. Bottleneck factor 3 (Medical devices) is a principal factor that loaded 18% of the variables studied and contains seven variables. Of the seven variables, ‘malfunctioning of medical equipment', 'lack of maintenance', 'low-quality', 'shortages of supplies’, and ‘medical equipment incompetency’ have received high loadings ranging from 0.809 to 0.732. Meanwhile, 'unavailability’ and 'loss of medical equipment’ received loading of 0.666 and 0.609 respectively. Medical equipment and devices are an essential component of healthcare services. Indeed, medical devices are used by nurses daily and in almost every activity to prevent, diagnose, monitor, and treat diseases [ 27 ]. The shortage of medical devices was found to harm the ability of the healthcare system to provide quality healthcare [ 28 ]. Recently, there has been an ongoing increase in the quality control of medical equipment studies [ 29 ]. Having a 'standardized management system' for medical devices that is entirely based on a quality control system can significantly reduce the rate of maintenance and failure of equipment and establish a good basis for hospital development [ 30 ]. Study limitations Although the results of the present provide insight into the bottleneck variables that could impact nurses’ workflow and efficiency, the study results should be used with caution due to some limitations. First, study participants were from the same geographical locations which may interfere with the generalizability of the study results. Indeed, the geographical location may impact the perception of the variables, so larger geographical regions are highly recommended to provide additional insights. Second, although bottleneck variables were collected from the literature, however, a more systematic review of the literature is required to ensure a comprehensive set of bottleneck variables. Conclusion The results of the study contribute to the current literature by identifying key bottleneck factors that constrain nursing workflow efficiency, which have not previously been captured by researchers. Three bottleneck factors, nurse staffing, work environment for quality of care, and medical devices, were identified as key bottleneck issues and may be considered by the leaders and managers to prioritize and deliver affordable and effective improvement efforts. Improvement plans involve the implementation of (1) a healthcare workforce management system, (2) a healthy work environment standards and supportive leadership system, and (3) a standardized management system of medical equipment based on the quality control process to provide quality care. Further research is needed to explore the implementation of systems and standards in hospitals, as this would be valuable to policymakers who are seeking to improve health services. Abbreviations KSA Kingdom of Saudi Arabia AACN American Association of Critical Care Nurses CVI content validity index SMEs subject matter experts I-CVI Item content validity index S-CVI Scale- content validity index KMO Kaiser-Meyer-Olkin PCA Principal component analysis Declarations Author contributions Conceptualization and design, M.A and J.A; Data curation, A.A.G, E.S.A, NIA, B.A, and A.A; Investigation, A.A.G, E.S.A, B.A, and A.A; Methodology, MA and JA; M.A; Writing—original draft, MA; Writing—review and editing, M.A and J.A; All authors have read and agreed to the published version of the manuscript. Funding This research was funded by Taif University, Saudi Arabia, Project No. (TU-DSPP-2024-282). Data availability The author confirms that all data generated or analysed during this study are included in this manuscript. Ethics approval and consent to participate This study was reviewed and approved by the Scientific Research Ethics Committee in King Faisal Medical Complex (KFMC) (IRB number: 2023-B-40). A written informed consent was obtained from all the participants. Consent for publication Not applicable Competing interest The authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Acknowledgments The authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2024-282). The authors wish also to express their gratitude to the nurses who participated in this study. References Alsufyani AM, Alforihidi MA, Almalki KE, Aljuaid SM, Alamri AA, Alghamdi MS. Linking the Saudi Arabian 2030 vision with nursing transformation in Saudi Arabia: Roadmap for nursing policies and strategies. Int J Afr Nurs Sci. 2020;13:100256. https://doi.org/10.1016/j.ijans.2020.100256 . National Academies of Sciences, Engineering, and Medicine. The role of nurses in improving health care access and quality. The future of nursing 2020–2030: Charting a path to achieve health equity. 2020:99–126. https://www.ncbi.nlm.nih.gov/books/NBK573898/ . Al-Moteri M, Alzahrani AA, Althobiti ES, Plummer V, Sahrah AZ, Alkhaldi MJ, Rajab EF, Alsalmi AR, Abdullah ME, Abduelazeez AE, Caslangen MZ. The Road to Developing Standard Time for Efficient Nursing Care: A Time and Motion Analysis. InHealthcare 2023 Aug 6 (Vol. 11, No. 15, p. 2216). MDPI. https://doi.org/10.3390/healthcare11152216 . Chen Y, Xie W, Gunter CA, Liebovitz D, Mehrotra S, Zhang H, Malin B. Inferring clinical workflow efficiency via electronic medical record utilization. InAMIA annual symposium proceedings 2015 (Vol. 2015, p. 416). American Medical Informatics Association. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765602/ . Mihalj M, Corona A, Andereggen L, Urman RD, Luedi MM, Bello C. Managing bottlenecks in the perioperative setting: Optimizing patient care and reducing costs. Best Pract Res Clin Anaesthesiol. 2022;36(2):299–310. https://doi.org/10.1016/j.bpa.2022.05.005 . Ongbali SO, Afolalu SA, Oyedepo SA, Aworinde AK, Fajobi MA. A study on the factors causing bottleneck problems in the manufacturing industry using principal component analysis. Heliyon. 2021;7(5). https://doi.org/10.1016/j.heliyon.2021.e07020 . Wendsche J, Paridon H, Blasche G. Nurses’ rest breaks and organizational leaving intentions. Psychol Health Med. 2022;27(8):1782–92. https://doi.org/10.1080/13548506.2021.1950784 . Machon M, Knighten ML, Sohal J. Improving clinical communication and collaboration through technology:: a benefits analysis for nurse leaders. Nurse Lead. 2020;18(5):481–6. https://doi.org/10.1016/j.mnl.2020.06.003 . Rosa D, Terzoni S, Dellafiore F, Destrebecq A. Systematic review of shift work and nurses’ health. Occup Med. 2019;69(4):237–43. https://doi.org/10.1093/occmed/kqz063 . Yeung MS, Lapinsky SE, Granton JT, Doran DM, Cafazzo JA. Examining nursing vital signs documentation workflow: barriers and opportunities in general internal medicine units. J Clin Nurs. 2012;21(7–8):975–82. https://doi.org/10.1111/j.1365-2702.2011.03937.x . Bemthuis RH, van Slooten N, Arachchige JJ, Piest JP, Bukhsh FA. A Classification of Process Mining Bottleneck Analysis Techniques for Operational Support. InICE-B 2021 (pp. 127–135). https://doi.org/10.5220/0010578601270135 . Thompson B. Effect sizes, confidence intervals, and confidence intervals for effect sizes. Psychol Sch. 2007;44(5):423–32. https://doi.org/10.1002/pits.20234 . Shrotryia VK, Dhanda U. Content validity of assessment instrument for employee engagement. Sage Open. 2019;9(1):2158244018821751. https://doi.org/10.1177/2158244018821751 . Polit DF, Beck CT. The content validity index: are you sure you know what's being reported? Critique and recommendations. Res Nurs Health. 2006;29(5):489–97. https://doi.org/10.1002/nur.20147 . Lynn MR. Determination and quantification of content validity. Nurs Res. 1986;35(6):382–6. https://pubmed.ncbi.nlm.nih.gov/3640358/ . Hockenberry JM, Becker ER. How do hospital nurse staffing strategies affect patient satisfaction? ILR Rev. 2016;69(4):890–910. https://doi.org/10.1177/0019793916642760 . Shimp KM. Systematic review of turnover/retention and staff perception of staffing and resource adequacy related to staffing. Nurs Econ. 2017;35(5):239–A66. https://www.proquest.com/scholarly-journals/systematic-review-turnover-retention-staff/docview/1954857855/se-2?accountid=15290 . Dall TM, Chen YJ, Seifert RF, Maddox PJ, Hogan PF. The economic value of professional nursing. Med Care. 2009;47(1):97–104. https://doi.org/10.1097/MLR.0b013e3181844da8 . Trepanier S, Yoder-Wise PS, Church CD, Africa L. Nurse leaders' assumptions and attitudes toward residency programs for new graduate nurses. Nurs Adm Q. 2021;45(1):26–34. https://doi.org/10.1097/NAQ.0000000000000442 . Saville C, Dall'Ora C, Griffiths P. The association between 12-hour shifts and nurses-in-charge's perceptions of missed care and staffing adequacy: a retrospective cross-sectional observational study. Int J Nurs Stud. 2020;112:103721. https://doi.org/10.1016/j.ijnurstu.2020.103721 . Griffiths P, Saville C, Ball JE, Jones J, Monks T. Safer Nursing Care Tool study team. Beyond ratios-flexible and resilient nurse staffing options to deliver cost-effective hospital care and address staff shortages: A simulation and economic modelling study. Int J Nurs Stud. 2021;117:103901. https://doi.org/10.1016/j.ijnurstu.2021.103901 . Wynne R, Davidson PM, Duffield C, Jackson D, Ferguson C. Workforce management and patient outcomes in the intensive care unit during the COVID-19 pandemic and beyond: A discursive paper. J Clin Nurs 2021 Apr. https://doi.org/10.1111/jocn.15916 . Copanitsanou P, Fotos N, Brokalaki H. Effects of work environment on patient and nurse outcomes. Br J Nurs. 2017;26(3):172–6. https://doi.org/10.12968/bjon.2017.26.3.172 . Model AM. The Effect of Nurse Practice Environment on Retention and Quality of Care via Burnout, Work Characteristics, and Resilience. J Nurs Adm. 2020;50(10):546–53. https://doi.org/10.1097/NNA.0000000000000932 . Kelly L, Todd M. Compassion fatigue and the healthy work environment. AACN Adv Crit Care. 2017;28(4):351–8. https://doi.org/10.4037/aacnacc2017283 . Ulrich B, Cassidy L, Barden C, Varn-Davis N, Delgado SA. National nurse work environments-October 2021: a status report. Crit Care Nurse. 2022;42(5):58–70. https://doi.org/10.4037/ccn2022798 . Iadanza E, Cerofolini S, Lombardo C, Satta F, Gherardelli M. Medical devices nomenclature systems: a scoping review. Health and Technology. 2021;11:681 – 92. https://link.springer.com/article/10.1007/s12553-021-00567-1 . Moyimane MB, Matlala SF, Kekana MP. Experiences of nurses on the critical shortage of medical equipment at a rural district hospital in South Africa: a qualitative study. Pan Afr Med J. 2017;28(1):157. https://www.ajol.info/index.php/pamj/article/view/167360 . Li J, Mao Y, Zhang J. Maintenance and quality control of medical equipment based on information fusion technology. Comput Intell Neurosci. 2022;2022(1):9333328. https://doi.org/10.1155/2022/9333328 . Li J, Mao Y, Zhang J. Maintenance and quality control of medical equipment based on information fusion technology. Comput Intell Neurosci. 2022;2022(1):9333328. https://doi.org/10.1155/2022/9333328 . Additional Declarations No competing interests reported. Supplementary Files StudyTool.docx Cite Share Download PDF Status: Published Journal Publication published 11 Sep, 2024 Read the published version in BMC Nursing → Version 1 posted Editorial decision: Revision requested 12 Aug, 2024 Reviews received at journal 09 Aug, 2024 Reviewers agreed at journal 03 Aug, 2024 Reviews received at journal 02 Aug, 2024 Reviewers agreed at journal 23 Jul, 2024 Reviewers invited by journal 20 Jul, 2024 Editor invited by journal 12 Jul, 2024 Editor assigned by journal 12 Jul, 2024 Submission checks completed at journal 12 Jul, 2024 First submitted to journal 05 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4693941","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336127755,"identity":"fc399342-9011-4c17-9d33-6e13b2b214d0","order_by":0,"name":"Modi Al-Moteri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYPACCQYG9gYgbWBBpIYDIC08B0BaJIjWArIoAWodIaDbwJ34+EONRb7BzedXN/wokGDgb+9OwKvF7ADvZoMDxyQsN9zOKbvZA3SYxJmzGwhp2SZxgE3CwOB2TtoNHqAWA4lcYrT8A2q5eSbt5h+itRxsA2q5wX7sNnG2HAb65WyfhIHkmRy22zIGEjyE/XK8d+ODim91BnzHjz+7+eaPjRx/ey9+LQzMUFrhAI8BiObBrxwZyDewPyBe9SgYBaNgFIwoAAD7OkmSt52m/AAAAABJRU5ErkJggg==","orcid":"","institution":"Taif University","correspondingAuthor":true,"prefix":"","firstName":"Modi","middleName":"","lastName":"Al-Moteri","suffix":""},{"id":336127756,"identity":"9cd4d311-8d3c-4633-a2c3-be71223cfbc1","order_by":1,"name":"Jamil Aljuaid","email":"","orcid":"","institution":"Maternity and Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jamil","middleName":"","lastName":"Aljuaid","suffix":""},{"id":336127757,"identity":"84870a12-bf76-484d-8eb7-b809e593d0ee","order_by":2,"name":"Bander Alsufyani","email":"","orcid":"","institution":"Maternity and Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bander","middleName":"","lastName":"Alsufyani","suffix":""},{"id":336127758,"identity":"324fc2f3-2011-437d-9746-54d4c1e51c91","order_by":3,"name":"Amnah Alghamdi","email":"","orcid":"","institution":"King Faisal Medical Complex","correspondingAuthor":false,"prefix":"","firstName":"Amnah","middleName":"","lastName":"Alghamdi","suffix":""},{"id":336127759,"identity":"884627b6-854d-46dd-a297-1a7a1ada0a88","order_by":4,"name":"Ensherah Saeed Althobiti","email":"","orcid":"","institution":"King Abdulaziz Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ensherah","middleName":"Saeed","lastName":"Althobiti","suffix":""},{"id":336127760,"identity":"d9818527-b9e2-4b9d-8777-e1e99f70c4cb","order_by":5,"name":"Abdulslam Althagafi","email":"","orcid":"","institution":"Maternity and Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Abdulslam","middleName":"","lastName":"Althagafi","suffix":""}],"badges":[],"createdAt":"2024-07-05 19:09:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4693941/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4693941/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12912-024-02311-2","type":"published","date":"2024-09-11T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61891376,"identity":"7785f78b-ab72-444a-ab3f-8536c541944e","added_by":"auto","created_at":"2024-08-06 18:32:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":190968,"visible":true,"origin":"","legend":"\u003cp\u003eBottleneck in Nursing Workflow\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4693941/v1/a9035ffd102b3befb32cf700.png"},{"id":64618941,"identity":"92142fc2-3be7-464d-b1c1-4277cdf90ab0","added_by":"auto","created_at":"2024-09-16 16:08:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":712843,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4693941/v1/575a1228-742d-4a96-8f53-417645072777.pdf"},{"id":61891377,"identity":"27bbc391-960d-44b9-8a2a-8b9b9608a289","added_by":"auto","created_at":"2024-08-06 18:32:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21253,"visible":true,"origin":"","legend":"","description":"","filename":"StudyTool.docx","url":"https://assets-eu.researchsquare.com/files/rs-4693941/v1/7be05aa961aacf5957f415e4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bottleneck factors impacting nurses’ workflow and the opportunity to prioritize improvement efforts: Factor analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the Kingdom of Saudi Arabia (KSA), the nursing workforce is the largest population group in the healthcare system, with a total of 184,565 registered nurses currently working in Ministry of Health (MOH) institutions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The nursing workforce plays a crucial role in almost every aspect of health services and serves as a driver for care improvement [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Ensuring nursing workflows is essential to improve patient care outcomes, enabling healthcare institutions to operate effectively and support service improvement [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Workflow efficiency refers to the measurement of resources, especially time, spent by a nurse to complete a regularly recurring task during a shift [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the efficiency of the nursing workforce in healthcare settings is restricted by one or more variables, referred to as bottlenecks, indicating that despite the successful implementation of improvement efforts, challenges cannot be avoided [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNurse workflow\u003c/p\u003e \u003cp\u003eIn the context of healthcare services, a bottleneck refers to a factor that restricts or slows down the workflow of nurses, leading to inefficiency [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Prioritizing improvement activities to address bottlenecks can result in significant improvements in the overall work productivity of the healthcare institution [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, healthcare stakeholders need to conduct a thorough investigation to identify bottleneck factors that may affect nurses' work efficiency, particularly if the healthcare system aims to provide efficient healthcare services. Various techniques have been used to analyze the gap between nurse workflow and the actual activities performed by them in a healthcare setting.\u003c/p\u003e \u003cp\u003eThis study is the second phase of a multi-phase, multi-center quality improvement project in the Taif region of Saudi Arabia. The ultimate goal of the project is to enhance patient care outcomes from the perspective of nursing productivity. Al-Moteri et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] conducted a study to determine the time taken by nurses to complete various activities during their shifts. The research revealed that on average, nurses spent 408 minutes per shift on the following activities: 93 minutes on direct patient care, 58 minutes on indirect patient care, 68 minutes on documentation, 24 minutes on professional communication, 87 minutes on ward/room activities, 20 minutes on miscellaneous activities, and 58 minutes on personal needs. Additionally, the study found that nurses spent 69 minutes traveling between patient rooms, stations, and units.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to Al-Moteri and colleagues, it was found that there was an average delay of 73 minutes beyond the end of the shift to complete all necessary unaccomplished shift activities. Researchers concluded that the amount of work undertaken by nurses during a shift exceeds the capacity to process it within the shift duration (8-hour morning shift: 480 minutes), resulting in overextended resources and shift times (550 minutes) Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExtensive research has focused on identifying the factors influencing nurses' workflow and contributing to shift delays [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, there is a notable dearth of comprehensive investigations aimed at analyzing the critical variables that give rise to inefficiencies in nursing workflow. This absence of inquiry fails to underscore the influential variables that precipitate inefficiency. The primary objective of the current study is to investigate the bottleneck variables influencing the nursing workflow. Given the complexity of identifying bottlenecks, this necessitates the utilization of the bottleneck detection analysis technique, as outlined by Bemthuis et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This multi-center study aims to address the existing knowledge gap through the specific identification and analysis of bottlenecks within the nursing workflow. Through this approach, the study seeks to provide insights into the key variables contributing to nurses' extended shift times, with the ultimate goal of prioritizing efforts for improvement.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eDesign\u003c/p\u003e \u003cp\u003eThis descriptive multicenter cross-sectional study was conducted between October 2023 and November 2023 in the Taif region of Saudi Arabia. A bottleneck variable assessment method was used and included a combination of techniques to identify problems that occur and impact nursing workflow efficiency. The following includes a step-by-step procedure [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eStep 1 Obtain variables from the nursing literature\u003c/p\u003e\u003cp\u003eStep 2 Design a structured questionnaire using variables obtained from Step 1\u003c/p\u003e\u003cp\u003eStep 3 Determine sample size\u003c/p\u003e\u003cp\u003eStep 4 Distribute the questionnaire online\u003c/p\u003e\u003cp\u003eStep 5 Data analysis\u003c/p\u003e\u003cp\u003eStep 6 Conduct PCA\u003c/p\u003e\u003cp\u003eStep 7 Identify bottleneck variables to devise and prioritize improvement actions\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSetting and sampling\u003c/p\u003e \u003cp\u003e Every government hospital in the Taif region was invited to participate in the current study. Four large government hospitals agreed to participate, collectively having a total of 1,903 beds and serving around 709,000 people. Once the ethical approval was obtained, permission from the participating hospitals was requested. In this study, the representative sample size of the nursing population was determined using Thompson's [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] formula. Out of the 2460 nurses working in the four governmental hospitals, 334 nurses were considered appropriate for the study when population distributions were normal.\u003c/p\u003e \u003cp\u003eDrafting the questionnaire\u003c/p\u003e \u003cp\u003eStep 1: An initial review of the literature was undertaken to identify bottleneck variables impacting nursing workflow. Forty bottleneck variables affecting nursing workflow were identified and statements of the data collection tool were constructed accordingly.\u003c/p\u003e \u003cp\u003eStep 2: The content validity index (CVI) of the current study data collection tool was evaluated by five subject matter experts (SMEs) possessing a minimum of 10 years of work experience in nursing administration. According to Shrotryia and Dhanda [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] assessing CVI requires assessing the relevancy of each item (I-CVI) and the overall scale (S-CVI). In this regard, SMEs were instructed to independently rate their opinion regarding the relevancy of the items by answering the following question: to what extent is item (X) perceived as a challenge that may restrict the workflow of nurses, resulting in extended shift times? SMEs were instructed to rate the 40 initial items using a 4-point Likert scale in which: 1\u0026thinsp;=\u0026thinsp;not at all, 2\u0026thinsp;=\u0026thinsp;slightly, 3\u0026thinsp;=\u0026thinsp;moderately, and 4\u0026thinsp;=\u0026thinsp;highly. A four-point scale was selected to avoid a neutral opinion. The I-CVI was then assessed by calculating the number of SMEs who rated the item as moderate\u0026thinsp;=\u0026thinsp;3 or high\u0026thinsp;=\u0026thinsp;4 and divided by the total number of SMEs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The I-CVI for all the items on the scale ranged from 0.72\u0026ndash;1. According to Lynn [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], any item scored below 0.78 can be eliminated. Nine items scored below 0.78 and were omitted. Thirty-one items were then tested for the S-CVI and were 0.85 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], indicating high content validity for the overall instrument. Thirty-one items were included in the semi-final version.\u003c/p\u003e \u003cp\u003eStep 3: Before commencing the data collection process, a pilot study was carried out with 25 eligible participants to evaluate the clarity of the scale items and the reliability of the whole scale using a 5-point Likert scale, ranging from \"always\" to \"never\". These participants were informed to read the scale items loudly and briefly explain their understanding of each. Thus, the authors were able to determine if participants had difficulty understanding or if there were discrepancies in understanding and then modified the items accordingly. The pilot study was then conducted and the results showed a robust \u0026#120572; coefficient of 0.956 for the whole scale based on participants' responses, indicating that the items effectively measure the underlying construct of bottleneck factors among respondents.\u003c/p\u003e \u003cp\u003eStep 4: The final questionnaire (31 items) was supplemented with additional items to assess the demographic data such as age, gender, qualifications, and work experience of the participants (Please see supplemented file)\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe final version of the scale was distributed to all nurses in the participating hospitals via an online Google Form, facilitated by their respective administration offices. Participants in the pilot study were excluded. Before accessing the Google Form, participants were required to review an explanatory statement and a consent statement. The online survey became accessible upon the participant's selection of \"agree to participate\". A total of 570 nurses engaged in and completed the survey for the present study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using the Statistical Package for the Social Sciences (IBM SPSS) version 26. A descriptive analysis was used for demographic data, and a Principal Component Analysis (PCA) was performed to identify how much each of the bottleneck variables impacts the nurses\u0026rsquo; workflow.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003eParticipants' characteristics\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that of the 570 participants in the survey, the age of 82% (n\u0026thinsp;=\u0026thinsp;465) of the respondents ranged from 20 to 40 years and that 83% (n\u0026thinsp;=\u0026thinsp;473) had 15 years or less of work experience. Such data on age and work experience indicate that the participants were young. Furthermore, the majority of the participants were women (n\u0026thinsp;=\u0026thinsp;467) and 78% of the participants had a bachelor\u0026rsquo;s degree.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants' characteristics (N\u0026thinsp;=\u0026thinsp;570).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199 (34.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e266 (46.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86 (15.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver 50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103(3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e467 (18.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89 (15.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e446 (78.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (0.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of work experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193 (33.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161 (28.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119 (20.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (11.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver 21 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (5.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFactor analysis\u003c/p\u003e \u003cp\u003eThe adequacy of the sample size was checked using Kaiser-Meyer-Olkin (KMO). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the KMO value is 0.962 which is larger than 0.5, indicating that the Bartlett test has a high sampling adequacy to perform a factor analysis. Therefore, each of the 31 variables can be loaded heavily on only one of the principal components, while the absolute value of the loadings exceeds 0.50. Principal component analysis (PCA) was used to identify patterns in the correlations between the 31 bottleneck variables. These patterns are used to determine the presence of underlying factors, called components.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKaiser-Meyer-Olkin and Bartlett test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKMO measure of sampling adequacy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBartlett's Test of Sphericity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApprox. Chi-Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12228.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eNote.\u003c/em\u003e KMO\u0026thinsp;=\u0026thinsp;Kaiser-Meyer-Olkin\u003c/h2\u003e \u003cp\u003eIn the current study, factor aggregation was based on varimax rotation and this is indicated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Using the varimax rotation has simplified the interpretation. Indeed, with varimax each variable is associated with one of the factors and each factor represents only a small number of variables. The three components are interpretable as follows: (1) Nurse staffing\u0026mdash; This pertains to the availability of sufficient nursing staff at all times across the continuum of care (Aiken \u0026amp; Fagin, 2018); (2) Working environment and quality of care \u0026mdash;This refers to the availability of necessary skills and resources for nurses to perform their duties effectively (Johansen et al.,2021) and; (3) Medical devices\u0026mdash; This factor concerns the availability of fully functional medical devices required for providing care (Aronson et al., 2020).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInitial matrix and rotated matrix of nurses\u0026rsquo; workflow bottleneck factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eComponents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtraction Sums of Squared Loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRotation Sums of Squared Loadings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Nurse staffing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.6 (47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.3 (20.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Working environment and quality of care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2 (20.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Medical devices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.5 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.7 (18.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the 31 bottleneck variables, 29 were retained in three components (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For example, the variable that measures the frequency of encountering an unbalanced nurse-patient ratio is relatively strongly correlated with the first component (0.83). Similarly, the variable that measures the frequency of encountering a lack of the skills and knowledge to provide high-quality care and malfunctioning medical equipment are relatively strongly correlated with the second (0.70) and the third (0.80) component, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBottleneck variables impacting nurses\u0026rsquo; workflow results after rotated factor matrix (load).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBottleneck variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNurse staffing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Unplanned nurse-to-patient ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Nursing shortage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Professional burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Nurse absenteeism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Lack of actions to control nurses\u0026rsquo; absenteeism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Insufficient rest\u0026nbsp;breaks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Unbalanced nurses\u0026rsquo; distribution across the hospital wards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Lack of workplace support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. Inappropriate shift scheduling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking environment \u0026amp; quality of care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. Lack of clinical skills and knowledge to provide quality of care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. Lack of research skills to apply evidence-based practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12. High-acuity patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13. Lack of training to update own skills and knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14. Dealing with difficult family members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15. Lack of professional development activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16. Lack of communication with leaders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17. Lack of recognition/appreciation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18. Lack of teamwork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19. Lack of transparency in leadership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20. Lack of out-hospital educational activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21. Conflicts and disputes with healthcare team members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22. Schedule inflexibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical devices\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23. Malfunctioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24. Lack of maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25. Low quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26. Shortages of supplies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27. Incompetency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28. Unavailability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29. Loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eExtraction Method: Principal Component Analysis.\u003c/p\u003e \u003cp\u003eRotation Method: Varimax with Kaiser normalization.\u003c/p\u003e \u003cp\u003eThe rotation converged in 6 iterations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this research, a bottleneck variable analysis using the PCA tool [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] was used to identify key variables that affect nurses' workflow to prioritize improvement efforts. The analysis identified 29 bottleneck variables, each contributing to different levels of variance in nursing workflow. Understanding these key bottleneck variables and their corresponding factors can help in developing and prioritizing improvement initiatives.\u003c/p\u003e \u003cp\u003eBottleneck factor 1 (nursing staffing) is a principal factor that loaded 21% of the variables studied and contains nine variables. Of the nine variables, the 'unbalanced nurse-patient ratio', the 'nursing shortage', the 'professional burnout', and 'nurse absenteeism' received high loadings of 0.830, 0.829, 0.731, and 0.702, respectively. Meanwhile, \u0026lsquo;lack of actions to control absenteeism of nurses', 'insufficient rest breaks\u0026rsquo;, and \u0026lsquo;unbalanced nurses\u0026rsquo; distribution across hospital wards\u0026rsquo; received moderate loads of 0.672, 0.653, and 0.644. \u0026lsquo;Lack of support from your workplace colleagues and leaders\u0026rsquo; received 0.556 and \u0026lsquo;inappropriate shift scheduling\u0026rsquo; received 0.504.\u003c/p\u003e \u003cp\u003eMany studies link nursing productivity to nurse staffing level [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Nurse staff levels are a determinant of care outcomes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Just recently, nurse staffing level has become the focus of attention of healthcare setting managers and it is increasingly important that healthcare setting managers control costs without compromising the quality of the services [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Many techniques are available in the literature for planning nurse staffing levels; however, there is a lack of agreement on \u0026ldquo;what staffing levels are acceptable\u0026rdquo; and \u0026ldquo;how staffing levels should be planned\u0026rdquo; in different situations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Achieving the goal of controlling costs without compromising quality outcomes requires replacing traditional techniques with reliable and integrated database-based staffing systems, systems that allow nursing managers to control who is doing what, where, and when. Studies that examine workforce management systems in healthcare settings remain limited [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBottleneck factor 2 (Working environment and quality of care) is a major factor that loaded 20% of the variables studied and contains 13 variables. Of the 13 variables, \u0026lsquo;lack of skills and knowledge needed to provide high-quality care', 'lack of research skills to apply evidence-based practice\u0026rsquo;, \u0026lsquo;high-acuity patients\u0026rsquo;, \u0026lsquo;lack of training to update their skills and knowledge\u0026rsquo;, \u0026lsquo;dealing with difficult patients/family members', and \u0026lsquo;lack of professional development activities\u0026rsquo; received high loadings of 0.708, 0.676, 0.654, 0.636, 0.616 and 0.600, respectively. Meanwhile, the loads received for 'lack of communication with leaders\u0026rsquo;, \u0026lsquo;lack of recognition/ appreciation\u0026rsquo;, \u0026lsquo;lack of teamwork\u0026rsquo;, \u0026lsquo;lack of transparency in leadership\u0026rsquo;, \u0026lsquo;lack of educational activities outside the hospital', 'conflicts and disputes with healthcare team', and 'schedule inflexibility' ranged from 0.590 to 0.503.\u003c/p\u003e \u003cp\u003eIt is well-acknowledged that equipping nurses with the required skills and having good work environments are key factors in improving nurses\u0026rsquo; work outcomes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Creating a work environment that acts as a foundation for quality of care requires hospital managers\u0026rsquo; support [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Some studies raise the potential importance of implementing the six essential standards of the American Association of Critical Care Nurses (AACN) of healthy work environment standards [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The six essential standards of the AACN are skilled communication, true collaboration, effective decision-making, appropriate staffing, meaningful recognition, and authentic leadership. Integrating the six essential standards of the AACN was found to help produce effective and sustainable outcomes for nurse work [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBottleneck factor 3 (Medical devices) is a principal factor that loaded 18% of the variables studied and contains seven variables. Of the seven variables, \u0026lsquo;malfunctioning of medical equipment', 'lack of maintenance', 'low-quality', 'shortages of supplies\u0026rsquo;, and \u0026lsquo;medical equipment incompetency\u0026rsquo; have received high loadings ranging from 0.809 to 0.732. Meanwhile, 'unavailability\u0026rsquo; and 'loss of medical equipment\u0026rsquo; received loading of 0.666 and 0.609 respectively.\u003c/p\u003e \u003cp\u003eMedical equipment and devices are an essential component of healthcare services. Indeed, medical devices are used by nurses daily and in almost every activity to prevent, diagnose, monitor, and treat diseases [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The shortage of medical devices was found to harm the ability of the healthcare system to provide quality healthcare [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Recently, there has been an ongoing increase in the quality control of medical equipment studies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Having a 'standardized management system' for medical devices that is entirely based on a quality control system can significantly reduce the rate of maintenance and failure of equipment and establish a good basis for hospital development [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eStudy limitations\u003c/h3\u003e\n\u003cp\u003eAlthough the results of the present provide insight into the bottleneck variables that could impact nurses\u0026rsquo; workflow and efficiency, the study results should be used with caution due to some limitations. First, study participants were from the same geographical locations which may interfere with the generalizability of the study results. Indeed, the geographical location may impact the perception of the variables, so larger geographical regions are highly recommended to provide additional insights. Second, although bottleneck variables were collected from the literature, however, a more systematic review of the literature is required to ensure a comprehensive set of bottleneck variables.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of the study contribute to the current literature by identifying key bottleneck factors that constrain nursing workflow efficiency, which have not previously been captured by researchers. Three bottleneck factors, nurse staffing, work environment for quality of care, and medical devices, were identified as key bottleneck issues and may be considered by the leaders and managers to prioritize and deliver affordable and effective improvement efforts. Improvement plans involve the implementation of (1) a healthcare workforce management system, (2) a healthy work environment standards and supportive leadership system, and (3) a standardized management system of medical equipment based on the quality control process to provide quality care. Further research is needed to explore the implementation of systems and standards in hospitals, as this would be valuable to policymakers who are seeking to improve health services.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKingdom of Saudi Arabia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAACN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Association of Critical Care Nurses\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econtent validity index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMEs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esubject matter experts\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eI-CVI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eItem content validity index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eS-CVI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eScale- content validity index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKMO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKaiser-Meyer-Olkin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization and design, M.A and J.A; Data curation, A.A.G, E.S.A, NIA, B.A, and A.A; Investigation, A.A.G, E.S.A, B.A, and A.A; Methodology, MA and JA; M.A; Writing\u0026mdash;original draft, MA; Writing\u0026mdash;review and editing, M.A and J.A; All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Taif University, Saudi Arabia, Project No. (TU-DSPP-2024-282).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author confirms that all data generated or analysed during this study are included in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Scientific Research Ethics Committee in King Faisal Medical Complex (KFMC) (IRB number: 2023-B-40). A written informed consent was obtained from all the participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2024-282). The authors wish also to express their\u0026nbsp;gratitude to the nurses who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlsufyani AM, Alforihidi MA, Almalki KE, Aljuaid SM, Alamri AA, Alghamdi MS. Linking the Saudi Arabian 2030 vision with nursing transformation in Saudi Arabia: Roadmap for nursing policies and strategies. Int J Afr Nurs Sci. 2020;13:100256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijans.2020.100256\u003c/span\u003e\u003cspan address=\"10.1016/j.ijans.2020.100256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Academies of Sciences, Engineering, and Medicine. The role of nurses in improving health care access and quality. The future of nursing 2020\u0026ndash;2030: Charting a path to achieve health equity. 2020:99\u0026ndash;126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK573898/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/books/NBK573898/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Moteri M, Alzahrani AA, Althobiti ES, Plummer V, Sahrah AZ, Alkhaldi MJ, Rajab EF, Alsalmi AR, Abdullah ME, Abduelazeez AE, Caslangen MZ. The Road to Developing Standard Time for Efficient Nursing Care: A Time and Motion Analysis. InHealthcare 2023 Aug 6 (Vol. 11, No. 15, p. 2216). MDPI. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/healthcare11152216\u003c/span\u003e\u003cspan address=\"10.3390/healthcare11152216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Xie W, Gunter CA, Liebovitz D, Mehrotra S, Zhang H, Malin B. Inferring clinical workflow efficiency via electronic medical record utilization. InAMIA annual symposium proceedings 2015 (Vol. 2015, p. 416). American Medical Informatics Association. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765602/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765602/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMihalj M, Corona A, Andereggen L, Urman RD, Luedi MM, Bello C. Managing bottlenecks in the perioperative setting: Optimizing patient care and reducing costs. Best Pract Res Clin Anaesthesiol. 2022;36(2):299\u0026ndash;310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bpa.2022.05.005\u003c/span\u003e\u003cspan address=\"10.1016/j.bpa.2022.05.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOngbali SO, Afolalu SA, Oyedepo SA, Aworinde AK, Fajobi MA. A study on the factors causing bottleneck problems in the manufacturing industry using principal component analysis. Heliyon. 2021;7(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2021.e07020\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2021.e07020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWendsche J, Paridon H, Blasche G. Nurses\u0026rsquo; rest breaks and organizational leaving intentions. Psychol Health Med. 2022;27(8):1782\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13548506.2021.1950784\u003c/span\u003e\u003cspan address=\"10.1080/13548506.2021.1950784\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMachon M, Knighten ML, Sohal J. Improving clinical communication and collaboration through technology:: a benefits analysis for nurse leaders. Nurse Lead. 2020;18(5):481\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.mnl.2020.06.003\u003c/span\u003e\u003cspan address=\"10.1016/j.mnl.2020.06.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosa D, Terzoni S, Dellafiore F, Destrebecq A. Systematic review of shift work and nurses\u0026rsquo; health. Occup Med. 2019;69(4):237\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/occmed/kqz063\u003c/span\u003e\u003cspan address=\"10.1093/occmed/kqz063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeung MS, Lapinsky SE, Granton JT, Doran DM, Cafazzo JA. Examining nursing vital signs documentation workflow: barriers and opportunities in general internal medicine units. J Clin Nurs. 2012;21(7\u0026ndash;8):975\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2702.2011.03937.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2702.2011.03937.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBemthuis RH, van Slooten N, Arachchige JJ, Piest JP, Bukhsh FA. A Classification of Process Mining Bottleneck Analysis Techniques for Operational Support. InICE-B 2021 (pp. 127\u0026ndash;135). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5220/0010578601270135\u003c/span\u003e\u003cspan address=\"10.5220/0010578601270135\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson B. Effect sizes, confidence intervals, and confidence intervals for effect sizes. Psychol Sch. 2007;44(5):423\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pits.20234\u003c/span\u003e\u003cspan address=\"10.1002/pits.20234\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrotryia VK, Dhanda U. Content validity of assessment instrument for employee engagement. Sage Open. 2019;9(1):2158244018821751. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2158244018821751\u003c/span\u003e\u003cspan address=\"10.1177/2158244018821751\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolit DF, Beck CT. The content validity index: are you sure you know what's being reported? Critique and recommendations. Res Nurs Health. 2006;29(5):489\u0026ndash;97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/nur.20147\u003c/span\u003e\u003cspan address=\"10.1002/nur.20147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLynn MR. Determination and quantification of content validity. Nurs Res. 1986;35(6):382\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/3640358/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/3640358/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHockenberry JM, Becker ER. How do hospital nurse staffing strategies affect patient satisfaction? ILR Rev. 2016;69(4):890\u0026ndash;910. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0019793916642760\u003c/span\u003e\u003cspan address=\"10.1177/0019793916642760\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimp KM. Systematic review of turnover/retention and staff perception of staffing and resource adequacy related to staffing. Nurs Econ. 2017;35(5):239\u0026ndash;A66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proquest.com/scholarly-journals/systematic-review-turnover-retention-staff/docview/1954857855/se-2?accountid=15290\u003c/span\u003e\u003cspan address=\"https://www.proquest.com/scholarly-journals/systematic-review-turnover-retention-staff/docview/1954857855/se-2?accountid=15290\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDall TM, Chen YJ, Seifert RF, Maddox PJ, Hogan PF. The economic value of professional nursing. Med Care. 2009;47(1):97\u0026ndash;104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/MLR.0b013e3181844da8\u003c/span\u003e\u003cspan address=\"10.1097/MLR.0b013e3181844da8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrepanier S, Yoder-Wise PS, Church CD, Africa L. Nurse leaders' assumptions and attitudes toward residency programs for new graduate nurses. Nurs Adm Q. 2021;45(1):26\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/NAQ.0000000000000442\u003c/span\u003e\u003cspan address=\"10.1097/NAQ.0000000000000442\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaville C, Dall'Ora C, Griffiths P. The association between 12-hour shifts and nurses-in-charge's perceptions of missed care and staffing adequacy: a retrospective cross-sectional observational study. Int J Nurs Stud. 2020;112:103721. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijnurstu.2020.103721\u003c/span\u003e\u003cspan address=\"10.1016/j.ijnurstu.2020.103721\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffiths P, Saville C, Ball JE, Jones J, Monks T. Safer Nursing Care Tool study team. Beyond ratios-flexible and resilient nurse staffing options to deliver cost-effective hospital care and address staff shortages: A simulation and economic modelling study. Int J Nurs Stud. 2021;117:103901. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijnurstu.2021.103901\u003c/span\u003e\u003cspan address=\"10.1016/j.ijnurstu.2021.103901\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWynne R, Davidson PM, Duffield C, Jackson D, Ferguson C. Workforce management and patient outcomes in the intensive care unit during the COVID-19 pandemic and beyond: A discursive paper. J Clin Nurs 2021 Apr. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jocn.15916\u003c/span\u003e\u003cspan address=\"10.1111/jocn.15916\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCopanitsanou P, Fotos N, Brokalaki H. Effects of work environment on patient and nurse outcomes. Br J Nurs. 2017;26(3):172\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.12968/bjon.2017.26.3.172\u003c/span\u003e\u003cspan address=\"10.12968/bjon.2017.26.3.172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eModel AM. The Effect of Nurse Practice Environment on Retention and Quality of Care via Burnout, Work Characteristics, and Resilience. J Nurs Adm. 2020;50(10):546\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/NNA.0000000000000932\u003c/span\u003e\u003cspan address=\"10.1097/NNA.0000000000000932\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly L, Todd M. Compassion fatigue and the healthy work environment. AACN Adv Crit Care. 2017;28(4):351\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4037/aacnacc2017283\u003c/span\u003e\u003cspan address=\"10.4037/aacnacc2017283\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlrich B, Cassidy L, Barden C, Varn-Davis N, Delgado SA. National nurse work environments-October 2021: a status report. Crit Care Nurse. 2022;42(5):58\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4037/ccn2022798\u003c/span\u003e\u003cspan address=\"10.4037/ccn2022798\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIadanza E, Cerofolini S, Lombardo C, Satta F, Gherardelli M. Medical devices nomenclature systems: a scoping review. Health and Technology. 2021;11:681\u0026thinsp;\u0026ndash;\u0026thinsp;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/article/10.1007/s12553-021-00567-1\u003c/span\u003e\u003cspan address=\"https://link.springer.com/article/10.1007/s12553-021-00567-1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoyimane MB, Matlala SF, Kekana MP. Experiences of nurses on the critical shortage of medical equipment at a rural district hospital in South Africa: a qualitative study. Pan Afr Med J. 2017;28(1):157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ajol.info/index.php/pamj/article/view/167360\u003c/span\u003e\u003cspan address=\"https://www.ajol.info/index.php/pamj/article/view/167360\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Mao Y, Zhang J. Maintenance and quality control of medical equipment based on information fusion technology. Comput Intell Neurosci. 2022;2022(1):9333328. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2022/9333328\u003c/span\u003e\u003cspan address=\"10.1155/2022/9333328\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Mao Y, Zhang J. Maintenance and quality control of medical equipment based on information fusion technology. Comput Intell Neurosci. 2022;2022(1):9333328. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2022/9333328\u003c/span\u003e\u003cspan address=\"10.1155/2022/9333328\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"work-flow, nursing, productivity, staffing, equipment, device, medical","lastPublishedDoi":"10.21203/rs.3.rs-4693941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4693941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eMinimizing delays in delivering nursing care is paramount for enhancing the overall quality of care. Certain bottleneck variables restrict the workflow of nurses, resulting in extended shift times. This study is designed to pinpoint and analyze the principal factors contributing to bottleneck issues in nursing workflow, to direct improvement endeavors. This study seeks to provide insights into the key variables contributing to nurses' extended shift times, with the ultimate goal of prioritizing efforts for improvement.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA descriptive multicenter cross-sectional study was conducted. A scale was developed for this study by the authors after conducting a literature review, subsequently validated, and its reliability was assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 31 bottleneck variables, 29 were retained under three bottleneck factors: (1) Nurse staffing\u0026mdash; This pertains to the availability of sufficient nursing staff at all times across the continuum of care; (2) Working environment and quality of care\u0026mdash;This refers to the availability of necessary skills and resources for nurses to perform their duties effectively and; (3) Medical devices\u0026mdash; This factor concerns the availability of fully functional medical devices required for providing care.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eEfforts aimed at enhancing the overall healthcare system should concentrate on addressing bottleneck factors. This may involve the implementation of a healthcare workforce management system, the establishment of standards for a conducive and supportive working environment, and the utilization of a standardized system for the management of medical equipment. The outcomes of this study can be utilized by nurses and policymakers to devise comprehensive strategies for improvement.\u003c/p\u003e","manuscriptTitle":"Bottleneck factors impacting nurses’ workflow and the opportunity to prioritize improvement efforts: Factor analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-06 18:31:56","doi":"10.21203/rs.3.rs-4693941/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-12T06:57:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-09T14:46:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291627242642598279848285322817107402995","date":"2024-08-03T18:26:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-02T14:05:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181227459395171712115206233529523403633","date":"2024-07-23T08:25:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-20T21:16:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-12T07:12:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-12T07:11:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-12T05:57:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2024-07-05T19:08:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"71592618-7350-4107-b8bf-6d8a3482cbff","owner":[],"postedDate":"August 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T15:59:14+00:00","versionOfRecord":{"articleIdentity":"rs-4693941","link":"https://doi.org/10.1186/s12912-024-02311-2","journal":{"identity":"bmc-nursing","isVorOnly":false,"title":"BMC Nursing"},"publishedOn":"2024-09-11 15:57:00","publishedOnDateReadable":"September 11th, 2024"},"versionCreatedAt":"2024-08-06 18:31:56","video":"","vorDoi":"10.1186/s12912-024-02311-2","vorDoiUrl":"https://doi.org/10.1186/s12912-024-02311-2","workflowStages":[]},"version":"v1","identity":"rs-4693941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4693941","identity":"rs-4693941","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}