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

doi:10.21203/rs.3.rs-4693941/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-4693941/v1

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Purpose

Minimizing 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.

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

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.

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.

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
Components	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
Bottleneck variables	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.

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.

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.

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

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.

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No competing interests reported.

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

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Note. KMO = Kaiser-Meyer-Olkin

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